From bc374e9d06afc648c4be3c218ec64aae2611819f Mon Sep 17 00:00:00 2001 From: markjrieke Date: Fri, 18 Oct 2024 09:07:01 -0500 Subject: [PATCH] deploy 10/18 --- .../Alabama/execute-results/html.json | 2 +- .../Alaska/execute-results/html.json | 2 +- .../Arizona/execute-results/html.json | 2 +- .../Arkansas/execute-results/html.json | 2 +- .../California/execute-results/html.json | 2 +- .../Colorado/execute-results/html.json | 2 +- .../Connecticut/execute-results/html.json | 2 +- .../Delaware/execute-results/html.json | 2 +- .../execute-results/html.json | 2 +- .../Florida/execute-results/html.json | 2 +- .../Georgia/execute-results/html.json | 2 +- .../Hawaii/execute-results/html.json | 2 +- .../Idaho/execute-results/html.json | 2 +- .../Illinois/execute-results/html.json | 2 +- .../Indiana/execute-results/html.json | 2 +- .../2024-potus/Iowa/execute-results/html.json | 2 +- .../Kansas/execute-results/html.json | 2 +- .../Kentucky/execute-results/html.json | 2 +- .../Louisiana/execute-results/html.json | 2 +- .../Maine CD-1/execute-results/html.json | 2 +- .../Maine CD-2/execute-results/html.json | 2 +- .../Maine/execute-results/html.json | 2 +- .../Maryland/execute-results/html.json | 2 +- .../Massachusetts/execute-results/html.json | 2 +- .../Michigan/execute-results/html.json | 2 +- .../Minnesota/execute-results/html.json | 2 +- .../Mississippi/execute-results/html.json | 2 +- .../Missouri/execute-results/html.json | 2 +- .../Montana/execute-results/html.json | 2 +- .../National/execute-results/html.json | 2 +- .../figure-html/plot-conditionals-1.png | Bin 178719 -> 196798 bytes .../Nebraska CD-1/execute-results/html.json | 2 +- .../Nebraska CD-2/execute-results/html.json | 2 +- .../Nebraska CD-3/execute-results/html.json | 2 +- .../Nebraska/execute-results/html.json | 2 +- .../Nevada/execute-results/html.json | 2 +- .../New Hampshire/execute-results/html.json | 2 +- .../New Jersey/execute-results/html.json | 2 +- .../New Mexico/execute-results/html.json | 2 +- .../New York/execute-results/html.json | 2 +- .../North Carolina/execute-results/html.json | 2 +- .../North Dakota/execute-results/html.json | 2 +- .../2024-potus/Ohio/execute-results/html.json | 2 +- .../Oklahoma/execute-results/html.json | 2 +- .../Oregon/execute-results/html.json | 2 +- .../Pennsylvania/execute-results/html.json | 2 +- .../Rhode Island/execute-results/html.json | 2 +- .../South Carolina/execute-results/html.json | 2 +- .../South Dakota/execute-results/html.json | 2 +- .../Tennessee/execute-results/html.json | 2 +- .../Texas/execute-results/html.json | 2 +- .../2024-potus/Utah/execute-results/html.json | 2 +- .../Vermont/execute-results/html.json | 2 +- .../Virginia/execute-results/html.json | 2 +- .../Washington/execute-results/html.json | 2 +- .../West Virginia/execute-results/html.json | 2 +- .../Wisconsin/execute-results/html.json | 2 +- .../Wyoming/execute-results/html.json | 2 +- 58 files changed, 57 insertions(+), 57 deletions(-) diff --git a/_freeze/2024-potus/Alabama/execute-results/html.json b/_freeze/2024-potus/Alabama/execute-results/html.json index 259411bf..e8c5ebf7 100644 --- a/_freeze/2024-potus/Alabama/execute-results/html.json +++ b/_freeze/2024-potus/Alabama/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 99% chance of beating Kamala Harris** in Alabama.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Alabama. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Alabama.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 99% chance of beating Kamala Harris** in Alabama.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Alabama. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Alabama.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Alaska/execute-results/html.json b/_freeze/2024-potus/Alaska/execute-results/html.json index 78a74164..08ea9f61 100644 --- a/_freeze/2024-potus/Alaska/execute-results/html.json +++ b/_freeze/2024-potus/Alaska/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 95% chance of beating Kamala Harris** in Alaska.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Alaska.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 96% chance of beating Kamala Harris** in Alaska.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Alaska.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Arizona/execute-results/html.json b/_freeze/2024-potus/Arizona/execute-results/html.json index e5e5efd1..a00a90e6 100644 --- a/_freeze/2024-potus/Arizona/execute-results/html.json +++ b/_freeze/2024-potus/Arizona/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 66% chance of beating Kamala Harris** in Arizona.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Arizona.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 68% chance of beating Kamala Harris** in Arizona.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Arizona.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Arkansas/execute-results/html.json b/_freeze/2024-potus/Arkansas/execute-results/html.json index d8bafe6a..fbf45b12 100644 --- a/_freeze/2024-potus/Arkansas/execute-results/html.json +++ b/_freeze/2024-potus/Arkansas/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Arkansas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Arkansas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Arkansas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Arkansas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/California/execute-results/html.json b/_freeze/2024-potus/California/execute-results/html.json index 694c49e1..25725243 100644 --- a/_freeze/2024-potus/California/execute-results/html.json +++ b/_freeze/2024-potus/California/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in California.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/California.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in California.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/California.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Colorado/execute-results/html.json b/_freeze/2024-potus/Colorado/execute-results/html.json index 314d9572..20d0a03f 100644 --- a/_freeze/2024-potus/Colorado/execute-results/html.json +++ b/_freeze/2024-potus/Colorado/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 96% chance of beating Donald Trump** in Colorado.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Colorado.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 95% chance of beating Donald Trump** in Colorado.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Colorado.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Connecticut/execute-results/html.json b/_freeze/2024-potus/Connecticut/execute-results/html.json index fa3166b4..4d8c6022 100644 --- a/_freeze/2024-potus/Connecticut/execute-results/html.json +++ b/_freeze/2024-potus/Connecticut/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Connecticut.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Connecticut.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Connecticut.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Connecticut.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Delaware/execute-results/html.json b/_freeze/2024-potus/Delaware/execute-results/html.json index 081c2d76..a9f6ce9c 100644 --- a/_freeze/2024-potus/Delaware/execute-results/html.json +++ b/_freeze/2024-potus/Delaware/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Delaware.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Delaware.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Delaware.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Delaware.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/District of Columbia/execute-results/html.json b/_freeze/2024-potus/District of Columbia/execute-results/html.json index d01b72b2..80176dbb 100644 --- a/_freeze/2024-potus/District of Columbia/execute-results/html.json +++ b/_freeze/2024-potus/District of Columbia/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in the District of Columbia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in the District of Columbia. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/District of Columbia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in the District of Columbia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in the District of Columbia. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/District of Columbia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Florida/execute-results/html.json b/_freeze/2024-potus/Florida/execute-results/html.json index 20e465e6..28ea5bbd 100644 --- a/_freeze/2024-potus/Florida/execute-results/html.json +++ b/_freeze/2024-potus/Florida/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 87% chance of beating Kamala Harris** in Florida.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Florida.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 88% chance of beating Kamala Harris** in Florida.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Florida.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Georgia/execute-results/html.json b/_freeze/2024-potus/Georgia/execute-results/html.json index c1c02922..30109c42 100644 --- a/_freeze/2024-potus/Georgia/execute-results/html.json +++ b/_freeze/2024-potus/Georgia/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 66% chance of beating Kamala Harris** in Georgia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Georgia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 68% chance of beating Kamala Harris** in Georgia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Georgia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Hawaii/execute-results/html.json b/_freeze/2024-potus/Hawaii/execute-results/html.json index 85411f81..ce997a19 100644 --- a/_freeze/2024-potus/Hawaii/execute-results/html.json +++ b/_freeze/2024-potus/Hawaii/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Hawaii.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Hawaii. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Hawaii.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Hawaii.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Hawaii. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Hawaii.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Idaho/execute-results/html.json b/_freeze/2024-potus/Idaho/execute-results/html.json index fe7f3dc0..7de452df 100644 --- a/_freeze/2024-potus/Idaho/execute-results/html.json +++ b/_freeze/2024-potus/Idaho/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Idaho.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Idaho. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Idaho.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Idaho.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Idaho. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Idaho.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Illinois/execute-results/html.json b/_freeze/2024-potus/Illinois/execute-results/html.json index 37e63422..8f528c11 100644 --- a/_freeze/2024-potus/Illinois/execute-results/html.json +++ b/_freeze/2024-potus/Illinois/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 99% chance of beating Donald Trump** in Illinois.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Illinois. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Illinois.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 98% chance of beating Donald Trump** in Illinois.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Illinois. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Illinois.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Indiana/execute-results/html.json b/_freeze/2024-potus/Indiana/execute-results/html.json index 389853e9..977621f7 100644 --- a/_freeze/2024-potus/Indiana/execute-results/html.json +++ b/_freeze/2024-potus/Indiana/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Indiana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Indiana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Indiana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Indiana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Iowa/execute-results/html.json b/_freeze/2024-potus/Iowa/execute-results/html.json index e8a67cae..511c9fcb 100644 --- a/_freeze/2024-potus/Iowa/execute-results/html.json +++ b/_freeze/2024-potus/Iowa/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 89% chance of beating Kamala Harris** in Iowa.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Iowa.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 90% chance of beating Kamala Harris** in Iowa.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Iowa.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Kansas/execute-results/html.json b/_freeze/2024-potus/Kansas/execute-results/html.json index 6006f1d5..7f21a6c2 100644 --- a/_freeze/2024-potus/Kansas/execute-results/html.json +++ b/_freeze/2024-potus/Kansas/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 95% chance of beating Kamala Harris** in Kansas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Kansas. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Kansas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 96% chance of beating Kamala Harris** in Kansas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Kansas. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Kansas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Kentucky/execute-results/html.json b/_freeze/2024-potus/Kentucky/execute-results/html.json index abc5b8a0..aff3ece3 100644 --- a/_freeze/2024-potus/Kentucky/execute-results/html.json +++ b/_freeze/2024-potus/Kentucky/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 99% chance of beating Kamala Harris** in Kentucky.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Kentucky. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Kentucky.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 99% chance of beating Kamala Harris** in Kentucky.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Kentucky. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Kentucky.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Louisiana/execute-results/html.json b/_freeze/2024-potus/Louisiana/execute-results/html.json index 7b579241..a02d3a8d 100644 --- a/_freeze/2024-potus/Louisiana/execute-results/html.json +++ b/_freeze/2024-potus/Louisiana/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 96% chance of beating Kamala Harris** in Louisiana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Louisiana. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Louisiana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 97% chance of beating Kamala Harris** in Louisiana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Louisiana. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Louisiana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Maine CD-1/execute-results/html.json b/_freeze/2024-potus/Maine CD-1/execute-results/html.json index 95ac015e..c381e700 100644 --- a/_freeze/2024-potus/Maine CD-1/execute-results/html.json +++ b/_freeze/2024-potus/Maine CD-1/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Maine CD-1.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine CD-1.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Maine CD-1.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine CD-1.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Maine CD-2/execute-results/html.json b/_freeze/2024-potus/Maine CD-2/execute-results/html.json index e0002bb8..4ad39013 100644 --- a/_freeze/2024-potus/Maine CD-2/execute-results/html.json +++ b/_freeze/2024-potus/Maine CD-2/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 77% chance of beating Kamala Harris** in Maine CD-2.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine CD-2.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 79% chance of beating Kamala Harris** in Maine CD-2.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine CD-2.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Maine/execute-results/html.json b/_freeze/2024-potus/Maine/execute-results/html.json index c5c64a35..734a5db9 100644 --- a/_freeze/2024-potus/Maine/execute-results/html.json +++ b/_freeze/2024-potus/Maine/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 96% chance of beating Donald Trump** in Maine.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 95% chance of beating Donald Trump** in Maine.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maine.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Maryland/execute-results/html.json b/_freeze/2024-potus/Maryland/execute-results/html.json index 2315c789..6e0309be 100644 --- a/_freeze/2024-potus/Maryland/execute-results/html.json +++ b/_freeze/2024-potus/Maryland/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Maryland.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maryland.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Maryland.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Maryland.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Massachusetts/execute-results/html.json b/_freeze/2024-potus/Massachusetts/execute-results/html.json index 74b8104c..dae000f9 100644 --- a/_freeze/2024-potus/Massachusetts/execute-results/html.json +++ b/_freeze/2024-potus/Massachusetts/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Massachusetts.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Massachusetts.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Massachusetts.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Massachusetts.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Michigan/execute-results/html.json b/_freeze/2024-potus/Michigan/execute-results/html.json index 7adeb5f6..c1f125b5 100644 --- a/_freeze/2024-potus/Michigan/execute-results/html.json +++ b/_freeze/2024-potus/Michigan/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 55% chance of beating Donald Trump** in Michigan.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Michigan.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 52% chance of beating Donald Trump** in Michigan.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Michigan.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Minnesota/execute-results/html.json b/_freeze/2024-potus/Minnesota/execute-results/html.json index 1799199f..637ccb95 100644 --- a/_freeze/2024-potus/Minnesota/execute-results/html.json +++ b/_freeze/2024-potus/Minnesota/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 85% chance of beating Donald Trump** in Minnesota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Minnesota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 83% chance of beating Donald Trump** in Minnesota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Minnesota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Mississippi/execute-results/html.json b/_freeze/2024-potus/Mississippi/execute-results/html.json index 47acba8e..6cda3dd9 100644 --- a/_freeze/2024-potus/Mississippi/execute-results/html.json +++ b/_freeze/2024-potus/Mississippi/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 94% chance of beating Kamala Harris** in Mississippi.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Mississippi. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Mississippi.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 94% chance of beating Kamala Harris** in Mississippi.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Mississippi. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Mississippi.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Missouri/execute-results/html.json b/_freeze/2024-potus/Missouri/execute-results/html.json index 1b361c07..5c03b4db 100644 --- a/_freeze/2024-potus/Missouri/execute-results/html.json +++ b/_freeze/2024-potus/Missouri/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Missouri.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Missouri.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Missouri.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Missouri.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Montana/execute-results/html.json b/_freeze/2024-potus/Montana/execute-results/html.json index 3e62dfee..59b1cd8a 100644 --- a/_freeze/2024-potus/Montana/execute-results/html.json +++ b/_freeze/2024-potus/Montana/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Montana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Montana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Montana.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Montana.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/National/execute-results/html.json b/_freeze/2024-potus/National/execute-results/html.json index 8478332b..4af535a4 100644 --- a/_freeze/2024-potus/National/execute-results/html.json +++ b/_freeze/2024-potus/National/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "30f18cb853999354865df51e58333e90", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=80%}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 52% chance of beating Kamala Harris** in the electoral college.\n\n\n:::\n::: {.column width=20%}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"12%\"}\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/dev/img/harris.png){width=120}\n:::\n::: {.column width=\"38%\"}\n\n\n\n


**Kamala Harris** currently has a **48%** chance of being elected America's next president.\nShe's projected to win between **173** and **414** electoral college votes.

\n\n\n\n:::\n::: {.column width=\"12%\"}\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/dev/img/trump.png){width=120}\n:::\n::: {.column width=\"38%\"}\n\n\n\n


**Donald Trump** currently has a **52%** chance of re-taking the white house.\nHe's projected to win between **124** and **365** electoral college votes.

\n\n\n\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Projected electoral college votes\nThe model is updated daily, blending state and national polls with non-polling predictors, like economic growth and presidential approval, to generate a range of potential outcomes in the electoral college.\nAs we get closer to election day, the uncertainty around the estimate will decrease.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\nThere is a less than 1% chance of a tie in the electoral college.\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Chance of winning each state\nState-level results determine the makeup of the electoral college.\nMost states heavily favor a particular party, leaving a few competitive battlegrounds that will be decisive in determining the next president.\nHover/click to see more information about a particular state.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Conditional outcomes\nFrom the thousands of simulations, the model can see how the electoral college outcome changes when each candidate wins in a specific state.\nIf Harris wins in a red-leaning state, for example, it's likelier that she also wins in competitive states.\n\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](National_files/figure-html/plot-conditionals-1.png){width=1152}\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::: {.column width=\"70%\"}\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=80%}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 55% chance of beating Kamala Harris** in the electoral college.\n\n\n:::\n::: {.column width=20%}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"12%\"}\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/dev/img/harris.png){width=120}\n:::\n::: {.column width=\"38%\"}\n\n\n\n


**Kamala Harris** currently has a **45%** chance of being elected America's next president.\nShe's projected to win between **171** and **405** electoral college votes.

\n\n\n\n:::\n::: {.column width=\"12%\"}\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/dev/img/trump.png){width=120}\n:::\n::: {.column width=\"38%\"}\n\n\n\n


**Donald Trump** currently has a **55%** chance of re-taking the white house.\nHe's projected to win between **133** and **367** electoral college votes.

\n\n\n\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Projected electoral college votes\nThe model is updated daily, blending state and national polls with non-polling predictors, like economic growth and presidential approval, to generate a range of potential outcomes in the electoral college.\nAs we get closer to election day, the uncertainty around the estimate will decrease.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\nThere is a less than 1% chance of a tie in the electoral college.\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Chance of winning each state\nState-level results determine the makeup of the electoral college.\nMost states heavily favor a particular party, leaving a few competitive battlegrounds that will be decisive in determining the next president.\nHover/click to see more information about a particular state.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Conditional outcomes\nFrom the thousands of simulations, the model can see how the electoral college outcome changes when each candidate wins in a specific state.\nIf Harris wins in a red-leaning state, for example, it's likelier that she also wins in competitive states.\n\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](National_files/figure-html/plot-conditionals-1.png){width=1152}\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::: {.column width=\"70%\"}\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n", "supporting": [ "National_files" ], diff --git a/_freeze/2024-potus/National/figure-html/plot-conditionals-1.png b/_freeze/2024-potus/National/figure-html/plot-conditionals-1.png index 499f1a0546cf984535ce481d4daa494ea924bde6..faf705dd4b54a23fde368cd27a7616a06bb94e34 100644 GIT binary patch delta 147276 zcmd43^;?u(*fk6Y2uOGf$c>~TT}pSTh_rNrNOyze1sJGEcY~Bj2@EqZ!w@2!GW3vA z!q8pAd>48@@Atfa!T0?#2M5P+?S1Wi?scxU&U2G{K-Lsa{>|ADaGpQO=W|^{9$OF= zu_+b47HPG3j!%T3k^Z=hrVnzWLdO8A~C1}G^ajac``y&f(I5?A3qQ8xTC)1o1gyPH7SsxqZyn%EIt*tF)4 zWnP2^x95&QMLjkPsAg-&+dNq-V%tYW6C+Ap4vi3-d07%6n!jq>C!c0cCbk?y^^+_{{&x$Sz*mxm_B)WjfFX4iOGo_hLVyf|d9j5G=E@vvIFqhs`A zuW50aNdM=g+w4ZUf|l!N#izgv-mOcP->YYV`~LZ@=^3T!I4VcBGqZj1`4j1N;k z)->CtYHhRe7Esk$9*12dR`$dn}v$-bR!)RPdt?`4F7&7>-hWr zph_$zTX(6mvoq5hP%hOkEA|mJuXp-ZmJ@r>c_{pk1Ji*wZTaB0P2oG_x@h=TJyvWI z9+FvZfrX8fnTn^!yUJPoqkxA`cvFE@n-yv9EX{0a37|bAPR%(c#u0G&)}|vb5i*;J zT`8e7SBXkr(lP%<$Pw=X2gBf9RHfelA*M4O<7@g8N~eXdAT>z2{~*R0FAkypsCD47 z06i;o9`vC%UAHIQuRV)ht&4P@b+Mz$zUP#h)3*lcP@LzRSfZMJ{T$tyE${sl8jLHz zB3<)Ycz5V`Z@UYn~bk{Q_>3vHDTa}Ly*d;1*xlGQ}D<1>sRlK z7k)pykSo5>Q(JW8d<&5k0Wk7fetDtTum-$i*eDoC6MP#>#k@pwVV&zhJe9V=lgy(J zdAdfo=*!XeQBdi%iP>v_F%>ocxg;leTF$92t(w~p>yv!?St@Wu+G4zn*Q_qEF8COG zJO<}ADCdez;}eT%l|PEkz~6kgUdovp_njj%E=8~@jTH^s{`Stb`QXE4%D9;|aujIc z?(bMA{qnQixVEC(xYjOttRjKH*{84=GRAOh;xyj$=%{IWFA2gaOQYk7|&sy zIC~LWT(iF2yg7Q9BB>%zH8+svX50TwaNagAmUP{V{L(W^KX$E+O4f$ua47a4%nYRkjb%XKEYv(4?z821V*|MWKzGuTwOFE4k4 z_u6>HyN9GRROcw`Y`a`FmKO0h6GxSmT_zVV^roB9l<97bw_XRj+_33MQ+#)bd|K&j zq?nP8S1M6 zF>0ec`ZgE#x%Cv6o@PzzYs15JYC1z(@ZNNMrd3iO37Xu67a3z;w9T6sE?}1FN5s2MWw0}4~FnwdVL-vmoE{pSMksAIPH(I3{?}GGUvGKkFVT)`@s8&1?o21 z`;uySX*u6Zskv$R)$f~*$7`K#<6eC>cl`Mx$1ZqN&EUH|aomZJSKetr(zaK2k_gq~ zF?dxhBoIjQ$&gX>O6BEMVi5Z_YcJ-S?x-KJd%9Ot`E%sx^EvgF_oHu92W`8s?cUY9 z6)P%KtDj#$sZTJPXtkbWRfnM6W8L(fhh=&mh-{PG$WMYdErnE#sQAM7SqiV$z-M`ZdXSRKybLt9>Sgc+287QGKcz zq%GAFW1HcZ*aY8&MTsg(%-bx#!D1z0cE>g&kz{rH#F+7FYoN2yz`U04)I{g()3|uL zxJX-E3Y&6_(PX9BI8&wdf&f>iXwaZLAdSm*v}5e7@miFV^xONi$YtCV8jA|*PtmRN z(k}6AeI=ZyEZdFI$>r^#OW|SP=QPE-B{FqYZ^)$0pN!oZHcfD)*+<&%&gf*^4ArL<1H)Co!<;hs(8gU4ia~NG=Z_Hs--8L0>h!|VLX1lpT z4zl=$TW8I_;VNEkIfrnJ^(1apRdR7SWf@KhzVi>C>x>&Z{yTWx8RAtaryHXUkMY}57 zEiF}nd^dp9ATyK?w+#sqD8QQ0eDov8j25hdAW!_N-azRr$@+ZFW(ksx*x^65sM(+RRAv2^*_ZN09PkCb8oh^XD$S1s@2LjC*gxD!VVx( zG&04}%99bf8*DP|LXa3?biIP5?%5|yV3ed!IIK-$ zQMo=wNmqO(tif*EbMvyyvP3H3=iT|oukCJEshpWkH{sGYq`ihe)VdYj1MZS$mFm=l zq5NFjA<_@VYgTtWOSG3iN{L%ca?DEaU8g_C@)#onni(bIX(p0no$Y?+LIhe*f=_i; zD@O7vKzUcRbMotjv784d22%;)I&!v&wuQG{1Xg#1ha(0mu|JN zsW&f7EK)g5bf%usj*h3|MX%mflk#8puO<^LA zPSwN;70m=}`cS9+g6Qmb>_(*`;0*&TD%;XfcR}i=EC-@X2agd;pd!i59Pw-hZfKnT z&)#w`UCsK6u$HPT^1@gmXr&zG_dS3QqE=R~P7f?kzl}PQa-8p-+^2g|dj!%*12Y7n zvnebqxY?z)d^%}l=m5RGIb3P+(j(28_^$cxs2f4_27`KgnBU}b?yT_Xlhj znnRB(#jqyRMXqEZ2Nqpp0o#z}X)6(^i{(w@M2wW_>8&%AQ6Bb2ITPPM|769ua|FUV zU_z&Hwrie>yR3`)eycs|ye17D@aPa2-0YC~^eJUuRRVV;JM`Ap2a=2W9^j_37H0lH zoYZA`xTp}f5A`e!p%}H7NYK!A?%4YaF;5T*5HsiNzZk!63$@nIj$%ni>kL^9;^CiQ z+Z_uByi;GoZA4rH=rkB|Dj1C#026^UsC567_twb=8}o={*Es`d-=)P-CY0Cor&~*p zpMyQpxBs$$?eR}+YNa7!!^`m3w2VhUYf*(*RX~^awZMT~uPu|yw16)jrs9v3eN2cyz3`}B-+^dA{u4fAu=;LL;JKkXb$$0}; z0-6-0ZDm>y(Taa5Dwd@19FG;CO1ADG?X&{M7u_y5D7Dh+dxw9y(Yr4~GRt*So9CVA zp2uL0p=NF<+Kh^g=>|2Cw9c`&Nv;>Z*-|F>3TL5X<=Y8Fc-V_x!IwebEnmMqxHnh5 zF^MpA!Pg@tB^?GpGEc4;vyWi#BX{!>mmTgHtqgf9bNFL%Zx4rjsjq>E3tFA|dE|7P zNAKC4^w5sF%m&I$^l5P82Jep0{DHCW2V>IJ>i2`UISmc??x|g(U8|79xbm66=T3%P zd}=r}TgF1CLC@`OHl_W}-ceCi2_igxlT4Zit9Kq0_KE?b`^qNWw^CJz=Y|w4E?&lu ze`S&g>>+zKoL8bPZ1CPs+AjpfW09(Y`7kSI*)(E%yfyRLoYJW~OVazJIc=b+Jr`~} z0PW^fEO{nLSz?zBwPnI>BXSJCpTgYBEDIZ-?o0A#t=>lOqF%h9IKw&)m`x(ws~9H$ zg`@2`cN~C#_S0>QKM#4w)+#b#P)_xiE*sH$G&cD$Qof6XId3YAvGi;Ul_LLlFrn=E zBBm^U{aP&A(KqbLkx!Xe6FFI(mv=7ONv^XUOep$HAL_#H-%xYQviys~xK#|k0^&AD zo#lqlRcvH)M}Y=0G9P&k{1ud_zH z8p(vLWToH;1Ouq%)W|(Zn@#y~r~2ksm3!RzY^v)0TxOdxWeGgyIwj+?L#YGqw~r$e#)~Xw;5tCPhm|u{Ekz*t+I31P zSBxn&jSX+0=e7wYyl-1-CiU1}^Sb!8L_xIrrVMP>t2S6NG;5^HP<-<1fnBK`ao|zI zYLPb#W4rLV@LjTv@O958^|5aeZY`JateMqgAQ65vZ0@GV|4>+caeV+^Hi#-WNdrNx zD~79k$SoWvpbKzmQI_i6Yo>f^*07f0UoEh%Q&P0hK9Sh0o-6LjD=gkB-TN`T*>zde$1UwwOS_|m91l$n$1)4o zU12AU4Z_7tJ`1SFOLKx8XN~G2eb3NpQ;h86`-=6rBa~fL2wD}A8})rsQvv+_48Xg) ziRv@^54&J^^kLt9E*Q@rP(sMqncx@03e1*Ki;bQf-da}s?6Y+a;<6bmWo`&hL8__A7xI@vszQC^sOhD57x3eB9osB!e?>fhAY zaseTOqfERCTHZ^DTZ)sv9l&c?k<5INi>*aCPe0cFw*QmZ7IbjtBp2cK7FoBBv&2QI!S6u7qYF^Rd{57bV9}7sYt9 z9Aw=GA4jupn`2{v-tp~tlzn0jd|z+dDiIgAQm3l2OCTxRe@lNciVont4LN(ahqD;s zp)aQWZ(BKtVVN3Ks}PSY)7$(ee1qfx1M5>dR+y1W{>>?9{L2-`h2VgFm6iI!-mbZR zRLAzG!0`$okL@v*o-)!g?>1lLfBAWV)&n_4Styo&A&@xrN7`5h5xu;v(fbCibuBD~ zieUkGQmiN=T9~?Fvc!m|cd^bf7W`KIT;FWqRaw*nbVLRbS7ZR|PS+U)RC~<%L$RSW zG8Q5p{Z5XT7VaM}uN-r0vuPv35uskn7vh_i3YLTT}zNEU7F^ETyM!f6h+YDv#Rh8|ge}Oci*n*<$u?I(S0_sDJYm6-=Zc z?%RF=6^2{W4NDW%genrDW*^f!u4$pG4U#AUS{|bzxb|0ZwK|7+VAPBH=E3w(evNIf zfuuY9$sMr^jnf@$q1*RItv9!SEYzgB9r~V@Pp`?yV$p%cmf}euUYvo>wG6pn2r{eLLwn3cnhWPLDJV_F zoO&ot+5M85aCw3P$QYOAPa z9JxD!JH9AYyWfWaZKJJb?6V!u^2dRaB}K~(%iZ=lb=Ps^%n}M)O=YQUrKoRyj{j^V z800QML@1#DU>^TTsd(W-q^}TTls0Agmja0ySMdM=;74RX1n#7e!YnVp>faB_kN9kE zKKaoOPs(>EkI@)5E2b)x=GG7;>g0eNba*HFuLFikezIiS;smk8Z)w3$y|L({fVdHL z@EKpjRsQt3K*kaDHLg7Vbr~*xQQE&}0XLS(`lVfSCU33DA#Mmv6sql8=4>J3!CzTi z1?3(BL?Vf>KhSV(D*vWHlEr|M<6mf)~1YZGO<7L}BHQwNhF*yGQ3%{b}oYDgBECB&|PovjA<3BRFG^%-W>(;IE z1xfD}ld7{`^J7cs2ixAHI$~fjOOg!@b%#aAOd4(m!7XOWu1p9hL%9jEM~<;4+=C^m zSX5PNbGC_%)AsDUujXRqS0gaaGno@yYgnYW2+%c-dqP_|lW^HbG zwVSXZ6L$hD2-mnn?xWVbF7(Cz+Gz_R5q761I)PuU*v8a+^(JF7;PR3thRYldL5`Wi z*s)YKu?N;IyEubyxb0Kz=(vG9<)Dzj9Xp*&Mpr6`3DpIO7yrYR;=s0*8|dVB!R z%6rA;JZhDNE^X|lBK{do=dtoOgTuC(i$6q~Xxx8Io2TuQF97~)zm!xas!?Cx6N@=M zcs`1Dj}O!$PY|<~kIUHy;?yT=*voz|;Ih!g3H>*1*+3&=JB$bud~>Tg1a~9_@h%zn z*8VaDQasW@WWvxT?Ki(GTMnNP_%5h`_xxr|>q@_=j#-o)^2B*r^*orC*WepfiCtdc zq0>>jVdVjT+K+1Hm|nEu(u1K=>+6_oyRy^MRLhETQF+{1lxU?{LyZp7Wca8ep0b&- z#F0nMvgbDSk3zbpwFVKV2?a7*)eS!l;rrP#0YXa%hAq%nP#a9!&XTVJ`qd4sVw4b+ z-h7;cS&5|kV-hh5>qR$Io6NcyJhJS_78&}_4le46h7V62_prknZq7Pi1S|NhE~opF z(NTrsQ%5zwCs%Esc2?E|938ur-FAm_47xUr*Qf9`0A39-AJ>mTrSE?d;VVaSJl6yS zF`YG__8Yh+HVGlf_k?JXgIo{jpJN!Nuyo@l9?cu;!Wru#J-O2j?@1)*!=MjG>_JKu zCfxkLPzQ3y)BKYH@~wNb=PX?dZ7SwG4W_L}o6(GK<@JEE)f3KGTjg@OOM?VcBWsFu6?( zJBZPYFxtw67ukRKnSi_&I=aK#q<(<^?G0L}@Yd{RR!F~GtJRC$?Plr58?!F}F1fZ= zF4X#CWHsXS}CG;|3V&upcOtw`Q*U}c!a&LNZLp62xoo1I>L_r7M0P0_Ja2N z(DanX4#`C=N|Zoyvxz?11~clLe(RrUY$_DV7wY$=JXc0zAakn&2(ZcH3tS19yzppqy?XnE|elD*|L-}vTUuj#b7X^NDh+Ef>cL;}q9mR>9Bs^=KDe6MBol-yq9cLytDt|(7j|)mCWsx)xjZD-rs5y{8aR-*LO*BI+#=j!q`9HCkeD4d`{@b8)?wxEGxcF#hieD?Ro$?*$C%12yyrx9-ac8$`eJs zz+jADLwhRZ>90N)Qdj8>DMV>e5ZC^$r2^3D7kyngy>-e_-VH=HA* z?Logj0)?93{t9km_nT(An8wOL=C&ssLuiS~>m@qB!i()DoMJ|ugJUKBR%-EzZ})If zC89UzFTUP<{~-K@x%2fJQ!wZ2R+`QKSLhk`?a%>pJ`@NJD4M4gl}&KIofVwS!;!g_ zLEGg^{6EXEJ=n~JY0CHoE`xlDJ8)X#W}QCWSof()*D{ppUY7 zcsQD4Ks$6Y%W-2!d~P5i8{`_>0AD-??w82m{+nC>t6s{l_K&?so9K#i5#3_V+UvNH za_z@~NA7LiYZ*Aw9#HI|F!MLQ}!nu&O_WUgay6VLYac2(xDo3jsJXyyq z=2yS19G^Gmj3C!}N}XdGm?LL`{CYRGn_zOBaJF=f+@<3gG-v-2_jD)l*)jT7O<=7z zYozO{EYX>RL0+sKr*F1>rlqB~O|-D0kP1ByV~NstkAko5wkh3P-oFkcVWX2FT_OIG z>B6>g@mLe%^HslAF6PtuYgpT@i__2ML|EvHd>>>}iO=?!zKBwt*qd!M%frqcqWEzmAD|pL)Yl_`c{Y2;Kr{LbjU{@1Zis9vPLg!kz=6i8V15T|ootyx_ z_kFliy0RM|wKP(#b5vuuW|o1qlAJj8bfJKZcE{kzqfMKMoqsgxV_ZT`rTTxbXn+F7 zcHJ;>X_qrMMJbBTRPo7TlgF0YwC6(#oz%>7`TQgHONHDq{>**R4loDV?9AYGGn2XZjxlvN$t1llY8v ziZyfY$EI({JC9%Tg5FA8Y)9~hXKF^~J2eU(55q@b@oq*8icJAk@ zBuvqS9pIX*wk|o;E%tGQ;VWE@D8i`w20gxM4CeY%?#Td>|91V1wE7>|;|qUb0m7dB z|B*T`EP{xBQE!6Mx9Z7$agEnVkQ=?MEpW!IR|2(pxan`t1n;Y<$TKQqWYh+(rR1*z_lheUPtq#QMjC{IevprQy@s~`i49lBe?sIe715Miv z&$z}b4&N7hYkxzN`rxp;MVm_PPa0=<^EcUlzMuOW&nG-L8~g`DW?qw|Z>Iw#IZ2$v zR0)j%`y6H3|DbY@*r??1vne;;m3(wyqcSTKur%X8o5Gp)GjD-7cCe~SwfzBj)u0$kG9tb`C{rb2(GmKp|$wN9Q|Iyo}*7NgT zLuyW=Lmz3~H?exD_PL>|7J-1(7rj!mNo!XIF$Y#^&QGb*Zq5CRGWi)d2duTr%w9f- z#WXqI)GBalH&-^@7N$pWjLrad_ZrRx(E~{WS*9E^lg4MhU)k8n0=L`*gHrmuv=AnH zs4{WYagJn{sFGeoioJ8M3yC$~Ri+ike~;<2L>yjp32XI!fz9om$qNHXxZ`{H@@cEd zFL0YMIJAg7;K-3%xA@bjq5lIU7$6n)kZe>$2pAvYa&og|Q;4$hY>s)MfWB9h-u#}i zq$hVIU)CS~Q;*6T7rB<7gF?~*>n}{T9>}+~iXgYV#^0G}A;;Dy@?Tf}jKm7#A?4-lDn&q1y$im<%04%Rz^|b$Jp_@DAtZnSN!MWLz zt_0@r<2Z`qC_(5u=;qP*-#X zT))g@6cc>_DWCgU&{+<56-_4~%h*!NV z_na|ft_FsN?NM}zQgzO116et*8A7N5a+k^%@RBjTS_J{irbl-&8F=23-6nU5G$Q*? z0zKl1O97?q#@w&IQ{xzQc6y#D<_E$0HQdbGCInhq4)~K^+6QpQNW8Udmp1p@ZENM{*+y6*?-Tm**{ArsZ$-o<*QY^4h0?)C6-VG1A_v>xS%m10Gpq353 z`My7iTCU~6jns#A1l);9F~TI*@d?6YS{~dCAr9(-O`k}&ZJH#phc zMEqn6()kkbQK1nJrT>hUkR(J3exDHUl(b_bQ8AG_(I!clB$VtX-qQbn9st}_AxLK{ z6&fJ|H+M&W9!yhS>m%Hj_V#lB;r8E!u2neNm}J*2R=27o%2d8GuSgs^S{MpF#gj2K<;KB%|fIKl)XF-_>sA zBUdT)H^(Ve+S@Vi{3@4di9_qGXzWn=V!9(cy zhFduqqm<9HgUAR4K9b)!KP-!uBI|9!gQ*a(^i}<>Y=iA;@xr3;#$^Op8#TrW%=Qa0 zZM_2xBw2DHQ}B*vkA;X{n%R4CmxKe^()J; z7$~k5qD}jA4A_{V{-W(2h1bc>g3Tg*xI!S!1l+f50}T_D-MVxBMnNdt9O2guJ})ua zo{(ToTj4bTb(u_H_KyyE#G2j`&!%jHJv;2~5GJ^aNmlF)kN(8V*oQ%B_w&YOrrcD( zHxMHfnb)Y=9n6(yxq;}F>!@!@7q%}SJTKKPd1)3g6HA0ofj5lX28)u_-wN%?cI8VLXZe zw=aA+@3+1*TL@T{&*rpbJKNqEdb^8C9(%t?Eji&>xjFOKMYTlRmTZtsU#ImlC?uDf zabw-!X7T~V{Sl~oZMKHhN(-IlP-I%(l}Wh_X6qWanZQZ7Yo;ETr5wjmX7>LDV1M2* ziD>Ndyo#LhqCTX9lb9mmHh%_sE{H+GcRlFFld`R&p`HX4I3?=hcOr`|yxAJ6KzR{t;iBH9YQ@*fL-~)p#Y(tKfYU=m%xX%edxCvVppKB;C-P34 zWQyVUTQl%3_#vi9by-!X$*h63B6;+$9{NA774g zIu`Ta4*fytc&=~D=Pc|%-PZ(!`r5N{X5Wuse6ZjxIRu~V09FB*Y;B>X^b#gz<%=EW zL|6N*=>VOAr-$Fh4>9z@wq5&=ucT1|TaaG^+UBgGMw6-3r0yf5Y+#>L(j8&H@^Z^& z#ilF?6%p3oMRf1pA!qZ;6*9*vlmM=v}#_FQR&sZb_^*TObe_&*!;{86*e25Twe7|mx$Trf5 z^nVB9ljN2LYR)BCt5)}0R7ufQZ)}8$j&`NBG}!3nuD5wTnW93WPv%!Duc3gN*RWxt zlCGfc1)YS|WohHt83r)e(JD<~U+xv`=)QJ*oXVkbNA2?Bv}LWgB42G8T% ztkxlGSL)CwF5x9(VTi5sZRPns7B;Jcggg< zce9rO+Sghz%fxT5;s0y4C+#826>Cl6*6k>9QJfuX7x|y#&Wa@EGy0}LOv%XQ+xK`q zU~j2<3ik0fKTk#fH8dC-NubM93^TU_K#1)if?n<<(q0shpW`mnLV-)A^bug`PWo{R zTg_ofgIDYQl|0s)!uSexPGd=BnIiE~bb>uN=8y;E7TgQZd$JyllvK;o1TX4Cdu>p%ky)UEJxnSx*(W()5KUz8(>YN-KidkdAKZWpVK+6lei~~cGfH-w(?xsN z4V-&M9LHoLshCm(IW=mkVAki99A712SR|$;bl(T}{cY3|jOd0&3reXJ4CCbdk|(>r zDr<{REZqMMJv)_fmeb%jFH*Iw>ZK9Z2FBc@8Le!Izd8uiOeb%G+Tft7Ii=qenue9( zY5z!@I9#OG>&ql zXq4hGjqfG#KlIG9-AgLWkLToQs%~&px^tS!^t9@+szVjHsmy${h zt8+%bE5T>_N&oZ;nV)gwh0D#reT7%Q7bUQ(-Cmn(Zk~R%7(M*W>M<|gGr9>^wokgxqywSrrS@j_)y-N_eji@cxjGbqbx`Xm)NRvE;VUzzL4!lp^+uWLF;A&M-Pe$2 z8H)0geXVq1|C|l52uSv_%5loiDFYf`rcM@YO6;N4dxlQ&zW`2l!=_dp+ID>i|Bd|v zEeh*|9iQmgj^}`8Uy8i+a$h51GYqnboLSJ z9Xc?46_+Q315EOFuj4Eh>j>=ou}3%#^_ozxWC3m-mql8Ujz{-QOYL*|j%n}; z!v*}0*PLx-YWi_lD*2wy;_VZdM$F@2Dur4gOB%L$*{8Dn_HgnHf_tm+9Z|Knzyd`fC?24hWKLZ{dU8 z5dKp>WSzOeDD!G4kjDvCtT7C6)TI}9%Sz%m6`Kroey}BbJZ+6tPkW%a1z!CLSV^~E z0!gs}>+>3-f5We~?q(f9?A*k2DeILlw%;UDz{jJUt-W9W6e zE)#EW-fDF~4>1i#))JF5^5HIza~B1A``nxBW{;az-2#^KuUC8;))Y%9h_g^12*b{Dh&fE1oO;|?B8*2y=u=SEi}ZA8sEbcSIH z>O=tET7K|^$UO(vPsFv2)@P=`N+~B+?t8|r@@t6$Zq|^RFNxhox}`6bBpr3gFRck8 zLQi%%e{vRp8kOHN^x=T(%{bSBUXA;EHw2M@bS-DUXGSU z@QQa7cP4HpgB^fVkxbVq)B5CDLSzgw(iwo@vjg3+a^s43v=qU^tOcC8p&d3{U)Mx? zV;0YRUX5>=A8Ah=2RrqUdsiP`wKpnRTRNWExlY%|gT&OrZ+ns-RWcYCy|S_tS)3b$ zBn?1Ci@%yl;*{@voAYYjk@vS+=V+Os;hl_@`>r%oHMV7a+2UGI*Es?I8IGviiyBC;iz&}~tdi59Xg6Y!#pdgf)FZ!k-MOPG50Uh9|ZOSNP%l zR5LhJmXMvCMZ1cHe8EN=BHZt2B*;aeQ%JBl!Z9TV>5_4Q1r0JcAji%3T7$Nw>T=h< zheMYg4I3^YgFoLTZhU+AM^WO1=aF1dBj$jme=wn`c2#bm-|-Q1Rb35s4a^_nQ)CI= z2?u;Or$%$=rQ6@8JOgNaZi3uw-K7F;9ApST_FgAoR>+vH*3gUF6o|n{kp&A8RGBRe>-v5Ds;JS zykb5VwD3%4%xHOjUyLmF=$94FVX1lm05fJhV|NHDC$zp(%MfaiJU;0BC|;9E>e=qE z_N<7vvts5Bj`U~0)b>V~xH;ZhaaTusBfj^tbpj6+XPFNDYC0%q#-+F6ovP6iJ%tHl zpB^@)%pj;yZO$Tfe%r|zd;Q|I57(E z@o;9k%^H`schNdO3DZL^*NnO`H`7ZyAt4CAVZ=7lfHg6+_&lv-`9CEvLCB$%!?&;A z;>?edT|3!b3?aJ&s`*Vj3D-kUms5wUt(D8ReKbnT#f|^|1=x+6c$zZ~u~WgU{#f(g zLw)2I^4=vzVaEj$>qhsR+(26RRMXTBEAmB0T`IF&VRJnt`NTzpHNe1{jNk*oaeB$m z5{+De&KUNt>nzdLHP0|KuDVx+5>eEA__gxLl3PIPGwikknw=4rmEr4f`))tqWS7i{ zh}RE~lJeS90kXKb92CpUyDaj;M(WQsF{|$zV#|%g#Rg6cG&8QV z$s|Xu4s=6VR*mG1`>-os*;A&i3U4(Y{iAyJPgh!~U;DA_+W58{@3lVAK3)c!V1Gdw zc(k0)9S0bHHg$-$&*QM$?4A_B<kmpUf*>Th70} zl?vVkAliIqf`+;#fQRu2W8s@T>fT6+v(16;m26Da^=6nO)KMW?8uYFaiL;^JPip2_ z%uDPP*8MGCwj_zox(Kgz%OodV9Gp-FS=ZOq`_--7xhS9(ksgk-&q)Dv*wuPKFK7u% zCASA@(2J!ysCO9S4B6XUq?+?5yR6p#;#u%+Wx~P{X^uFuL_7SKs=g7vqhxYIf+nDj z7hxQGZvQIDSd>r`h0{u`QWrL7!z>gw!3frZus+!98KN zy<^8`aoNy)hqc*?vcvTgwJL9Aq^!=<#)o{yEkMu%^d2K@%`Hug(VyqjUn8B>mq1v3 z{0oqA?lo0sxIK%+TBOU_$T^L0VXVCSy(*4VuFi)-;H@sx>WC4hgS6=#45OpCTe;rG zPjg7`1NY^Pp9On!Z&ZZNJJ2>xWt0YnR#UG3vY!#X6vBIkcorDEqk#GC_+U^S1+XVq z*^X^&mm42C7S&k5zpuOLkancMxZSU_-z(S*H%H1T0ad~~WUWIu6Q>3&>s4!q|%4ZH$KTtVTyvBS3 zuHZDq?vhOE!!#FydtLYOc7JA+U-(KX#2M^;u`h9iO@SRWAyBZBcq+0EA?UB45rv49?WmUyS<{HR{zci@dqlQZ=bH&^zUsinfSsRAxH-wBn zjl2b3E>7V$1uugAur;&qb0W~17FDkwSltc++%Y3^T6I~%rMIYKSo|}!J7$g^jq;}; zTy6U^M(W-d`yA-$?4i9*jn?*T8ws7+HML}N?)>)oZo@pxTKyvRIU}7Zlj}_NtNoh( zUfwI5Q`jK6*m?XbIz_-d3(<05sF3@O#{n*bofL`l3EsQWO_Mr0r+*@YyRvcvz{^b7 z!ByL|f5IJTC7j((oSugsrJEM`(tYtKCj&1EabERnES3y>@S|HPihLlM=3&y&f7*G* zZOj_!Q<(sY;Ov`b#CRR6abCfhL|f?DIXTkNOKtREWC;sWe?na*}kzKbcY1o z*b>(h;T$nW0Fp-c*JF(b73a_O^heiT>F7d2TtMz9^mRKV;dxP@lR_#qSlF{UpB=d1a)%_3qT_ntVgp@am|1d}_;dx8!&18W3wT0BtfbX%x|` zq3zoCrRR8>qR>Z-w`TR~D7~aGD3)PqzV-1~qJfs|>6`I_0^Brs&g}XZD=~uLKL?=Y zvmwHBoobx))tQ)eevyDo3)KzbZx;~%GU3*93ePGzJ$TwV9kAb*=-O*L*MzMBaG{tV zrTdokufSyqjfN2c7LFXnjm0dF-IZdKD_kDpn~FLWZnozNb}FI52eVG24+*C)vDcS| z79M-8z5MlXy4Js{;_Sb&@C&+$Z&t5G&5BfM&+8S451z(?*H6E{t#NDP=+3Bm#ag!O zosJ3}7~CLvQmgQC-c)28-&dKHJ)G&fR?m5JR?>iL44{(!y+Jo4dyVABCxwz2rX|#% zeCk!BQRNJR9kFe?P6^Ggl!MAdf>LRlu;FQff+d9gaT>y031pxJ1V+J~9oHS)9P z*^p;h`nfivTgX6AXcFNgA6{tuWTC+O3VM|y4`xa%gEVe%JHlW2^DQ{_7EpBVay|HT zrP+Wmg?WO`7^d1SmSlcY1Fw}n`jd}+3HoIQbt!XpTgpuRjokW}mkSfLdDj{yWRD)g zRykr`9BX;ohBuVVoxS$Ab~`Qzh_sf%dzM=aUpzvCFCdpnO3m5L51V!YYPx0=Wo_a6 zQbCeDnhear{K_%RyS`)Zvj>l!B;NsxHo&vJRVtZb_tTHGLZGl|7jZZ)!pocf}m4f^Z%vc#-dw&7p;uW6=JsxsA| zew%{tI7O+e?=JW+C!mAQ6lRzDRa^Q(-W@&x{s%!E!pl`&(>BntJZ@~dIL@BlO(Z7_>m6(L5J>{rzbhx- z9dtrN{du8Gyx9gk-($w#i!1jWRN1GgJJT1=Aw!CEfs&Z3hM?|+A&p5K@+{*M?qvkR z-1^UDcLnkrU=<+EO0mjY$J7PW^>6BkzP;0U_up%S)$BLz;gn^%cXvBNT{b0yqQd;c z-*E(K?634M9$+QrU7+uqJ=sxDX9?5!P(ByMvP3*e9zgAbHa zFo^ZMi)OG{{yMrhb1ZL^9zHAL8noP-nrbi(g+SxSTmv8rguxERH~-=fjG)15xuyd> zRWv=YwD=4yRg)fOLw1F{VeSO&nIcE7Pgg$v8=gjmH~cQF^EyRRtCkG6{G~{z$er@N zK#!jErY=!Dhx3?hi&fMIX~1ve+X}a{4)xQZQVV3p;hPk>ZivUiy@t;Q6*T^9pm0n? z1|6wJ-R|GqA4r!TB)jZ(fiIt(6j14IWb`(@z`M3AfN;+C&un2E7a58vn+`oSa`d-i z|5%~~~bxhHKqCF1{j321mT^}toFFrXwx;Wk6 z+iu3GcJz6AX}spWpBO-)ukt|4%egzb2QrZVl?9{3nnu8>nUl27!S*PJ23|(7B4$iS zP)aKSEPQ5^2>gGjd+WETzBg(Z5D-BG6+vl4Kw4TFR5~T4QEF(Ak{Aw%7$BgeG}7JO zs5C=|G}1M65Ap0lKksvW|A6;;ulE-&Wrj0n_SyU1_r315)|Ln2VdhrllY~1P9XrQq zwh?cbLpjCDG>zS{`_md5Td5-&&UKl$ZxEqUGpL3dsR%K~T%jt~!oiJL^PAcFRiXO> zMNhA??y9tQ8=#{ux67C_uo$sp${X8({ggvKH*^2H>Ou)%6-FNR79;rvSsU@Q;HJy@ zRjZyzF+Z>H-U;Hu#pJ|V;*29hhAR8@&p!FhznX@x_=MKjw3&W51-cz&xr_0(tjeN6oDbs%K(V*~90t}+#@?oh>wVlNE!W+V zWg}Smli)$3JJXB=22A&OGMyUNw#_7MKljBlm3iUuADF?r{3IR_0Mc<&^*vpvL5wiu zt5;lRY`dN3c`_REKVCc} z<7}SGxbGvegr)Qsu%JF$+H6n5awCBpMXG z@*m7Mh`n0%YL}ka#S{MOka~0vREm$`?=mq9iGIAS<2(ffQN1%1iy~m*L0SAZjFjO#2%hT2TK7-rl2*A?7 zpCeUjH}(4CgvF5e+lSE`8N5l1fRXWDcEiq6r#<1O zc;R$)l||yrqpk6`{x>$UXaMZtvsYPPQ}!0XG4vLq&v@;4gL=oBBO}ibr>yIwI|^zU zrONZxdA>$N8vz?50A!z3di6KveI|gB{=iLF_;jJDRSz2NxVhUMtOYMRr<@0rFbn)} zF1b6&D#k-V1-$c4s{d6lw)^s=;Lh9*J2UPDNqf}O4NGkW^Xqkn>0%=W9%Wg~I8GKJ z_3;@k03g-yt?`&-jv@3w#FQ=mDUPNdfkeH2#rD^=67IBTBnl!@h{h(pmkiKYhw>cN z^zs#BUHJ&MKaX|$v)M!VDVbcS{LH{h` zrCPGPdb@<%r?1|XqQfB=Nu9iaqg8w;S#Pmv7-9@qfnS8}YKCIYXB95OdKCHNoNo9s z#B?7;)Ah8Y3DLVE+efslu$DP|_&4H2dM>GxW2Yr0Xz^+u4e~u(p28@%*?dcaXddF6 z*CKerxzlzB)K?wQ`IW1=j;l$8f+08V!qW7HzY=y0bj4t{X;}?rTM}An#onZ!4y9*| z^~qcIkFAFD+r^QvsWy_we{q-?GlFBWec9=~LL__Rc64TwY#nOv4&g!39fKt=r>2pm zDb%wX{w(c+{8CVw@sFOzhhI**y<%nL|AFBjd14%#ge+gCevZrY4bS2<;f zocxw(cN0`y{&7s8MWyl$+WH4HYRoJ|8I{w5MkB2I=BFha$W-iXPrdKD#E@d?MP3D? z;g#bOk=?%qT2v#T!R?$^K=Q2?Or7inUi3!YMyxU_Z+afb&|Akj+v-0TqwY0ato0~? zuCI?uutdswe50$z&Sb~Jd-p_wM{?S0AZR!{k%uS(&bw$L=*gc&(PTSb(J8zc99hJ; zB{L4|eylj)KSjNtpZjRk?|uDI=ygw%ck5gR-QxlI7E`B*W*vjSI0nnuJBqM#*g4Y< z1jigGhyJ#gBxv7Yp*u#_`Bg0w5Ttus^}RLxTOD+9uH|p|=8wO|bP2&!LJ=dnMBB$A zL!9l$H?V&(!WH4TO~Dt1qas3ILv}&Oc>BXh#8Tc1$$avo%!#c|N=xzc+gXFV9-lhp zDv*YK$)@)Wd;}>ouWoK~mRLX*o7J5!LkDKg&QR@kVfUM-$5XzanA1-tu-X^3unV!CUHbG>t@}BuLx>lcQ~2AJDT%&m&u%{IIlBG z8-caAxb+e(mDV$;aM>6$z$a&yxMze%!Bm3o`mvpChFS7l9 zs`yALj4nl{4k@J9HkEU1aJcui&ht=5QhQZ(a^l@k1~d)&F%OaBWf3DrXW~_hW5Zf8 zHfNtSrG(Nwcp-jVBHT9*-KiVDv??2j%Tuuab+M}Wj3mh14=WsQWTrs%3zV-Ue%J_ zxZaw}veVOC>*olJR9n*%_1ZU%%rCvQ@(kI_^ZN>bMH}jhcQ7lSv|a)y(&>MAu9c{J@mlC&C;*w^}k!5(o;ZVbUU3tiFbxP+85k6O->%){@U zx2z=x5lID-a54AxbiOB~$ep1<-p|o3%id|O?3wQt_Nu{fY=9zCi?oNc=&w+F4klb< zw3HtsvV0W6|HEr%`)-!!P=uB%RbKP+usf}!r|LydaBeH^hJN<2PBrC$;|P%&oFo4H zTmWpB;^bVHXizf+y4FQ9xlyy?$bfclg-$~NcguW@e0OQ7u|hcD!`#Mx2XP~Ks(*%PsNg{iIAkpRHbS-LC@ z*XzIgc2T&Ncpbq!tMXssyoHk2de!x?e=rgQiq__5FZBeESx095%EBT4){}jT^(JvZ zA^J*|04cw(JIKD{v+<%20P8wPhiaRmWT`wka^6zl^|a=pIi3g%%GfvSP|ARR;Ie33 zx^UbtCPNWC_uS;}Ws>hZa}Bio)x|DOoz~7zlczLK9eAY5})IW62m#HB{3zbac+vLOs$fq?2L_g3LF*Mq_?UaO_{ zFqzUCd3-Ei{09#nfMRLV929;o&da@$cD38mmNsu?0%Kwy#_D(?gqI=HKafRo8d;GHH#0b7UQ< zM(6uk8~)JlVj&j1`kP%nOT?BqPmZ1GhhvVCcIWn7!pogc0;e?A#a5;B8<(H^!<=_8 zj4zx>xsylH87q#n8xxzq(pDT@>xgL zyM@<1ZN!6V?~bu+1I{EvPE}JJ=>p)+RzTbaG@>4Z_z&yX@@p=cL`@>!@((i)V%GdX z4XD>720GCt)cW>T)VD_dlvZAiAjP$z7r`QTD1X(ApJh-}KBPsHpj$Zfg7k4l-7;^j zE=Pjs((R|u2k2G&?c&pHP_Eu?$h5-&GIreunu-%oZ#qpU-ZH={q7WtiHK)-CrFQnS z@f5u%eJPD{9HD^}5=5%f4(@0l)x5wpIKTK$h?l)M;&YF%zdsSidQz&Vb9*aU{9j&h zdZ*3otxUuDiS?Rg)>49JH~wCqF^x(ZV2A9i<`l$qy&n=m{CIO2#UGNwfJqSO-czP4 z6bx%eMC3C<%Nddpv%`cZ52p;W0d4K(!=%Y_0sBSBGsb)xXp}?uZ!g|Bc0M~oN4G{DxFwjYW01e0bX5ReR zd5@DpQRK&g`>yD)ph`2o+IMS|y5a=YONpG}uUHi#*|fCEtQ8MK(Hs>44VLjJlS@J&?b$VSBPw9NlMa}c zly2%v@A}Bhtv07Qr3Cb!bQC)y&7xjjzVp8y_obkuQg>Q|nk$|wX;HZwbztYQHk<0_ zK<+MdT6m~rGLIOz#h2QS53l0#s;w%CqQA``K1`Yh3?k4fD^zt;DU~O3sP*nqKb7>s zpcT7vS(Bk-;pc0x!Vod8w7(C^_cuu|ha2!3d@gqG9ihP9# z{kO9H`|Urn_;ROLy!$}o{rluoc7VDLIu(-eRU0V}XJZ_=@Gr4Qb6&H~OG=zoij|cf zxN-vrGZrHeHx~Op`PqM*alBt&8uQq5VjC0j)v}(Q-yO4dz4I5v`0wK`$zmVHd=+&{ zWOG8K6~w~o#1-=6`YNsrK2}0=a&MdS|3|s|f4-XOH4+33rxb%&F^iOKSYI#+@dQ3e zmS@EH_UC7fj9>cr8>7CLV6ouiJxc^!>pr)x%&6zYSS(jv!9!jrx&BuwKnzDynWwo@ zTF})0tG@wn5c&hZ{0U|5)3&t&#cL6V!_n5u_5{%yQxFA*nSWp0 zC4TI&mucD^rvvEoCdT%o%`eDgWn~NTUN3aqStD78uH(gtxT^s!HWMHSe)6p|BQr6W z0kr+(#A8sbw+i8)LK-hh+?ag0wKlJ`(x5`&({5SortHr`+eky9&{Bo0XRwP1HmPt~ z)?%hn|NAwe>mBu*sQO7ygN9wo#h5DdjqZ52MNjVwOY^DyxE^81ZLzk;9WaKm<1wb) z-)@9nb@AI5?g7{$kfwG?A|>Yhp3**e@T@yQlr@^$EHqWFU4WQUTl9_IJyKFeP==Et zOBBD}m)d&LyF-~oj7LNi6~?48=BbvYmWCg|e^rhC8U}u9j+|cDcHBe!x5_?WCX#CA zdGG$^<9`7Y=YL+sNc#WRtI%6zTKC?vZ@Bb65)ydt-o0xBY6+}lcHY0thQB$2F>za* zU`Z-SG4965Q(V^N_pbq7t_DyAQCc$FU;gG_IBTTAQOi(f2RXxDYxeUD7#N@c+>b*b zM^40A>Pd`Jise7HSjfDC&Z0o-3|;=5pjEAQw#4$WQ+CW zdn@{#H^|x&M1?UWZq&V(%l|gpTdi&jDHe4=u*>zHv-miwOtcySW&`50&GgKQoqb`+`A(kg{gu0@Y+l@ z2nZwWZ~UV$RWO;a&4F=vJ;9Lj1AQ$#eXz)vt8ogVxWH@p4sqbE!8N8De?G`qNLA9wkw26jLYFqm5W8c4~U+*>Q zJq7w#RF;JH@w8y@Zs*@}d4}OjfqjGWG406}zqb;P@L(1z02zAwJ*)I4rl2>V-%I#{ z-!3;GvEShSj7Se+aoCs;?4wn=M5QIRu5HCL9p$?}s(8_`3u|CN-)-fa?c}py8ft3l z>x_uBAW4oSU%Qi7est2u0P}u?r(gBD*?a2R8>ht~MvS)Tks#g6Va5d{T#Q-2e?i*E z5B%@-R&&ZKdRVcEF@9NGK4k(7%&TC$=VtWzRB%hegHn(>F-!*ahFK_f{ z)9AmG$ba(U#W$Pj`k$^pz%Vd;i6t*654v!FtC6j15W{azXC?iQVT~E}@j4bEWN6$~ znV6~-U)^6RSF$W{P=s^FEoS`UFU*OqgD-dEnTP0g;!+j&d=xD9&I_?sx)8C0N{w87 zO=`P-4XdGgqYlm72gn3>!mCfc>njt zAuPOK1~YYs7fgiYeB~j`1L@7n5=Pfg`|ZG2=;RukVy3YDUCH(1)v-A1D-GMTv&|j7 zN|@v4gO@3>^b!qS2*-uY|Ggqsg%<5`o+y;+e70<81^ulGvZ(w11g$U{{wM!sSeF51_Wst-+_F$kJ&&pa z8*jOYw|2gjcZ+*8ef>V%01Bm=^;i{@H)EOgP{4uPIS_)bi^+vy4}UBDqCj_(6s(Sq zeKOBb%KF|*-SL5aVm8iB*vnB>g87t}xy+><7n*N;OL^v<+>ausVSBYwIr?_hOyeI^ z-{^6|F+|~*E`qlUxP+%4S3Lps;us4*8 zU>VBG$6C6qY%Be-GJ1?cIWuy<^cFhEMKSt|yeKN<&C=3mA?ab33w==K&-X{em(gMN z*R|u>PRX4qVf(AP127e0yk5@R*ksX+mq;FVuFnmz=?eQq{c2}9ZnKUDf`~I_^r|NQ zh^5D<7=H0SBpl-NdoO$N+DwsF)r5I)i%u?HP@R@Piof|eZQ*;p=9|8gvz(7m71Q)@ zp;hr0DvT;|v?>*z^1K5oi(S!as^6a^cgh6a<;bJ9wN$Be8RtJvwQo2|oS>6=3~-Up ze7pTbCsrT5E`F+h7>u#{dsoSgfD4%V^uvu%P9rf0oe51iwo<8;!k$+l%g5)ZJU1h} z&raHb`teH9O6fJQBJn(YbuQKmo5b-Q51wS67QZPeMG8NqUKTUNRW_s(zOl0j^+K|< z7&GL*cL$CVr80i}^DDSqI}SWJ-HGCyXa;PxxpCQn#T|YgR@j9Ws81fqK*5wddjl$+ z0G_iqzw?+MjE;i>&X1ozOi`sq^xf{J`?|p8Gh`uSwQN2lN~j}$w4u{1EIRTnFz!3L z{!5Rx>(nnL<+L=MLsMRC4P_ZlFEhO{rBrS=$-=6YABu)O0fURdWp!{n|3d>!lI9wd z>Bz=pdCWcT)TpHdIMgzoqj+{X&#@FGWS+XL|9;M>6l1Cv$!n=rb#^e$;I=W5OvQv2 zUVwW1w@iUy-Mk->*q0GQPxeL<7+TgvJb^WEKCyadrg6Ju2l096;CZzwzolt07GI{u zGwuwc(`hS1$rpE7F_8ql*VWM0kJL1W#iQQs5AU!-Pxvoc`CazKDq#H0^W22l3}=G@<~H;^FlQw9s|K@rJ3S(145=J1#J)CM5C&#| z9JBfd;aV@tI8>{MPMt@Y4K!Ik8ONHpT(#Gd4LUOiDGwH*tbsSK$)3X6^8uDgLz_;E z9sS}LrAsOiENLVURU2^)L|<%T_Qh8IE1|uvot_;|qv_`3f}y~N06>6lsqVXrp@4WE zVxX1#f^OPtH|RQ*kcJ_2=5jo6I(d)Z<{M~HwU`J`0R{tz@sCwz)L`G<`G1hn+jbES z2@n&bx`>+4<4)w|Z<08SKyYTM_4*UD8xq@ZNoS0#+uNT}iGl+ZEQd?wYI&XUKQJg< zl)^D7_-(?ko1ZwGQ5p6*znZd?c9Y@wvsfZ}zI5$%^JzhPJ1kQ@)`Jx~FH>~g7-Mb@ zAc>HVfHn@wKT$q@qq@lXf|fZn;|nQ*X1f}Zr(bE&V;IfrUcCOhYCyRtAAWs|X3N3v zgIBrw8d+6=aFIzTK)!t27W2bF&wd^FM=!QSCg-`0bcOMXgEv;Po%^$jac_{boiulALZs0*DA{qk5S^H3D0k$MlWmRLrlEU^g0{}ZW<$Qzf&0nl z9Oi!Aau|ob%i1bX7`gHu&}>zi^7e(2WAOfo`sCVC?6lN9YT~>y(N2#cD?C5tzA1Wa z(JUY0bSC7a^$Rs3RR3xzDd>d*Qfl5w$`AsZhWqM@g_3Gtd0P&&j@qo}q^tAvW@C__ z*l-agxGX&0>KOy%O0B?9)br=^@ls-x@0F__XX6?2z^5+UzTr&E(EHfTayT~{N!v$Z zHTL|LL9d+y>wpJHUK=lPczrC^^@7Ys=mzNQxG|OV$~6zD_9q=lT#o~~jA~lN`qSj- z_BT212j!4LVff8XI|5A?oQLcM-*FdsJu1H|#)>@^<7r6SEKna0{t>Jo8q(_AP1SRCYvo@(K^w7NPx8s4^u62QHI9Vdp4IlY9LK90N3A8Y! zl9XXkG;z_yng4n4MeYR#vr&HBNaKw|Tl)iRTlF4j3jPcwdq1k+y%)QR_>h(%gdA$i zS@%4we=oG?Q3_wR4Dt4_a=IuS=M$Jd7JGAHur`|IM0Sik6r^sAeQa5Ly0_Z{5*keY zZ>=V2?sMMMNb!|};c;&31AFS&AWG4egSqP`I|R(;=?VK<-ibUiQ}7E2O;idQ8ly=` zgZ54GY-oZ}E{DH+Yx_3yIP3R{sYG1hleg}Pn&&EV`C2CKt*`@0L~Z@*SWcVm$6Cn( zvBZzKX_i`Rl!C6tle&*;6IYdC`o+u@-2X* z2Ur!PcPq}@nG(Z?^Yq5ohsp7v!Bn~1XoL(_NQ{;tJ0v$EL(0G0XH2r_nQ$+*-O(7W za46DutbwSjlq?YNHIOg-mT?UuNr4Tts8zm%%SAA&&nvmp7-Nli%46le1qPS@CO^7Xcy;SG3JBr>)ZT0$Ppt*E}|pC{&9u*sryOQ zl9Wuy3SP;3b5ni5;hR78Ct#%LZQus!hm?#%o2#o92#)otGgY^jKd@ zOPF=~O>Zt;=SmqjT-$q(j2l$MCmO6XvYOWJbiU!=c6v;(zFeiwxVn?)HWgNYOZZvS zqH6e}ifnvbxjGlpDQOcO%8vcG-%+k^jjVP_rg9u(&R)7O`|+lV-FEDw$M!!x!j~_^ zyq9yuPr-qPOb)|)heSEHGAC7iXe45lG59~r%J-Yby&x9Ni=)97~CQRgqY zou9ogiyh|}k2hlJs9Ouni$|9mqZ^bK@7aUnYaC?!pi-NuTB(CrOZuH}HbP%JUzapM zq{t7lUEF};b6lxyKmH-9NmiEz=_%;2U`jvbI@1_Q$gvf6DIWM zilGiS$q1c=VCn5g&5A^NwQsm$|5%*A^>=NIR1R}&+Dkk&<7Ui40oLjQpoWLjd^jT> zvYZ!IPxNQ>{9=B+U+kNcd5jLCB^PRvt~p#UI(uv)9sIE^c~0mz_#4HUUSZdmqqpYH ztMD?b7>wNFa-wCL0VZIoKh5nrp^`kZ6-(y_KgjVqLsS$uE28?kv85Mybdt~&wbK%! zLfv=Sr?{u#?!ybu3(*lvlQT*?tfFdHNcjGd%#|LsXC1+$)91M_v;(MsvKC!z;9>bX z&b5qU+In0_m1vUf-&9>!{(k7_aj?!@)#a9`Z3NduD^ZSnx_G~^VZwi)5+RQt z%Ur+bxc%F4RP0Qf;<~d;m_>V~qJb+pCqh+&}N zEuX+|-;{d4YNDV+WQk_4pw{hdShgjVV>%VHkim;d)Y>yjU3F>kv0{H9bEa^878U#> z=4khgT;#fkM6lfiDon+8O9hB&35soGlPbB&P;D_~eFJQU%zGg^1wnStWa6$A?IIww>S}#bUE~C#Mnp62k zGDd*lHFQdyx=cVpEm|N{e|wj-~jhH+kQ)OG7}woWilpVb*%|GIizRUO5;=V0NwGe7SaQyPMQPsq=E#8%?MiODxyahc3N>So zDC#p|TVG>bLH)TdP~mchF)4p}Vz=^#@5^df{n@uehqXBTVPV#Il#ZrbsK? z2y-zmY&T6E2!+hrLz`2?uJt9}89LVaYojgk2!Gk_9l)-}(yiaGe#5?~6h@aN=Ume! z(x30XM75x~BZot^=)!@L`ODaYpvtu@{sYJU2R*&H65|=lQBEkLBO8@UIarZr@V&T~ z)XWHbnd1RZ%~J;4rHY>__Y^@5li_6@D7zn zGAYW$d2WTQ9gWfLiH9W!`LFc$J%<5bL#Mpr9&*!?*F!8d_1Znu0@xSJ(!kmHYqx!^ zUB$)JXI0_Xdzo;mV{sw3vshrL*cv4)3!GBz@y`2@+PV5>ZmjC*r83~kFVf^A!XeOd z`dyKuC}V09;F`OV*+Nr)cJlrPuV}EVtXAy-5xRA1;sh&~x{MMIm~};I@#N&H!p4ml zA+fi)Y&|TUOy>N_3=0Q75Z;>=><~+DH5UH61CYr^-;Wk~r_!s&em=Kh)8|q=QffD1%MRue>gtpY z7IOL;x?>nX4O8J~?Eyb60=I9S=($+JewFc`)pCf@zQr3wF9tEC>!OQdA{y@%Wd;Yn46;oxu z*PIGMJl`N|GEMUl6!G~7O)!31MB}`n^Hx}-2iS_KPv^=d*~4zag0HP_-|?Cs+hgaE zLSKkueZqgV1<4lCj5_c#rw)m1XA z4~p5#-@<`~cFgfC)0@fCmbydPai;NSxdT4bN~3vcLn$HBY$2ylO4h?!bkxuk5-5gQ z-Z{ZtJ9>Lf5v3+`-D9oxwe3h(7iug;Mn#zYTQH+jV##Eg(&}JVnlcilZx7XpZH~eB zX?{@(zV~?=}!lnFx-!tYexO3T;NY z1&!0oD|;rA9$KE8x0KcH^(%0{*)rxC`t@MNJYA$RnqmEQW!>|;@N0WC;o=3z0z+k)-1IP-p zkl(x|*QEP*Dz>4+$U3=^723i~c@5;C$BIY1IlLlTO)Ot3!z+Z+cwR{B$(}`3a5K+8 zBvzlScYRP-$a&cT{*ujTfq$Uh3gav8ZA2W`&k2=ka$@SvpM~zk1n)k)Z;vSKyk!@9 zA4YlyAM2duOUe_$u)d5q4ey%R^#tK*bDXgT(=2_cYTdJNSchk5Cx)}3IAnd%&AI6V zMcV>p0?*o!wP)5y5NJhA!hVFlz+3jp;bKO0P3*)ZjO%@x$g0@Yb^h+^TvfdIW?Uue z4vR)FE(Q`*>n68HP*^zm=9AkmRt}^GOqLK)Be;v}zyG*jA2H8T6yL0Moe@*k3jMZbu|=od6(8j|kriv`wj6cy zd3D%9-f~%g*6%~ZSmh~~LV34o_J-~_29(m4M^yggx{@H`4W&VEh&2Qc&W^7Z)9#Kd z9>T_;5_fzu-I7&#VpNNaBRp}-Xvc_jFU0N z6pw>=qB*CB2ZXPDsU#;{p%`jPEScflJMeEbFG5Q-721#gpql!&Fsv9bB`}@2Zl^H2 zD-M?8_Q5)Al?0JF;jG$PLiIbvtyb{@XQ@%a^x7XjDc5P%*olU~?>F>))zjrI3C@9a zEEv?}2QPL|`(ZF3fAybLY<@%~B3Fm9+vl0p%|Mw0e0{lq5IP6S5jt>39Z=2U%fF`a zT`qdgYtQ@9Dm!{rQAU7T@b#ec3#uqq^Iw&beq3F@ND#~~g3vt)Vq>hDe4HUtX0-=X z>;pDVLb^hG>yC1f41fG|<_sin{OwspVF@eYen0fqe_$kX+eJ&gAy*_KAgZxuvz7r7 zQxR^`8r+!#H5q+UMv!P0H7pdEnfEc5wtS{qa@u9p&bLr5Im0Z=x6f|qxeDj|g^Ouv zi6`^ykM%4BE*lzGgQsEFXZA(D5O*Q&<7DDTp6Kc8&Ki zL|joih&^3$G;g~ZP%HTm%KG6iJgMk zqV@**m;Zb~cx{#0=&l{_lC=#&mIJn5!f`J|Y?8Z7)Jt#g)e)UDsVi=U)Vto%ZjGpS zGuqJY`4{~18$Jk8J@pDyj!s&Te*^O(YCgZ*j3gNyXall-zWsbZGe!@{aomd$x4BPS zZ*rSYsPtFwn@~r4+&5Xacc;Qjl|o@9ZxG3{pe@pV57$>7K#6K;@?)0uXm$gIfF1Wh z(v;ZYYN2^=CR2mDO=K{&xOk@FqUj=k>;tTwljiW%&dq`!BVbiTbmm2|vVbv@_tZ zdMr$bbF5>6-xx^%-EYn2d-;7TBZuXSc!Ov#?m<@~$}=}Oi(*Zw8~DC`lzv}RPbuv} ztbH|Qt@gR?nzcdt=X+-BnKyU(S*gr|;l)EEA8>DQK_KqH@j7L;&neo(c}9_3fi;wWn&H=%ggJY;l(pw`1u{eAiv#j6D$&bTSuwnQq}R zWJ>HkyFDyc5oG0mLnB+QD)f}gzP>$XH-wP9uZs=U6|PPm9)37Bs4s-`EkT86)n=?z zM?SoAQqirtgSy%(g5`PqY-H|0R-sV+aQlt8>fLVP`GqJ$?khR+f#$uA`>ep3NjH0P zE%)oU{9LIjCFzwmsqrCb_N&bL+iJW|rDr4{Xg2x}Rh`{MZ)Rn>`8?*>9eJr8mSjySO}*^&gRT*%4n1thOjj`AXW zvA(}Fk6HMcVY*g-yO@)7)C+&1>mibcgNcxpEaD6$nt^hpy`mZH=YzZNjO^E$S|6TN zUMV$4jy((k1@Vb}in1_yV)B5SB}T6}E2Je`lQGtD>$XI4GhCMg@i=9`S1Dd>(iBtI z3kc1IhA@(j6feLa*K7(~Y|sES@k)&M(S}~1uZ*wiff0ph45$6n!`KvLo?%`#ycAv> z1WpAe;%>zLYAH=i_`7;7A0(~fTw09JAgxz}u}+sQK-!reEI>ivkG^LQ2|yPDpaAjC4ulv^?~8xHB;XcCiTBepW97JRe(1@SBfc$U#dCaoynu5{S@7B^9> zscbvR_>!^HN>RR;H;chGttM?Yhw6cCch0DrNiog%(Ff4rKX;^}G$5dg5O_8CP|$U~ za__jbz)SRvYALdhQ@0*k)aTU7%_I!(+mG3(K7bz{izMJuE^wtBs-?^RDGKtY(|;+w z0b1ZqChqeB9%0+dMJBG>ROY2d`hn?bc0008Rd48I4sdWej8187V@Qq^L8 zoI6dShID$%3pn~yL>8gzM?n9>H6N3sk`rxKOnU?z=pOe#5n_`QcLy5y^7O>-8|Y3= z+}tLIrA}kY%WS)aum(yb;lZ@7)b$wv=9GlxEhHYT@75M;vCW)!^vQ_UF}zv1ig;Gk z&w8GbtUKa4xhA_gwG@NO@ZMMLT`@cy=SN*C;umZerzKCsc<$K|#IXIS4>13uQ)eEO zTS@wH&RX~k$}fbs-VmN?8RyT(NC4>m*yf!WNofT{Ov31EtKn=%pe6exa=6Y@P9s^7 zV~(YAtH$QHLz(zfKY1^li+Asiw2c~dZu91=XI5FTxw?sX9TmdN>Dm1QNs4>8&Qv-{ z5oSs4uU_%f(>SVCB%pU2(>eC#_d)bIV&r@*t?BB z&>$}4XK)jQH(z(e$(!K6ZVS-!g#k{F7WxSaf4K zmdlzf=r}USl(FoL6q|*%bjdz-Ops7p(u^iY#rlAD0Q3h(FAoseMsMomTb9)hLHkL^ zGD~L{@MYTw18@-U4a>3ABKTy%1;({&g!|Qb>6>-~3`r_r%cVMeq#GlKzgK+Q8wI zcPB1`HF0lfnzo-~ZTyRpeOL^r<*XOmiKYqO+Qr?iE>k>QaFtj>U-1nJfMDhual9oxlt8kG*WS4fvYth$x zAA2x00!caL)~!6^j2Z;G&k=_j8)L00Qug0OOzTssqY_&#*g`5SS!EE*8U@XYXWv^# z(2f6s*artc-Yr8t^6UH#-hZ>^)8|QGn{(|w%1HEPiAfXUFiVcMd>M3&m(!?Tq)FTn z)07679DDkz?rzR$-QMCsWmzmN#T<49sE7wX-%{b=8R!wM^K3JL*h6&_55v3;hr&}8 zBbESk$=P|z*Y7H_+MHc?>_#0( z|B<^#1t86aS+!CDDEdYc6Z#n5o8zmz*T!D{uK&sE8dCsHSPV?RdD=yBuB%JW4@# z!}n+9%GAdWp}agHFH1(+p|9#oDY;%9^X>CZ0sdyatVC_6-gyhi;h|LLm+AJOZ?S*W zCgn8L=k+WkRf^rrftAO$>WW%vH(WG*C?}%(l{)4YF=;;bf%7WF@Kk9=1(2WgU+XQ6 zwd}8VVC^B?rUT89Lo2BOa;J{tGQnlGkltD?!X-K$+tAHhMIcl*m-CeuvW; zRJ#T)`}Ik1A~zi((5IqgoQai;CYFj@Mz@nA#qKI**j;3{wz}>Ds-;}ft7iQgz)%3W z`f0wT1|BAKIW*$L53TD9L=S$OyzZSa@mPq^eq}%(Z}}aRD6S1+@RMI2kSn@RaH>f= zamqhY0V`r#pHOPi%wrC$sb{|$dCp)?A{VaN4Ktt1>OJPOta1n*q!HB*sCe^CEp@kc zgn9w<#mWelBIe>qRI6ab0CQ!_XL34G&;75E5u*~fz+HikocO6i!rmE5!g@!Su{6bK zHE;;m3+bSHYbCw_fC}$9@xDJf?L87aY&}-%Px!jsIvq7u>hARHQAjX3s`kPnnM&fT z!iT7_*w0t5nk}x6?#NAh_Zz7Pk+GNxpYu9M2L`jP??^Lcsa3t6pETL8cTD+UmTXx8 zDf{sY1-LSB(GRbRA0>e%GII+({s~t)9dBlmzF~(*U)0K374gKSJT;H+wrdL z)%FayM?2hQb&O;gSYZ}XrEfN%lyk=#flL%cH(zE|^kIcFiPWMVvOqCm2Dn0Mv%0-@ z&g>h&4HAa&^}H^~8u$BhcN2te8DseVy7?Gl)>zCRV;JZnF@nx3jDDo7P`(~2ezmRN zU#ZJQaT+FOD?joo;pRuJnTQ*l?F9|ppTE5yAGuNB;?;bEHb(f6DE8%x40!)%F~IX8 zt8<@Y5i0xb)FXoQ@ciktZ#85J+$^~*HuP)qL7}zMpf5bgJ@9tL2ci;9j9k^ssn%jx z-Syk2{*tZ95?i_kbrL=OFlr+MAYu}v`smAaabeGcxX)CgYTg2VpjUhrREs*lT3#PW z)>U$b`|0`dAS1@|E@qfd2o~K8Iet&f+(2i^veS)Ajr^Cd=hiQKI znCM08YW>Hrd8PN2Y&ST`>;@Z^w<8dz!^znBfWrh~9ga=yL|V2aP}7{Ll=7r2M)xPv zHf`TOt?J)ggl^fVx8v@0R z#r9Ry96OF2zSlrx!?NqmXe=-Mn6K^QzZO(YBM?5oWaL*6$sk?)ZA?wIvXQgSFQDUW zstIO>t#tkNTx;uNSlbAf4p6LsHeKmhLDW5!EllUh{G+ygU{M#IbrJO7N$=NkG?w{=JV(w^FRnJtFJl^sk7QB!5KHXd(z)th z#hFi!;v)ng1Ms(QZ8@MTjAdS{dpxG$Q|87I=C6j=TYJPNyQlDWKX_PQw2@soQ2-u( zX28SXW|KIYOBOZO@X!y}o6fR7wd#8|gF=iYwoFLzKnWAi%3>LfWpjrncw?G;WcW*Q zFD5>?ek;X|ZyyO}q4C6skD5z^|E5`<1P71Q8t`jcH{L?qMLs1GB{564t|Q}dU4KzE z-u3LeIy97}F7y8Sz1a$6$}8|^63$plO35ExrU@h331d43TM!G=-+!hMp39kU43Sx> z8m)Q@Jfk+?R>`hKMG~LhjxXIEVd8?YQ@Sx1PWhq!x6f|%QUs1yY(9$Tt65(z!2*(o zs;BZic(z8F{tgwJ@nG!6Ug^UJP)bczlhaEgHv$A zGmYEGf;4%+S5DO1_S=VQ)MKt)xrnK3Vd_aMdx zH^cf1D)027=$SPe(*c1m;B{sVnSG9fgxr|NPE6&aNYs4PnPPYE35 znx(^Y(nQ;*6-@Y6UV^7_+5Q;N#$>?}IZ{`A-rCM|f*Q(fDLArM!f)> zD#k@HWhLX-sXh5)_6kP2%i6F4CQq53O@8b~#ZN_o^s~pnyXouc@lNz+jvzf0yq0u3D`(PAU^iwruru=xNyVq$$=TxFaL-Ke3$9~0{B+!BG_M^3# z80Aj5e9mp9kKTH%qvR0%$L{;?IUcp2P!N?(Mbu8y8>@)RY3xk z>N<+WxSa6~P26jL6*IWz%JytV3m?dQhHmN@*`>>Y##L%=^ze|SRK}waIo#zoWd9ko zcN33+?xX~($(R2x)%%MomQY@u`QANXofeO41I`6WCbE~hTDKxmsas~{u7^NF%#F{n zq{zi7%`jA3lBE=TRp|SW{R{1LndQ$%-`xRcBfJz9)Fs9T2-INtiO;pwPx&xajeb*Y z;Xb|`tiRxquO7QK##$gwgcO3y%``?4wQ zu!}>k&F=aXL)Cv)Fzr+O-mY=;4@IQQE+?kNC4=JM58pRBeHmAV{o1q z|8AstNjDI?M2j8-@b~{GO8g#Z-ljt25*XJmbgrJQ7aDerZW$loWwKG-I*1DVs0$I^2$M*LqE ztOV^x9Oq$d#KzP2iL~d$z0TNg6(utEg^JDd<{KKy<7ZjzgFGK($%#oyHX8V-ysN=2 zxcSjfqJ%LB;Z+=F8WKHS1^*D0R{Dvf$6_%7*GNSzUB!)6 zxmA!7eTL6dPRgMJ8eh;lZ2S1<>B5JXKg||%b&BoIJWwC?pAzC(O=Ny8g`Iz#FerL5 zKUDiaN4#L$eZpuGuXq<7MoY|#)s9$3k(du!29PknO-JyO{6*RR-4#?hgw?K|LCSR@ zBT35w0P5uamsD6>ByG_=8MTvjvCf_)fxviKH8}Ax9g}igIP0H3p$yif?XMP%y8yd8 zHI;|$eXL5(f2TRfQIv^chZ=f)SO1}z|BV62vu|mmk=FgCbxg)2r*3@Ei5d~Z z%C+RZ3bY;!*G@HPvl52`t>`Y5B=|lO*U^F~%v-U4rGYaA0Q5Uvi}-GMDgXE-0j_nI zw&>cW@(2GMJ#Y<%_g4LZqJ$am|C2w3u49Mlb1~qK7oiVBn9;_fqoWID)8R&K}5SB&DU1MpPQ4JCyG3*dQt@-6bI@E!}k^&CuP_ z2m_KsbFM+}=l48koj=Zc*Sp^LpIN$w*|TR~^^H$_10<{GkqnBygrR+p@PD)WfB*hu zusDZNIrDe%XC-p*M<}U?B){%Q7wQWD37Z}U4((GLiro9ir`!y~Ul8m^ax*R&o82B> z)w7NKTE%)77#MU(UhKpFc*+eJw1i{mrnvAq5Au54Us$)@7-?#*H0529c7m^Zcqi4J}z7V6&oyYfi~RWYizHU2Y@7lBZf<<6RhCu+k3mS-9c+zv@COt!0GmS~ zvr?7tzx_QrAUV9`L&2j&`XNwBVJLjze?-Ere|u)VvF7V>H0fXW9TLUjfN@F7P9hhG-3n!t%@8FWpC!$Oui z9|DJzYYr7o^WVa^k`D5-*yuuFEcO-_aR01#Lb&_f0=W! z;7Wb9`Xj{GbW9Y~yU5?l?;h}nbXNwhV=R~j1aG@_Q4c}O=?*e)VCX0vtZ$P>FLjBzpuKm<`JzOPERv7e*L_S)7^m~9chxW$taTCeFj*!(@!S0!-vN$@yvjrge4mfmxUHj|-6lz4ZFplJQ zRiz@z%=%AVgmWLDVL!qnCN>q_=?J4#{MhOctiuw1PMjJxj4aw=Q`?^&SagBV;O&E; z0IG!@w9^b~ty1GlefeL3VsEj4AS8%?Xgf>zvCuBr>~|Mmd|m3CFD5vYeT>?`m^S|t z={|vi*22*2ma;Th2MH-hSm=5?$sCdKoaoj{UpVt zfLCHyK6i0(asP=GZ0a&balGWo!~hTfF}64$yuHFR@J<9%{V~phH}dg9xE83_zEyZg z->M%Zso6-P++%Eh127b81`^tT_DsAV;NfHJw&iSGU#SmCkUkj?Ck7+Mhz)AhV&dMr z76Ne*I)HV#kL2n7KlxIE|N7DedOQMx#iP);D2LE{G9^USS{KYfOECsXsso-9Mmlt1 za69!2A%%a)|J&NQUV9Jd!JKMkyFYr!mPR3pOU@U;G`bVhrIPJA|1k@BIYiQ0gj{^r!@tOfo~2! zj5dZu3F`#_2PO(s{<=X7Z~Qa;1-7sEosa+d;J^Pm4ru-Wg^<&nYL+*dw7h-KQ8Wf> z%gciu55p!50(_|BPFCH&e*IeNu}=)#P2848Kb?XbdA_o?{J$NAzHH$${hN}9Xkv!# z#at$9eR}w;2|_M;Km(q2(dSwHW2HscT!$Uu(phv=9@_)^XAdHc{+^?NV19kEP@?;M zP(Z=F!*enQ#RRYHg&F8#GF@9FfXRFPRaKe`ktgsY z$v}-PsSN0Y@;7k+1Ls&`Ekez4`V1zILXGmBqP5@zE*p3d5i65Ul}(iBWjuqxB8z+? zH}+T_uwm@tlW3IrzT`mlXT0BvF97CIDRLJ8&fu9+5zlb$J*EPJEHTSnC!g=%9#7?^ z_7gL_b#9F2$Gao=C@R)A;!DD?Oa@aMl&LiOMw!KKwb_=xP=X2zqrBJz`27r(OD{dv zf%ZliPkh(EKdu^#xjRy5MLB32k3~Lk6@|6_<*((uC^3?sez-ZFL`v;CK}wAz%7L;^ zBzPvr4u!d7;3wmG`BQM2c70u8-ZIh(n~N-VWo5L)v|G+slZecM?ytwVl_`F4dZ{FB`R8GZ;bFgf|Go>DPpx!N zLeW5kL$*BQM}xU!SLjD-lc5(+JIEI>+%a4;VUphGRzma{cWOhUaCHwk(;bPt9p7AF=UL! zH1oX?8`F~dX?I-6#0QcVX0T=b)<4r3a)iGcRL*jwW7My&RxMUnBWBg7Zc1DMV5dmg zX6Zl2?nk@FJuBsH5OP9CZ)|Lmzob!L!>;G(ZLUewSn?9 zLjW)mE4LVa`5=JgdZu2oKLO1W5$2lL2#!I?`P8deFPYVtcGJ(_1y*omenmUqJ$`P4 zf#%BsM#0Ntv(U}>%KrxEcFyilC5%wt%Io*SBT^pI0=@Cwm^-eio&F2@ZnvA?7ix<> z0(9C+)}HW3!VtjexQAE}AV*1T0cX9r5}7|X``@4&nL!z683UmG=yX%e&qyBKSBXGl zp!!p4Im&c)|B0cW_ZhruZ$RQM1{)5m1PXZA{0&xB540NEtpp|Hvd86(d`rU)=vyI> z8fThEy^L%W^DUk(d-5A&{pYQ7t1srP^^Pmh=W~HgOO)`?XT$0JWGLn4gKq}$HBR5N zbIH%7F}CU_#XGC3bL~%iV~F6RoS?#Q0}kxd~FS+t_=G1 z;^(vXnV^-=XPs_98^@x@bai=_dS3N9UsgJ0 z_7KgeJvhQF#iKo2qR1-S1t?Dj;$H4fbk;xhIx2wOIT)0r?E3z?)fm(^>Fp63UBK8C z&`qvAFcxyqJW8{nD72@Xo!hNBK4VX2&h>T5eT#iEbEYOr9OE669rgIE3wa)@e zhzoshxtkRxKmQT)%6lK~HdL>rGHr%ma}>}-Mh!x60LUIM*67DXc`?_1sDGxXUaUov ztDGGv*}Ll!CiY`F4pz%IH!PsQY*dX5^uJDn*SbbAK=Zhv{-* zH2B8N*4or@NL!sbaR1f|fV73Dlq2J>vJ{+*Uiuv$G(9iU(VbcwL)ZV)5cl&-@|#n! zoGQ=lP2yAJObjLg>NHVc*~zZ<@+mkKsLVkiA4aODC2iC~SC@NJR>d_GA#q^NL4Ho? zYzFm@J*?)K&*IJ*HP{S}w05~xtNMQV2f%40-|z9>|Mn{2G_w0VNgDeh2om0|Y0vM) zUY5Pd`cx^x!YGG+bOSM7u~w{;6Y6a!@UDD#qw1+sW*g1;4flGr?;5F!TOr+5b#76A z`9^U>%o>yO!?eXMFTMU}*HPXA@$pias5P`osWuQS!rpXVsu&JL(i|YI&oPo_*exR8!w!HO?T2YUa)Vw1X)qO zS{B%S_&3A&F{ffNTU&v9ImifYy8J3eqaS`>47?3#u7EBjr|-FY($7;+f#E~MJAQl^ zfGmyk%TtxrT%9%Fo$q{a)M0!!LYhA8Pr-MCBb_M_cH!*9vN@=;H(Es3mdH~g{=7>$ zJJ^zM45%o8-OV2Vq5_*st`db6G2;~Wsl73ALy~K4Mk7D_{OIffl)zzRT8=7KRJpM} zQZzds3V`-~dG{FK9PJirmOP72_;3WDX8_@Pc@C3mb@*oVk(OXNx1nU&qfEtAeX_*c zFWZ8MqV`GH;;qH5y0_+iNhoG3T;T$wf}Y0Fd@sWidqw9jo~T;RcohTBo<8H>(k7Cf z@_-`fJfe%Jn&*Hf-^obN)q4p4N?Y^YZad;VX0RG#aa64M1(0PXXYbtr+L6;?$k^-4 zxIn|BCb|3URUGm`fs{z2V}C;ueviG(a?4Q}XWD(B)Io#hpYGI^$deBW7dpS<7iN*1 zYb7-U@7)*9btTtNzc~!On+V2u>b_ZGS!zFP3z6S0H=KQlje85B)f2$b*M%q>o9fWR zD3e*rYs(a0Nsr`_AOnmCKqDj2pwcM%viD=D(B^ml6GD`c(-L(gtNWvjkH~M?>A+W| z%X-Q?{`oh`sny$mwgGCw%ILUY0wntntxl!2GU8IebPDk%-JdT=zI_QP-8ix_41-i3 zyjVV2OF8uXP<<5PE*wAo4_;J@)|n(XO~e@7zZM}28c+!>CXeX9`}p@J2x=%KY@ymU z?5BA1n5Rj$m%ZD9YP8@^R!r=FBxkUg`~m%b^7g3PoJS5tCh_bR0oxZzN}rKAO3w*& z2GO&vGUY;T9!6u+w`s@_6jHb+WpuhFD>6GKqF_78G@UKS6}I7cxgeej!{%#>aJ}N~ z5K&H)6MIkT+bM@Xrs){XzUf4%&aXpdihr-qmZm}x9W|L#Ho&Odd?47%;`b{ba+FLh z4}yZ+2FY&_#-SVT*< zQ?ATP_rsnf(IL61O0V`l4Z+ULCAX+AuS3HH^Ze^x6^m6!sSmzvOZf^wT_FGe7TvMlkz#{r3@FDn5686a}4NU zL;xz;XkYHb`~YBdgp3%{Z)HlQsBW_xbc%2j4iv0RoBT4+k^hj^sx$^~((8Cm=>uKr zM~is9)3$!o-s294<_auxmVnv$GlSI35YsHY;HzK=UBz{?G-+4!lc;90N@-)w>BKU2aH8N zw0sX954{CFmdSW6T-wgvoV-mPnw9?dFxXX(^g~RI=$i7zgfspdqK8nowq1SG;#3 z5R}mB0%#?$l5Ikm6yERxW$MlP%Ee^FTpWnBC#f<)sLq6Jb5Fbwe5+ohZNiD06%N?X zGkE72 zc&1h9(6nwR0Hi&R1N}_($Ec`Smwd_C&yIGgiqGy=C$^NB!pM|slMdNC9m4*HEM;{DHdC3Fw;DOO zv?@G9tV(~4{qv`P&9`%IL8-*#n$QBp9IPd|{hrn#fZ&6H=m208rYu^x zab1H}!>R#g(nsg`sKK^Mz#HGLE$Mt#-X81 zM2ZT4{9BymiQSwK7tVP(x9yCk<60ku88+;}0Z=mOI>si)f3Ls!(d)vNf!Scbq`$~b=3rY717Ds zq-ULdL#Z{K6HAu0-@|p-BQ-da;Jw`ns)A5851Ih(gn1OISrhgpDfcn+;O*X?s}o6Q zvO_jIXPI!B-ixKXUSGU+a*+a_d!BM|zwO<@tNJ1*)?h`-Pn-AWQrzHkCM1BUIXrDy z$C9I!6VL*xZN9C%*rQjDWm9ev@3sD<{$hAoaw$TSm8F2i>0oP9>%5Wq0tzDLZ9UZF z(%jz7hAn!txZYL(4)N@rSrm24&p$^KSc)j#Ljq?czP?qyJPdWzsADjhPp2z&YoUJH z;3FA5DqQavvu-pQC`{KeOO*A!>$Wo;*M*Q$`pK>gz``Fd@>f^_6`ycWK`_fNPL`jO zX~jIWK^Xvs%GdajQ+*i}f<@a)HePW1eKqd7x3RCHWX^z~k3QGM@svhfoi{fT-`{3LTr(hT!h3Gc zWb@h=2Em+s_uXpWVH?kuTSRd~ZsoL!gf7`1Cmu{ADxs?r{WdamGV}{1#<&4z@O_e0i4r@!TU)mlD~Au7NwD}32`&O0KD7N2 zdN{tqQp;1qTg`Sp3+3S`Y0WjUzQd6ow}>aK`2`j?e{>O64l_EgaKCzS!;E`6#B|DI zqm6{x&T)7SXr9UmxcKg0ZOz_vhg4sTH}6V3+ZjrVey9IjEYDdyH-g?3y~Kz;tv+2! z2_L`5OyeHW^A{UxVzmcznTWY0yeU32!zFs_NSLXL>kgD+28=yd`_64?lP0;$wkmd> z^f(HSY_x0^Wl=J@ZB1BIt-+$!YA-nWmSIujm?$>0#BS&&z5BX5vvCi(Ad9=YNg2Xc zr`zDz>MEM8lmFb^>Uc1rRP#o8*9!D}#mbF50mTENfy9!ZaZmbh>-ye9mZ9vgP*V zs&d{uSee$LU><~e+q4~*rE0In8%SwB72b5Q9FH9|YVXb)&41AWy)pHgvovk2rT4br zHC3d`?Jyu17FC3skRuO&Pzz>B^RGin_}LTo7R*1T%dT$k2<3_p;0XQ#pzL?>tKENx zTT|{P-78idd?b7)#TLLAg%F&h2gZvPS!G4>VzGSCoHz5F>&9*u5lSYiBzwcq>}@oP zSOU7wvP~i>(>Yqf)HF0U(}a6p_l`d;?H=)cy}6E}Q_Sbpb00X;6zjG5G*sC5t3Wc3cu}0tt$a zNbjw}Hsk2O2z!m0vK`5qNUKWE)>g@Ilm(vw>E=w8IJTi$HD-&E@BSBy_N%>Gu91nM zxE8+^^NCs0E8gzLng46;PNjxL+cq&~^4gkNZAO<2gcP~@3z68(ndm7X6Y+QJS9S?f z{KC%&WGE|adgItrjr*W4jAKVBXNWH_-7=F+^+`icoF5e;o&_MY@3gH26)7D(t5IwENG0FwU47vw9cvXexauZ(qK88S>C5V>;D+ zOUYug^gU9rf5J3WUDXXA_+nHa#N^9_UIM9eW6)UNvybcJI6A%L1-%KRH~UkEy-Z!l zq_kzPkV{dnoG!V8eMQmEC`C~k&`^j}yxdVavj>&Dt9sD|y`+RGAHgN|ZL(|rHJ%tm z<$|@2;1*zn#jFT$6gr03tY87c@DF}RHjwp6Q2j8DVjXJ?;SwM>Zdk6;4WAu|-tJ2s z&Y7Gv6j<6CPYwKpiP{EC2~jgVamgl^Tu8rX-a$#^_4(j^HjAiy>ND@DB9j;$;SfX6 z7Bq>HrdPLQFz8gJ6)P(XEH2?dLEZ`ZeXE89FrHYy}D52IlQh-^NXc{Ft9QX z)EZ+!g>z=@q1CF;1sbD9iZ|zRO{K{A66_ODyD80oT|30-<#eZm3hx`*&0fwTv%W8} z-@jLj#$j4snY$Tce-R*OWUhP|o|SZ`bB-pb!P47Yij2{KB}`~;&lDO&p#qUl=0pED zeRqAJx5TJ5Lx1-q!{w9|{_aF~J&gV5`QSrE7xOgbq>ory6_ypMjT`i3au-@%Ul_~Y z1-_StCGtFvlMYINC+w0aMp{c;cl@YUFu)1G03x$877oHX7wf!d?{P%TnqhS~4(>IqF^;;%m ztGyVvq;_LrD{^gnk*o#JshneY+d)(BF(#HBA;Xi`Ak7o6$WvRqeK{CVnxtg}RKp^) zR%N?DA-@NN`fjr*a*Nvg^OJ(E+tDe-h*I+HrX79AVzB-M5;XEq19G2QwbnHS^WQ~c zX^Lz`KtJsP@8*CdqfYk0Xo&A=@ttqULLS{?t}0nFC1ZJ33(9}Xtc>=c4k-)ZDJK8C z!*&Ffm#){Q&(D;BttUy{ZIg~W@Jtg}Ca$Vx)QSNiit`I5HJPc+gdghc#O(oMCGJ>`QM>Wo3FC5=06+VeYWC zD}hx~fCdn0_k51)Kx?{-jLW^=6gI`5FXH|?4fahJbOHFo_7MttLl=Fn@E2tYiiuAY z>R}_j$3aay=!eLT94P0ALjG8U6ZxoO0B0HnH2J4BoTCWV>T(q3WzSJjm)-2;` ztS+^p0^NFdB~V7%f#p7k!ohf^{5r{8*$>B@syDqY$YwK%&uUc-Vo&6&YkgSdHZP*p z_~yOF8tnm9NVm`Sdy*H>(|K!O4maq8t~k%VdS4=aa3zV?CYEEVz0I?)S)6jhC$Z%l z=n0fOTh?E0ASbHZ?z?g2>?Na2vVlZZHyR(Oio zu?uhp>EVG=>aPXb>l18*#{OiFNUQpF@%c{Or1DPN&!LN?1Wq^DwtE2dd9{aamO8IH zx=OHHvA8m}jy#&~nBj0nUHh$ADYm)m8`j$Bv(({ikNP6apX&s=Hd8Ji5ATODoW5SF z-X%1y>RO)3?6T3nd9`jr3hmwyQM+E`R8aVHw7bFw;l`ce;v$#FMw4cZvU^$Hz!J99 zsN;@>X=B~cZ8oG<8dW^HLee{e{KjLMmpoRiOVh5=hZ4|dzGH?$twm;*^cZf6y{s7? zvkxQdle3st=utr`XU{OHa^_BG*AbarDqF!9K!c~I!Gf^3$X;jZWu%CYry#Q&6o(32 z?LTX*(JiM{ogPJnkn?@=#Xj}yAMcGzV=xjH=H`BGTTSe&0s?oB-%>}X;iGhLM{p+dY@&9L9Tqq%Nf!t-??N-%+pa(wVSL}Ya|y?%~P$>VoJOh zSEM>xYie`lhB?7&6`LVT!rle_Y?TBtL2zZ$rS&bB>sg7OEe0flmH~<|7U*VmBZcrA zZ|`oql^>~d?K*GIOx`J)@!DwsqG_Ye&(na8FPO@u9y=VQ2!ttVlYP*ZsB1fO!~-qP zcoxyu1WNgts=Qhdl9%nL;A=TppCHGb@WlXrQVY|bS?zLMby!)1=U+^o74i zTCRfF4c8RkQwt>eY{scym=lKnsID<*H@l=Kjy(?KN)tPF-pN#JFc*Gg+9##p4|M_* zVn=i?{d7P7WRrb_Tbx4FOPsMH#-BFSnG(We0f2I&?$g0wS)}l&soM-2aC4~Sww`%jX**4SNX`@MwY~k= zuKrRK;(LF3y-A|I61$<@boz_g<9Ex=ETCV{`Q`_f#de!=JQNWTu{U89)&&}vbiL>q zzI$B^s1sg^NCj7ZorT|BkV!iB@gQ9zV1(3KJ9txX41JIakl~Yg4v$oBQdSy7AQwlo z-oohi>`EGGlxC@M?ty1n)d2|L0d<((3$aZ|;We0U(CxGLGf?@Pp*TV%>fGh|rutsT zHzmhqs8flQ_k2yWIEOGlan>QQ{(R_`@^>**uLn!)C5|OG{`B$gk*rTw6(eQvP5V<= zK?oA{vo%l_P>>dF#zCDCNiTH-C|Xj`lv3r*fBp(t92Fy~wvJt$5_D_STeMm$Cw1I7 z_b9H7EOTbWZYryQ9Yc2pnqGp(7(ke1zkM=#Ivq))HzxtWK>uImZA#!cNGKMI~r zJT}ehfr_VULYuwc#dn$*8s^tc%s%Qpx%zH9S3e0v7b{a8O(}l77QGXr0E8`#;f25+ z%X@b+N4Y?g!wc6XQP3`BYpOwqjK})1k7pC3R+(uG;^v)j;K(g$a#IE>#Z>xJN^8az z`rUlPm9!X$jmzY#7cPVHTO`n2?wAS#NLd+|{WG&~T_QQC&zCyF5CYYFV=|N1HLGxI zG_Kg4@D9;L>Ja@)EzI-Uf&7J3Ci>QX^1CxkWpho6BA$7lX4JFzTSU8DJF4-}{OQsX zoEpzotvvQVK^<_~tXrlQj(Q@q0P_t{8z%=*VauyYQCGKkVd*nn-bbTd+5({j`w^m$ z$iAJ9cG8C9&~n$_L0SJ~!HDP0`w(_Ps)Y7++_=EvFK3#Y$YInjMH&_Isv|cqew5aE zDABa|NHr9o--{bh^SHU^zdqo%Z#KpIW&1Xs5g^$8#Het-aP~Z4-$I0*ds@*X9IZ!{ z+WeCVHhs<&7|lwZ5N%;ca5OLBOWCZBTAUV9e|H4o+$#5KT*lgbD0XoW7gAys3B~|P zwM}zKcEi}+MWzfd+3oW z3*gyvZui~U)#v0kdRbSl;hr?6{9VBYn?si8Oqnh5-F?vWt`d(sfe~~^HxvAx*Ov7k zwbKsrgA~zf%e5M6U3>)iN*LZJLYYBHlUAqKwRsR91vLYs6;`mkV?aG$()WB<5-D1@ z)!M+OcSroDt-i+!QJr!8nTS4;HG+ly0kDRD5TiuXIC)yA3-_|^x;XI%@cTPR0?GJX z9kHA5R9e%1JMT}r0#FPumy22y5kysqJc&IHB8v9TC^L+A8hv=ZBD_%W!1mKsLh)z& zi6$9nz6C?)wwdBif_nlm@E*KsUVr>Mxox#Ls3K7}cpgTA_*$i0VCNatib}y!~}nT{J^X50Usdt=I)4MX^@n(fykb4F>aX-G9u|u~JtUv_ zQN(%l13=YJ_39+Ecp9*h4EcHrzs{9jU zUkBRPFL=#piWNoFAd1AaziTdlOmv?>{*`VoJ1I@IiuFM4mQc|pO2+gE0`3pAPT6vm z=}v3?lPsvr8imBTXhT_cXNXOj_c+9I6Yu%SuBXPoFH)xdAS~(8b26Q1GKxGI(q(G~ zNJE_O$)Q?6N4?l8O8tv} z>?2&@3fteViSp_-d&n^Fvvw#9y7b=dZgb!xVS1uFr*h)W*l6B&$*bL-!iZGRs!qxu zAg4)G+ZP((4q$dGT%>gpkveycY{WsYM)s(EZ&--@fh(3F;fhBWIS3@O5eJnG0FM0T z#S+8fUSk3rq&0XlUwp1Mgs(x8en^ny0$=#*pG)xrfe$LPdB`&?>T5Kxn?S~_$GDj% ze=w3r=YjtZz7|?Qv$#=z!HrZLW z|IXn!k4iLLre|Uu_V+ua(W!F&0#uO6c6cz@pIB&Pf@gdiR1=xAk1Jj>N&Gc!@HZx; za%P_VR)cqq?!~@DLQ1c)M?NB9_SmJs1SHyeL>KEXruYguB_S2dDAX`U9I8pM(q(up z+1~wu7+--A+-qXwxke{H#s=XTItm>*56QJV2M!Pz2LC^qk0!gG&bRQSc)^%Ph9us) z<^wQdLEyjVb}}Jg)EKd8lfcjIdI@`v1UEBbHG%(K@@2jJgQ$*_2P?riWAz!pk=UfCs5RjKr9i}IHE2fMMN*hy` z^{M<0)VXZp4Op3E{=nsBDis0r&a)GF2k~|=hCHFilE{I~m0SQ-Er2VG85yPmc|QRT z@C->vMbI8({TiWb?)UmtOIc2DfpJh4eA%mlXaCmkvVPOd{n`q|Zy)`*cd6cMd->sh z0&Pc^+q{R*+S;rKVrD~l)lNI-3Y|ay3b|?#Nw>xPy>`r>Ue5%KXDE20=wL9Iu#tWn zsNVbL{~-_jOAio3L9WDRzOZY!G9w1?^$+E7n=KLsQ;eS*KaqAA}3-;gm$E+;qga&kfuU@ln>FscZ@RhP=2Ksbz5lz;zHlsN9C&SK zXhC(bTvU-G?0=qkR11a8P@8NVoy}t>-}=Kve)LF*g~>I8XqXPKEP(y3Y_f}n?MDjh zZ!!Y${E`mj8935#IHnmk59|Pij+RwiuZCw+B8g#%ljFfWKV(+-@5uRNF9GAi|D4QH z@0wR+<7jEQx$$}I^HjjH{=B9jnGzNapLYA<6b*F(rFD~$b&kEwVw#l()E%kZNV+BA zz#Dk>*yT2akI4CKB7tg(wB{#*A{YR&BKMqsUqrB-J?-8ucSed+TgFAdj0pl~`uWKY zAuYqB1xbIfK)T8|hT;6fz&7FqL$n7IvZ=zTa&>Q9Sp_r;Lby=OyM`bWB_j=BO8+@8 zvgE^KoTi565xZ#BYq!yVzXluc*z6c<^7n8*!5r_if2g&k$DX|>u;&dFkoLoGMxTMR z0U2H3+Ss^#8#V!;!m5+CHc97Y4jEndMILB-)0(*b;YAQ}3??p_l)JmTBTvBQ^)IFMZ?3%PFj*N3s9c@Qcax3{ z-dH}eq_ckV>Tn2y795k(_G)<`?mI@e%~hBGfO|Fhu+sAImu7%xY*N2j(E_Eg`_IKT z2lWPERaqzMwymo?1b2F1gzHdxQckD>rB3i8Tks(4qa~auGYjjD=a8sO*jeNLC;*NK z(~8i~L*JBWy5c#EBK6`vBg4aG;t3(kf-e)b^0Bz5ogz3R-`IZAw>&Q~t&%;p){3fo zs>@Dv3-4>BUy=$irOh=cF6!V);b4rFyu1voA3S3Pn_wA0iS0P<%({6&La`zLj;NRn z_SSrp!RZm4xi#=^97xH%tY3okZ#hOoKLU6w8a#WZDbh|ufV9T)S7mu}v;!N|c3Yjn zAmy>k@VVC;^Fn)+iLnqyQ*YR3-mwdcQIYCt+M{_&B$_3TY6Ylbz(cgWW7fe-B?((9 z?$&s}&FOn6HV0`wP*?NvkSxWu4ENvI6HCA!^bTdNE-y?u0X5%?Hqv9N7RtR{Kg&N}Id z=c3Hnw^uA-Fr1Nk&-mrO6vi>92~gj-#P5t^s5(K*t4@HvHqIKq)+M#dx<$-ZdPc2X z6$w&ge3(#!dlx}iLO>XWpy}o?p=Baz+P@hNsM=z12OSC{W@wOBmb15S-HHMV-BZ?M zwe`P1anl&68*u{mz{$@*O5-0N@rQ~HLTiB5f=c5KNUF6did()lsD~nk{%7rp&jC!@v4+Ok50Y#AAX|<0Fzb_6}IRv_mvWyQp z5~0g)66Ee6hB`4ixIbwQ#R^TEabmCY*l|vx9elNMVo#ELnMt>LLt|>7+)*R&XuGb+ z%LnjwlB732>qdB1%`pLco|^6Z2wWZcA4~tV1<}&Ve{`eXW##5$y%*)?#YSWJHft4pmiuev8=QckVRChOBl}{*W;RAn zKsK33>I0^t<)1c{J$C)&>87h`G@H`tkSx12;7rlQodi&sdR$!~U_0Myj^e>KBMrdQ zCO~o)0^%Pl@qv5SI7TWW9Nl{77Y{9bqs)n^#yJg3c%0y&t?6M1U%kgw>W|+9_>-xz zF_8{)sp2J(PUA}z__5u{Mir|?p8y1a;xnB~;FnQq3fHdF3oF#WASOpCAL!jWD0etB{D@ zd`WBXl);-01;FlN0Go959#MBsVa29Y=z~e&lnio}H`YC)@yBu(qrBsX?tkgJ{#WSE zi`q-=A#g_nBrO5VNSPpF)=R-=c#MKCIVfV^zW+q5)@{f3Z604I1iE>IYi?4uIr~WR zhm;!H`_1u~VXvd^KGB?>Hs~s(!e$N7l0aLcH0;68*YtSbD&D;)k1TNbap#9NsUVuV zpOUcXeC6p@G7tTByezqH^S7>;kI8dEpD7i$g14752`Z`aKJ@ z18R`9^W`UlD>mD~;T@@4rUA~Eg6=7|$uG--Nx9T9>(4@$dn1;b^v3mT#=nX`=LqUs z_xdKQF}*#7MJqu)_e(#83~Woj*y+_ZtrqiBCR|+kPkYai%z^mH}vX`S4vx z$m3|zYz6S@pNGzv(SlKdKYl#BR52j}mE{{*=?Y89` zoPl$QenXE)sj(b`+l=xv>Ry7^RnctCC^drjit&FVweHNrKVr^Vb3jXRoJ+LZCcBYY zt_6||XU1HF?UwO|))aPH#tU#9Q>> zCIWVJXwG%H3#Izd&wHO_CI5DvMKx_5p!6*_VkGG`X3$8m5`iqc%-(*SgY5if-q3e@ zFfnpeu2@QXXJ48pZEF7;ximv)r<07-)c;^!Aw7^k+<_>V5|*%d$LN~p+?Q|#v$NK4 zNjZNgeDmFh@m=LdYak;+Kdj}Zti<)Dw_S@(M%}6Dr2a=2HnE{c-_g_aWf>~1V@v`> zO2g3Tb*$Z|u^3%D;E^Ia>f?`uZss;0D$$Ei4KDv012mJZEZ|G#J4>gLcsN@JoZ|G} z&_I`vH@y)|kA6v!lxyATC2=C@QPUMiH|B41&$Z&F^Yhw$J@PwAy`lz?;PYPz>}z?6 zy_|_%FK>Kq4@otQJ8-X9yON_%`Rrg?xLXklDvHu!;>`_&2WANi{n}kon6h`V(;FefkNCq=vj}y7Q7D9?QyoA zv~<*NX@Gd!xV+0*qsiB&SRk*ZGc|$TmU{sK)XE}lXKRrSpay}sfa`czIZ$lSdooMs zx!Rd#y7S&LqX&cpEiV_-6p_zw>}o3te#kn^ zm6Ru3&J-5~`YYiZT&A6%f0Mc2s+R>di8$t9KMyOUU7Jw1w)tpNS8NkhC{p<+C|__! zg&7yhj`7({jj(MJk$Jjs`g*eg#lDgr|Is2nojK1=JF>D`gcF2i>nv$y^qj@9*mK9f1H{ueBWh)s38T|{_$`j&J@(gv zO!qBJ(|-%aq<+ASQ@q+SDy#H5Xi0{S;8QX=eRqTU{yA;x7pWqBEtFh5?OkXz^X?N+ zwxr32$T?>2Y)@EmH+W`&Hd7C|-C;!0UAItP74Cn$zYVSQ(aWb$7rR0)gnal_l1S+_ zUj`L9?bs*@%7+QN{7T&eP3J9=FC6`&_h3a^Pir&dyh!=BEe!{f8uY7rKGr}xn+MG| z_BxFeI{|*cWFhTtro@@+!|9^6FZ>=u+4YxB-L%qnjm9o945{AFQlOu72~VwJlPkuv z_9ez)-A)vn8(MMr)Ab%f(JWfZzQ?{&<+q=C_9ua^pR>;ahoD$?i$+*Hi}K&#O&1N6 z)k{gyUB;rST11PS4xtlZ&k=cDs20BkBT)v_H0OEPAJ{qy{x@aLruC%?FRPvcwYn{W z*G_U{skh?TI;H}AB>OwZ2{oo9-@owyZ?IavlP)DE#}#lazJbeAHu z0zl?d<7^1&Cnei;Ycij25(8fs;6+}N(i+-Kh=pBGpQ9TTMj=XnhM=t8{@U`$=kzF! zA;oiwmn~3hF?TrN?sLd${YO`n$zdW-!jcC{H86x;hyq!K*PgxDXefauZ?FF$< zNVvq#fYPH;3h|m(#Yph0dEdhq|m29;us=bE_KlMIvPCck?3RS(+Lt-Z} zzRl0F6Rb&vYjCNRa%!9>r(dtuXHb2A$KwG=+{bTK$5v{WIGU6v2gn@FN2m&F9S6uy~HX)fBGz$zsaWs9?KkB_vt~pk}oDwx&teo^IW~ zjmn8aw;<9mw*M>6z#%!^AhO}mw0IMZhkHLH7SnVx1h^k`Xtz<&`$>+!9ZxU>RMpr; z(}co!D9buwv+Pz)=pag*v`TiTzEdM6o=10iM#rgk2kz72Y@cy)1`6XLl!W*farZxI z?X7khnP!Y~b2VJX?$VdO3P^qvyK9P>5GyE$BT4alw{rbvBN@G3Ia@}y3m<1$>rBv{ zC-U+9UAC5^Ko8BJmIJt-4gt*2cFuDz3ib5o(8w8Fuo95?+A`+qng*J){&|m6WB1vU zf>`|@Ox_JgeL+}3!#iTQfo|42i;V2Kv7>9JCE!YiEyt0H-Iw(360~AGZ_l_;kj(Gv znNBL}p>2&vK%MZr_4iqO47}Iqov%`aZ?1fPYO%Bn5qL6n(BRpl-MsrSnv2ZXHxx=m z_|7-pC6 zLFhFx5APPD-oWe}Z#e6eVAVd=C1DUySvWs&avbt*s`8ocGlx<~k>Es?Ix*C!N4&*a z*(aS-hmPiLI8RGlk~)bfToAen){{&hHsq|pajFHFZARQIbbIfkm3QPyh$r+6oc(KiKz-3=0yK?l>x=xP2in6goz^0 zg+I2|*T6E@MsHSMJ)WnG#YXil^@aaz!jc~E6;NDz*Y03&aE!J(XU&UP{w58*hwm(w2Iu=+Hq!sW4`%xMBN=`# z$MQAUl|o=9L;jYv%W9@I!zl*AQO;V9ba^5>3cOJCD?Izp&09c6oAKf{r#~PocGXvS zZ7scbt?HzKG2hg>yKx6in#Mjz$1s?U!JtWB&+8HFNnw_2q4v?_VQS)dEMmy-Ybz8O zCY2&o120qB_Z{Dz6FL)UTuLF;ildj6a!9o?=xu$+hp7EbRHt3VtjQZR%^$W| z9esb=QBSbZ%wU?>pkeEeic_-%8j0MII7L{u5q&}&uo_jG9pB{u789FcqM0N0`8`GS zdCwGFGsU)on4d-lDBS$o++_nz8~;r5q4OCe&pH^ed;2}GWu6YrkH9|cX8nF;2hV63 zXiKoh)0xI3`oA2v9N&Y7?-(EV)4X{|aCiwC>q^B{Rs*jBzE$Zre2m>?2$gEAW(vx` zll=8?yg-Z7>*ztk=^FRhfR+WF%)K~EwLB?S!rQ9z-XA}!cT@!N)WCV6ji}rv+<_CT zRhGf5I$aYW$;oRJ(WmJ|iRAF2y!n;MKiqLKR9wGN<;StNU9cYw8k$kq*BtNU8snhINNw^H zBX@GDEQ}9wTJk8bWaiO}D4(Fcu&WP-4X17%>*s%4tvh97snW3^W4Gp=>u zS)i%SZAC9Iwygb(WtDSlPV88qvCL_FG;7d>I{s{#*{%r6MU~Tb{!}(WUod&27us7r zvGWLnuh!!bb=SFi9Bz>Xl-%sNAGlkwqYsXifJMtBk{l6cjOz9YM7%@^ITOE->78`qnzF0IBHMrAi{ zr$N=Gs0lQ*295cQ6^kNlsJMuwuT$BIqL)%YXqHCwoL>$IEwD^A0f@_VM_B8JX>n4$ zO<~g`>f}Nf0JtUN7h;XIL09KoIHFtZ<<#!9UPg zx6b`Re36_-C!aXMg-?G(_T_rS@T$?Yxb&LtIWHW2h@}ZXK&w*CH*II9?~x%y$$Syu z?@S%f#;%{M+_p)UMp(p+V!pdb$BSYPWSi4_f~|6uXtqtD*5dqL>)$TkYd<~_rsVBg zPM0io6hDlk57FqTyCdMcIOS1<`^o#LV&_vmOU~c^gWp}W%LmhrAz|;O{t*c}9ean{MrE_gB0hLG$K@^PAyzK|9iRv#nk6(LD>cQZHfaV*-Pw$$mvCBYc;~zDYoI9E z=K;ibLzL`1!(R+|6!L|60J#%^I-v>n)>L^irkMba6^i-gJk*m=X7=Jn=p}E@=VYE@F5^xrc{0AnZB@PQt%NoG@Fi2cJl&eo zA$S)<)}DL}Ehy?M*@!wUW{wmkRcEO{MRoRQyxvcaB}jQZgK>jB7fU3wvalnWG}HbU zb8i_{Rn-2A!WNVk1Oo+Wkd~GP0qItdMx|RCi3K7cAl+;_rG-tmDAFL^A>BwfoVmgG zf8O);jC=36Ul=-e&o!U9p7@1GX`CbIR*879e^>vPyG@{TnukOu4zm+fZ^8KVWc`iyI!oc&Zq*S|J`JS*fRtcz7Fh zk}9JywaOv4s>y;lFHW&g*5;V)M9N_yy_4|C%CFKCMH0r_Yd`jVlCsNd>QnmNdE&oF zU$E5gO-@^;2>qKdeby6+$?S<`OJC<^q*@kV{wbH>1jr7YqZss8LxuO)26;|-^SRpe zo8BXE2J|E57`Y0GVToZB^5I)w45}@{A-xx>+`~CK9YTD9N1jOYNcN-j5KE=s!j~42 zKtxT#Tr6>{tczW{YQ8lr-RSt6utYx#5S{)0IDIAIN0W>=zYv8rfFY8{b=evi%fCni z+)Wt-zg3KBzwrozee%t>%}mvL-cVosZMSKU^H;U>U*9Dq4{SMzSY~L1sC7VMiH{?V zlU?|7Sf$UezOyK`QiYYyI2_;;+YvoNYj!>5*3&EiBp!a}4~^lhYodE$3!HGD@8q-y zNE=L%bhBZgl{R*h^3MY$E0y9qkld}kAe@iCk^^g>U=M9YHK=Vy-qpg3h5YK)UV>_E zK3FS2w&i4jT5EA{DiQh#K==CS+8xQs06-4FpKoEV(IXpdF_*UQP4z=P{$zHTkP5kp zM;E<*<8&N;-tsE@6j3q9U#On|Cd%!@)zdaf4sOr>pY zXGzn(>RXe?>+T`7dmT{ihAUGxg81KnC0t2F_!4xBAxWdoSPC$h`Bvm!H<7%^PtSZ< zJEb~SYPn?2m9V6QD$o7RYLbs$wLIT|LxZ0lplQ6)*LYgHSAb2thTVKq*?RGI8L#S; zfJ%n482x!;UCG7o??-sX5Cc;5GR_J}d}nTqt;-g5{SMeDIq?|=YSrswo5iFN@fGGG zJy6T#VNPOOnQPDH8qCw)H>&Vek*tsWxm`RkAOK#z8!F;?YW}P7FT8lb-HMpsqgLl2 z&olyfqIJfVD|F$_=Kyf^l+_SQ!N^c@$QOaFl>vj%a^TmzoB5mGwE#YQ^mh5|IMV!G ziHHrs)virOd&p^`j~D~pGI_^1=!eERrQxW{(^PR5C`u}N zbg4Hw-E;-jP;?+Pr2thvBrb+6_PZQBw9F zasMPnDcu}%mTyy`nd>B)U#a$=yr(rysKvVUpTp7`hLjD!Md{n7$Gy3exjYXKqmmr! zx$ES#!N3d28&P1vFbSm0VNd$VglR`*iDF1-d12^Y15a#hTNFn5j1nx^LR9&kTn2yI zYapc9B3@F}T7DvW+98|GCd<9{={5yQD`UfevyzmV#mlGVIlE$s7SY5yEasgf#l?>A zf8X-Xv^=euui5hZ^+8T%^JrmsdcOV^-{`2(2V+xtUh7igZcs-OkK#o(_2@#*0rfbCkf#w%}{zf|zpB&Hxd zaR_bEnv(mgsDJt3_sL806z^Q*y-#ZgjEA@pieUJiDegb(2+zJ8$bHps^;p;=ZP}QU zc`#4Cq6hOl1hiWChSDR7O5Gr!I1YTO`^)GQ;;9dXk3t2`THhTYzCH$kX)Dv|!UDmw zA0fg7q&RiC`Xm}r9viJJrb~Ef1B$^r=XKlsk-Z2SH-%D1_0SBt52I6EkMZ9 z=qK8R$;(NX3U}pOnSZf$F6QbEZ;i^mIGnij1TL;m#`J>N zzApYq1hMMMm0s3OubPKc?GN!8nSPsi95NhKT^bYJK`R!XRXyYvBvh((c^bkZ>FA9a z-%B)hKIJjISy^YQDxJ(S3Eiry%YB)X=ANzLD0K9fir4BQ%>FK}!9(FUX*lQGl5KVc zmH-P}K73$U;HqAS>Czp}Yxk1->y|HG8OQlV5ma!)qo?6-_y%Igb!C=jTxdi1AHDMK z@>E^TJdYkN?jvrw`m>!5wA3^6=s}C|@`40z4sCl(U|bMDv+Ra^sxPMGmnZVs`b$@{kj+gq!FbQ08+*xhJEW87!pSW*8slH_cE;{w(l!(U9bIE42Sl^BqhAvwIy2kFWBP}Kxcnf zIhmHW1|cl__H?tSTqnl`)>Lziw{vDTn^+5(zb% zv!>lB-V|s`X(F2NXe!ZO!#k=5URGjlC6)o!xI_8{u!0q3z7t zi(l~a?kdDZ3Bl5V{GH3}7>$tf-5(*XV9dF&>AF=h%naP@ksu~+>ru)sInDCJDR-@h z+*WT9^f@6n;dR?5m*JI$FRwE;3>XCTRAPX+md-;SD<(z3{m6u;-wPiS5$TOu3n5JY z_RKa1W`aRRU(xg+`SeluJAm^96RB1FqhY2MF3V#_V&mwe6W2*y{2%h$=HR2ThEJ3hz3hC=D|3jBjHNe=3O=V!4bO*(QZ_>hl%1crvXo zD>j^EBF5oJ-5#1eY0@Gu!9rV5Ve9~jrRu3M<1QGd^KX%r3o}I|p|P|cQ4oh8ExWVO zW!&<is1|Oi$hr zFw@k^f0}0^zHETMA8f8~#3#R@#6CqA#BwqJ<2!ORi}bO1R8VW$zxZ{AtSex52yWfl zh`Kel&=g*5s;ig#(I1C|ZJo|ksi9tn@zt$F$iDH{-Lv!?oYU^7Wdk9GZOp?ynl_K97lVk(={@;onroI@`lIGQqTP;mGOSw_;!oGDWbMpf=-eJ zVhv`4W*}5@=3A3~;Bl{fslK()KX09>_8M4_JL z{I4KC?m#ky%8w`0ycr*o$Rf)ie5rF-7%k(nxtNjkd5mvc-gI}ki}~TtM`x-65S~l9 zJXhR_g5+_rE8~h%(H98+EaGH~iy6lamDKuo!xHGv{%P44T0|K2<=F0aDZ|Z&{#qWh z7LXUu@o$Wkhv`9&X`>jY4O;x{!^0Vm=px!?-KOgfTRoko4xYVG?m>$Qxj5&^Q9o2W zCEf)YdkH|w0oM@{TIV{D7|dsr`N;reTiVHL&wc-m8sTy1xz6tJ?hgqf>to33%IUc~E6mvLgWu+9YK zdCd?HSJul0UfN5uFd9TKg}%9Ez}=5}nmGEs%d*DPySD{5FX8~FOh z1Gg$lJQ6wKlcWhP_JO4t=$HM zHdcEgB?K)V6!NYldRYl0JqpKy0B^J7LYzNG%N%EJ^cn)BJ^E?l{*Oo{JwrmSBu&|` z$9^@=#cvP7G{>cCt|!M8B2f&l#JxR{Rxe_Hcu^o++dg3urg3R{dr?wS&NtuJD<%7z zzA=w`bv%n;OlZ_(w4kH_^hAd8LfV}-A3avpo=~17A!XmFjoc*nIaMJLxDFF z&1tFKGsLAWrd89rwEqcK(_w%5^hpXySp{l~QKCi;e!%YmewQ?Q#IHjlq<3K*WO*Jd zV`X_j0=v5`5UHR|PL2+6Toi%Rq)*g4e@@2Is2|&MFH6Q8w@30jbxzhgrGPXJuXZIT zwFBs7+zoonq@Q*Za`0#T2Z+R=4)MhMh~(=Xyx*CZ=2w+P9IxnqHhKk|TP;KkspTGX z6(YYsTC_;u!$0hQc_Ml@b_PE+ypmmr`K(EkIm-M7Flb&^gb)1}KHbyv7LcYm0pphq zF^(?N#R8;E=pchtC_wZb3%E=|KQc#EvLk35VScTJdi zWuxS_4|+WZXaLk3K%qq^AIi5l4=}w?F*gI-z zp9D_jc-r7}A=9hblre8GV08EV0Tz#UyD(cR`4Qk+GR81Vr{>vd#E8ORbQsUjpMOMX z?O`DS{74c8QuzDJsT}Jyr{v>HfTSm{E4If5k3YdAM!wOUP`~UIlZ6~voJ(0VOQRo*66P6*^3}ZCXDzNe-Bd%NoMJLBwG=&4PKmplq2WZlDF0-pkd?u5}fA#O^13mEba z36wX`v?`Ww!ycm}C4qs%%T8rT5C$6D)5EdhkME<81zLMoBDsy&s6xBWUOy>BsZx?% z>?guMdJ>`ml)bU=T()$?FU-lz5WM>sxb(PX!ds^OJA>6fAR>}p_j4zm7Ms3o#jVd5 zH?`>cV_9Dx`O=JRGMtM>^v2U+*?d8f_2gYt>sP*7LwzE!h7YzF+^ zLS7Fn@(0Kj{8b~9j9fu15-PBQ@2t2;C9d8I;{|dFw+s8|u9mO`dFf)cFt08h8}icqzyGn&M`d!$r;Y(aKw{@FcEYpw&jBLr{uybe-@gb!=4NRABH#c; zb}>c!9#vm+b@9Cb?xhU?#|Uk)TK+rsMX)gEyA!o!VptNNt1p%K6MzL{`+$^Vp{E7? zmYejizv&0)Bx7H9Duj^l50cv^v^@@it0Js14mb}j#bDoOd4|~C`!8~u7Ybo3P-*lm zi(RZn>(gdy-uE>|E+jT!5;A#(Hkz>%T0?c}tT|0t!V{Ta2->5;FN1g6^0PoRT2M?22_~k`zK=wFG zKnzr2?Ss-@3`$B$Wsp7WDmZeG!Li@nZ+#V$DM;y-CstI6R}L6BU`(A0mR_N5ckj`4 zR6ap67z4(o=&qtNfdO29)h&VZ7uQjVg^bEs?mo;I0${>`fd!&+fM2oUUsOH^Yw&;h z!FS02eem+7g7}LUCk-k{o)wWH;CPGq-R=PBz4 zJW7tVmM`(nPR6Bjbn}8sO{YbHZ~F2jc7}csGT{T#qisO*M&;XG|C&9}-!m95-?07D z2 zSlI~h(es(=)sl%La|R%!jZl4CXwGEJlM(M^I9a@7922M za9Ktx-$$$ifKk(rraRh_W_)h|xrF^4v?!-h;h4BS-XNT73fQKlj!sVNPk3z&LdG^P z(uN<^8LsRYZo<=ajoZQ{+9O#k$?h^l#^eHK#m4SoV3$^T>&$K8aB$9a_(#xL?wj^! zMZJ{>YzO_QNY||@t>@1_FYQ+F|5Kp(pI-w~6>X@+T=BUW^gL54j8cVNKn^%?(gS2H zUOrym7}0%<^7ja$TbZc;#}WXqJ`j?v){#@{L@q)qPxcOs|HDE9vj75zrj+kwGIbvu z>ab>xOh;@q5RR4uRluGiEH zPC(Zc@UeIPv?PCy_t;-pQYn{dJ=ZOdmbj-B;*Ya+*Bl;p6L;af%91V^1VWEY)l~} zg&seXIy3HywgyeZU30%`^8X{FA(!zIqhtN7*xjl8xCN{jzA#MroL{#n_cB_y#6IWC zqV1oD9W4%%;#6K|cVQX5*`~-`3B{{3(CS@>6B50G3 zM7blkh=m5C9rRud4<@R1)~r<*GLAcTj}KVeGm>dz?|L><)o#Gh-8w%(Gaac1I@NB{ zjvF|kL2zz=BL~fIHmMdlKd5{>%P1Y63K(iyYt4Wj5 zdqaW-lKoX$ydKpLWXEV>1Yp%ps~k7tnY8M)qc7?@qi>P%nw1hUs=bWowjzn>unE3} zRr#Yf?pD3!1d?+8pAFrnpub`K!(e&~k_w{@%Bb|GYE`Yv1*%Mxqm#F8D7*&8*PS8$ zi9yaE-AO>|JFiGm22ezPGRnt8HZ9M%-7e1`^fpF=TLv5@`iAY=5OWKQhcCgAY2WFHv>wh2I>so>E_$x zRLx!%v5@jPM!(8a4~*t>E!;pLV_@c;^FO~x4lhqnlsGQ)Xp50=q6#ZAuwB=@P;yuCx8gX}>;%{DRH<;W}IpgaRVqff_Yt}r9 z*H2jtFBAVM%^co-L@YUr`Sdi_`2ad-XKk41l7nU6W0u_CjnqpBUR>^dmnOGF?D#b*Oo}&j2DHyK|O6 zcfFOwsgYz@m*rHy3?RS<5?-=6;PnkCPglc0suzHBKn7`p3XP_y?jSxvh!im$NT3sA z31014gFR8{A`5I`NmsdxrYmW%htOqu!mtqsQV( z_Ifjz*Fsht=ycsul??y0n?>x=I73f?uSO(DY2ZHl@`kOU6zo8aBF;QlNG{=-^2z=h z-Cc&4PXimbD#p|Ld9G4RX$k$yre+{0KJUyD=lroNy&-~F{>(rvkGsU-Ofk){$pqsQ z5oiiZ1&KQ+30n)9TB;?P{_Wv`rakBiw93BNG@lPsNse+3#$#p`YeY>KO^+)Ft95A0 z^XM&Z;NnEuWx2f;BE0*R@zh;Di-L@dEPKhEHxGE+$Y>Uo?o<*hj%1BmSxwYZ+nF%HVIK5bxBhMBS%m|X2L=Z^wH(IW z%A*VOUbk|8K8xsz)bl7i%)9Ser8R6bj8l{9RFtIBBrd%gs|wf8k1Zc7GaK`yVn*j) zgT4k8A{%iIz$| zfB!ha)Ifg6tSGPzeHzHu==XT>8)ZynH|`B+^3HB;S<@+UK9izT;?)0qH6p{WY78jw zo8R*x&%TvdWB3HU$g+;E?M4-XGVypE0zS>WbK1B7HEW*2KXtgWqi zjdD5jb!ybI6cWu4x@+}7fw%o|8e%OzuBSbd8bKacKZ_bSi7&!*X9l7##mVw+c`yh$ zkcH1OpJ&Xx3z1qaHKh^^N!(<`hjvYF9aczx<6rN~iRgL&>V4wlH)m)o7dx`4g>}X~ zx^Y5wS(90(D%GLq#(ne23v*uH7RO(e665Y`PS^jy?vdTnE&4QRsAn$H{rk-in!w|4 zM9L_?fxAkef@j_S;-y83yq`j%SQHi9x-Tqd3q z59q++w0bU(G()}TaW7tbFDA3)UHMN=fHJtg+j@~sqX^O?f>8>VEu?H@C)&Vs-uJVg z@^vZ{9_Qm7qxO|}&BqEKCax?|EzavSwF%2)^fxFNJLGm z|1rSknm14aOnpx;LH@YT6QftDN7P3O%& z7@@)F)CP9q!qCPz#euI!_2rSvvDqTu=~orX5Kf-&B0+ig?r!0@CEaFjW<;A+CpXM> z9gQlA_l)_*%)NV$Vo6Q2{BJ}dt3$QZQ6J_sE5P&q+>?(hCGUVv}?_#P+*`? zDbb4_-JvK@^f!nS;LMB^?6p<3q(#c6Q>z8Io(j4 zB53936P>1tu_u5TswwD*r&ahY);#*?`5eB31@1$IB0(3{ZJPlIgPu+e+c%e}**EGe z@4-pV?EKv`+9H($I86;?K0(LQNvp3U7~6geAWJ#wze!77aLF;D(=WIe#KtgBPIL!g zYw9h@?e;Tcha(1Zio=E$z}FXCtI1EQT%LkKpO$V%kbLBLnZTgayI(S-5qCw@zhrU|_&n_7Fh3yag^0db*YB5OHqn#4 zDW}k(7%G-kuUTaTYIl8$ZFKU#lKU3D$7zn81{ogVXNrmg?#pM}68d$K+hn{JJa<{N zpR5-;Lpq($_~Q$6Wj~j@9wyCn5HV{~AMq+)S4lP)XIs!E;=6EsrZo^^xYIlTdG7Uj z9FMgwF~4K}0Q6N^O!vbI4U+99L8npb%XdXJCV%T?ubb0c@#6jIGnb2i&SR&FqavKp zja!t8&9y8uvCDH2ZH$?^&Cj_xloEH)<#U!@y)X&%!U{n4tK~&Vw;4G(S@;BHp-IoyNOc3#<=U?!B3*A2+)qVU1+{scE3DQA zpfWh_1sFQi2)xcKKkn3=?1r1tgEGo^q$)?Z=pZbo;hd7V>$qFfVzDe?pEzdD&Jp)N z%p!jzT)TACW6x>B6ZOV?-QiSVxF`p@>7u#LKsj=7F;bk4doy5191mHqhI@Ke94FQ6 zFivjFjf^>zxc*|oOyGy~3KNC5?CpoU4%cfu5{<}++jTa*l&5F=_3qjM18^?$V8M&Ji>=tk zc)I74e|CEW=@P5$BS22sVJzm`PMCG}8sGw+lg_OMCetc}Lq!b_2?RvvpDA(-2~1x| zz*SBdRjcLHb$l$V$7EE6hB+fRfB4}Q=Ma;&9C4eQ^>eMQKGR9fickPl4g2-rz3i;l zLLO(I1hYiEU}29Rk@4Hb^U&ug~0$V1UaW$V72BaKZ5d9zSUMJHjBQ*ZTaw(d7Xz z8gEcTyGtv9BLaamctdaKA!Lp48tK*jkORT@Jj#mB$_)I^xE$~_^s>fwmx^m9eDWd-6Mk7=VjcPlkeew8;`VQr6BZp06k)` z3-6ZXvKo-$10~$)ZIaM7VZ|^7+L}_tJ-7?AvHP!uqXZrAjq426Ijs@!qRhPgfL+0& z%HNP5QV7y+_Q1uwS9!GHKnw@&R4?4z-AQYXk#}-xY%hT<>JlNdy)9#oa}$?u-m(|b zT3NLSgyTvp!KlCq|0&e^=8hwg^{940+}2EgoDUJ_;XQrnH+Nhhguyma2kQXjAj(-k`g;*JX@BkTTC)V~|$0q9PB|8D|yqOgkja!Ly^3b5cpOkDvx#VAc zO_8VVq0oUYzKFQc5{ph$8)Ow>e6u^8*7}km-KZ$Wn~p`?{>eD#%lS7ta1eahTQ0V_ zjf>M;va%sJ1kZ$6K^4sXoh{zTeQV*5e{fU*X_5OCrY;|J_6iI)(#JV5mVvBZc{Ug(vsOddSIZM> zfTj`$Rot~25P3|xpD9o_%bLTR(lB4$@HTRezsw~t|HLLm&gEMY)A~-yI#=8$tJsnb z&O>BHap!}cgT$aX2d2Xa+NIn6zLe=$MsdC_;+K)*dpWEscJ0K{9KB1nY`=Th%7k1t zdMRyaNC$k?;wO*)<>uC>PIgY~i>-rNzqKJQnirG``}wyx z*+s=0F1TOe#R=iXTJT9=`RNAT?`;l=V?vkCB<1=pMKPWv=xVqNIjOw!o7i-|5LswH zxRF2S@KX3TytQjnP?wk-}p$-Lo03O1ZBJ7)XB@`1^Ob zV0YbFj}V5SPhwBh@=a{+{Q;mfN#`$CUrfnH<+4a6`)&0Ug?h)f*^i7{kK<>87X6bo zh>NfXfSV!HEj$vOiW#X6%Z(a-qiGW~lohU0oDonp-NpSqTElZzJK)A)Z9m!RVB%jC zpG9psa&HjE+vQy4IQgNMx2RQ9ZmEoNP4%#O@F)$c#yhOpcO9RS{ZC+cGBOUUf>XJfkxz65x?jX|TUO81b2?LJ$v5EJ50P+9I5aJO%xQ}s%m{WYb@w`5 z?+?$E;p3J#;xIVbXlxXTfqAkUcayx)^DkyI4kk%usp*WAC*E5zPA&IwV{@LNW4VQ5 znmBI7dKem!tuWdU3UJilj*gC?PwT-;*JtZUxL$QyEoh~3W9!RF$H%jp+J7|ky|g$s znPn$TAVKV;7HVHc9#1-slf>F}NT}m2JwOXD^Nbd*GE*C8!uWh_ykmgdIFo6U#1zhN z{sL3LQQ_dRAe}MGO^Mu+tDMO@Sxu^}&n2@)@2*RV!IV_b8#)Mv@#+5WPM+##VETV= z#~#l8hn8>7c8c;9wH+N_Nv+F{l;<(e#R`XU)9t;@=-3105vGiGQ<_Gla3W68`D$`mj{Vw!vcPvS=POSxGf`g_?%m%p==z<0Sb&{f zx|KAkhgYR#)Kw2fI7oH4@M0@rIXsWO9~}Nhxyz;5UYD_RA?>_xa9g70ox*jqy-^;$ z^piR;U7z1>*?JlDdBt*dmKzu4;ouV2%HO|KY#YA20xxIA_RVc~eqxRuwNOg0HGd?z zef}_1M>xPMH$F7ywA~@u?37t5^U!~*iLYE1VYkzA^^a+?< zfWlVf1@d3ft>LfY1$f^l(<+T|F_H+0XniA#(oHAk8Si{XKx^4z%+lC>qTZd+Sv}<@t{}---%3Pigo3C# zRWc;tG$zgDq+PW}qKC?hG;fKiKIG)ux?$n?RZ-|f36yvI(dmo!SH+wY(a2jF2{YFj zuUl^&Tu)pww5x1Oei_h`Y&FQ$*4j1a3J)vbB}(^{**(MZGGO>Sonydfz*XLbiuZZ zZw_mA#x%%IB;ukES4VOXxUdtq)~CIG-HE_KT{9PMs~HlH`L^pGv9kHpg!+r%hs>I* zj0Py7-Mn2K3zKEXaqrT`?X~$Xxr`=m)9SnHeswAGrkyc4_r)!j)g%10nNP|NaCYbN z()PGYd;qb7nZn0hVQu=LYNV0Uc&Vy-@{L)@ZMLF?)#GhJA@!E;@2>-e+Q|z{oLhl+ zs63Z{ZLuzz&LA3#xmJT&)W8Jvtg8R%h4`Ik$x_1$B^W=m8}|ca4EkHG4K6=Q%<`CP zy6Fbg6N-RR3r!@ro5mixGY=Bi46&B(gVF<50oE#8i7nakGTM*F`#e)PJbwtxb}0G0 z1Ae^Q|1kz`Se7BxeGDaoYLxU6<_NREvuCcK&lQ0iK=U|1B0(4u1&tJYYe|@3Ki__J zxE8!H5_=RmGk7Hz$7)<4^8I&KK-(r7hr8HGpv*Bd6YbYH?@W+)n7Kx%v}!lDJnp=!%IF5bIa)m?f0YVb!E3`-z5I zj~4abxXQj4!>q7fwVNPtnJhENF?sPu$0jSJ8M*)}ym%3hSsSPm`F&XLuZ`#D;G&En zAg3aW$#VHkMy(+{XhHb06{J$Y(4;iW&DHzCq)LAcP_dDcyZ;?1<0jTGb=MHS&ah+9 z)D^PmM=7}2!VQh1J(5i3*KFl23LPgsC0sRDA=7)#YeRb7e3%8eD(ha6mCA(iYJ-n) zrdQYR4|?||Rw#ns$=|8%4)n*Vgk}_s*(1@Wa~~R^s{#3wdBDSpxX)i2GKAp(%Qh}j zx5SqLsQipmpxToeaF;qydUzoghu3MdcHqNTh0>FtJZ~-$Isk^T{gqMfktyk9db-Z> ze$c^2_;_a#fhyvMN8^U_TbF}=(FDCI5_Gyb{G{thtC?cwUaLbMs|=OFi8W(`mc(+j zzJGrBwu;>rT5iuFx$?o8NreUPz~Tj2%DZ|c10qe|j^Dk;@Bd5{`K~RUjblqPbx(&P z)C7jfW*zpMN4-*s;@JMT$fKu(_}{c(j<=8_C;5BH)1aPuqxU5gK$F|vh4`}gDF2Ma z&jTtRki}T-r~zvHeG(K-*8^==?2w^6I#9j@^lq5=(lYQ%0?$m8KCoql@hZ1M~E%8K!fz zKo86*-*^72Y7wDt)MgP;Uj6f=+J12@NA@^KAxXegH7exdgpppaUb__%a}K^9Z$9&) zln5BHjA!62w*=Zu<$-$|wy`O*NAM@_U%u97p)KEfutayuiyxY@9%p5X_~A_a?hJD| zK<#|jrs6}8?ESt5I(PHPP{;jaCofrjl20O>XtUrfdno4LFUD143$S<_VhAGh#pJB6 za|`a|oc=67Y?MAIkmu0S=!WNFcQT#(>0Wf6dQp2*jVK=*{pH;qUry(xj-q<*bLME> zqP<8e$&mPF8?6nBYF3N>21W?(NJn~(-BS^v{^0<_RVuAsVRcUSB?-&FAB2N$FIrVm zQIT_Gqg1F!59x#}xJ&n%jh<;@?QAuDW{3tvGrQuKbvg@l7am-#x`*EQ8r2$t^ zwl*v|R&=qD!T3D@{>r4+lvHn>yg#ewn}(-H?BDdn z-%!B1TjKo5-*iPezV+Un^NRFLxs|>!(oOs6@{RF@7(EDas%LTeQ5DK2eKuMQP@^jc zwzqpbvEyN<F#z_br27_7Xy8(ZPiF3881`M=9Fudrv;D3TpyV*J$ zbSkb_)7MVa$N(_4IaJC=o#FyC!hDuQ}-<9 zxx2Q>h@~7k5d-MEOKVRxn)8X^4SFu`aSR{llQ$>BhT}` z88YS-?(G&#w9B99Ozy}b=VMBxGP73z7L>xIRo-cEZvua==3+IkXuYaHvn=2}S;A*z z4BJTR_fHY-Y-ik6+1i)xo67^vlVf$tH!tVA<3;&6bICbwmWJnSm4_eTGUA{8AbWx~ zs|=CXFEP))UrwzS0u60|+dG|UmPvJ-a(^$Y?h~)}&9i5cITiYZv!}6hIL&@}D7=m; z59chm<4+9BIYk;_X_WSZ0P>yOimE-{$qhJau zpQ7;Kuc3F)Vcp@v(KcaP4xBXS(IMG0eVDgV+enR z$*jpP!gk`NfuN{FZVB4ZrUAd+PGKPB8w32SJ1BRXWsr=Y%oIkgU%{k|@wQc36;{1K z<>{)v+r%K@a}hN}=$ zWpE~WdI5mj58Q5E{>w+aY{e7|sT4XV-awj344Vm#aK)0l;vz&HD?tfAk477+AIjgNbQ` z4zt!yw*2&Z{&eNkRL6kn@YrTx=V~EHL+}$L8H_GwtL#@wAd_|j$$tCaiE{X=_F7?0 zJ`2P25t)a1?uIShi8~vI@7Ae;F!!X5X;V4VgVVw0QSiM@N(uCtZ7r}4IrI;$Tvp@C z|CIYpq!54bb>n*-@bhV)QfvL*F+d@Cn=`Fa4+SPxe-fl_8s4fMR7`STT){Xj20GB7 ztF?;;G<+aRK!l41!V*`QYQP|{Ar^(`DlA4-I6ohG1CJA+%2n;gatjGxOk%P5v%>|S zY?+Qc(yUqQzQ3p(m%-@;FWc;}mUSB}(iZQ7laqtSEp@O6G8_J>gX>(;fO|1yyYTR7 zddQhk+Hi{ix8;<@4SRc%R}AXW&bZozTSr(I;n8b;cpsry|X zb}8BGjh3X12HS6fLH61HN{V8}RQkT9#&LCsfZ6@jy3_7)0*Cuc*;=v9?kO=^ynTQVZmZvmRN(rw|p_pJ_4aAklF_s*|J)|GCLua-kvdS>r{ z6d=hv+;rI5xU@}85yla3jd~@v)jYy?t?m%0chAMnGdEhi-SN0=3er_Sx%~*x0SDY@ zR6ULdqcg@zO%Z}n;p^VC9mfWbw6cNS#Wt$pP8p`lQM+|&>B`_)7w;^MGuxx5$J^Qd z?8q7p`}g}%!;+O}nqibanLn^uf$v_aqeZ}hqE7MQZbq`f^0z-FKl5MqcRnAso(NrY z0{re+IT7Zw_;Dub4;J=XHwmSHJrw<}D55m7W=qI)Y62QIx*uRAe9e^i6mi2ciRLee z&;=kaV&IUQz)p<9Mg9c9;=^@k_8abH;hxG&$l zEs7&sr?q@18QNg0m#1R6Tv!(20LcC0G8~e#oHnykMc|)e$xh5%fhrR+kVi*`b|dK;(G}23NOXK)ALNIW8l_UChy`0cSa9od{7i^zJqxVlP$Ap=tFH-p;dc;tPrCt25lol%QIjBj*1n*2StbprTC zOZKK%)V%j-x}l^l=vVXfSf zyP!_dgABL?;u-~XWYN@=$1S%4Vt_2Fs!E<`jH>nt^AV{Rj|b(1s{;UZ zqQ01r{sdwx1`bgNW$7=C-CQl$Xvp`f0k-7xmTa#u%%24xA0H{mA7&O7sUy87%8n9) zelh9s`osyKUKMw6-?)~ZhnY#}R#Q2gC{ z+tN-Q8UD91Mwlf-NF&yUi)2L+Frd8#JLrI(su$Mi|9QS*Si)qk_4iZ&F3}pbJG=Ja zd?HuF`(R6$d@7D({EK)PI$QJbvxI&4K+BTF#CBO?kH9vKY=gA2i14ln?FA^pshj_> z(W1@1oB>Fm5{EUl9OZOhq*s)}Y4BHI(%av|WP7>Sueh-ZM(eVmz{yO)gbkrX)4^HT z-GEfX0y2+q&`3lQkmM5iUOna}#x%wF`BKDt43MMgK$p72Zl7(#Zx`R@m3}k-4TB}t z2LCuFreQ)&*1`4%T)u179MBwE7FJL%(zWdHTw%zm zcRwBT%M$K}^Au^bm3`!T)~ZaaDm()1^-A`EmO;h`*)Q1OAd^yoHSZcn?8X#I@MYm- zWvHJsNLc(<3}QdtfPI!y)TaYMNbL0~w&F8jn^*{B%0N-2A+g%V=e|J)oQdkaci#F> za@K@JL&K6P7Sir+(!F2Uu2rD%qdYy5dT~VJ7f#BCbt3@nVAPLgkHsKYz{D{*opvtu z=B(+rP;T5iC$oX98DDckMa7Uq&j=A;fvR+zi#C<~uSDj(6KEHlF>*rwms_Rt^K0Gj zXdsl<#A%()|E+IL5z6mmb=81BNX8_j{oK&dK>xj}L*dKv8_lUyQSY05C5Eyt7spHK zOoaE+%#)BerpK(I{rq(UEK5`!+UVSNVN1sdE9M{#%bLSC^j5%*1L#XVdV(Y?wl zUy3w{z`M6wK_h)7X|cx$?-W*nU@wYP#7%RKXCDg3fB(?%Ce|# z1|q*2HIElzR=!^}cK@9MO&KxoW`NxZov`xW{>LZqe1v7vR*Xl=H{ur^K5T0sDk>Vz zWvf{Mh+W21P969P#eqi~6 zBwk>w0|-ao7`Va?r;D{vi(q?_ZNAFb=?fo2Nn_BOO8LF{L<>IM$#f>?;BU zCcZ29?^|oB+t=K1NG*285neQD6L^W^ z1DW*@K#*f0DD0#mkaG3{S#vw^IQyT6a->)2cdUx=DCNabN~(XWLN`1xFH%=rM`Fa{T}9wU8o#)CN6IyE~<)rz_?}wS#P- zUc;s?PxItem|g#w`2{oW%Lt34*YKPFWzANB4E$$zD*+H13@yf2JRJY)xt=0@1X589 zQ5g#IPo(=?B(=#fx8m2-NI-`~S_(+$?Un!z05vD2?=@)nKlt)6%$Hx4L`#5b+9<_3 zGA8kDRK0+LgOrj|D13^v!skDM4-PC>OyEr>+aArlrpvQH;S|x7U#q|3<5xR6K?~cY zP8o%|6Zw}EDt^}e*VmdEf=x|L&4NokblBQ;b;}TE0+v8jro9&3|DbT!;VN|k<`(*B*+(GCr&jNq>5sVglNFVi;_+;Y44NP!sDw_z6s-R932h@ z>!hYXz zaaB@MiWRJt!$Yp1pOZfu;21vhvK%i5-rBwRf6=@^KcNEYx&EqSuSYBB%~d9(1mh4nYC<;JxA?Rvw+q5&5GLI=+VPUXD}Jx328 zh=L{~Iw$_~%UZKF1G&hdn#UR`5!8{BHl*TI&*!ImR2$b3b>aqXRPvI$)?$Rvk~LNW8PV>s$bz1JsiL*L4$H zib5I?yOY}9kC2@$e<-=|TXwBW5#dwUr|iU#gsOo`sGN$OChRrX`+HfqxA}{9?F7O} z=s$(Tza0wG1#5l&e3P!V)B)aPn8JHh9_7wnkz_MgEG^JUXz}N#Q}1ANlC+mFX57cf zd(cmn0D-je6DQt(H~W7#tvoVPD)%t*(cX?hy|*&jjiZTn5M*J#jz5>vkdw-1kCEvD z_^*$nmQeophL*cT3+G#pvl(-szasmM2RWIaT&(!@pXO-umGwg*rcKC z0?^UPd$dH{2B+3G3hJVx@T~G8@%2{t`+(mu1de3-G7YWrB?zdBGQ$}zS#pk>}gTo99>9-^kbuYU!KV5HG1j1-L?u zd+rWS3F9om?w+C>T_1u!p)V*d{5zeB|40VNE)!nukVFa$%(JD#&4B*@?!9}DkfvAQ zYzQwYDe*IdeQ#!+3(R1$odlbZ9XI+W#H~Aqm(Ed3JQ$En!EzeU??2IcRNvCFS z9Q-Cu?BW<%HGFyP^=ODX*Q!3^6nx{ZME3S;ttbRm;9cEW(DZOR&&x6DRTLrtjC6?)58lVh4+0Ds}K$QVG`T&!`qKb(x1k88V{5y`umE zPtAbvuFU7i9&n(eZ70g4{Vz`}y*6u?a>2&p1c{?$_wR|(e$)YX6BdxWckOT=FMVAb zGY6C7UCSdQ&b-kdWxEJ~&DI^Xo_T@$ZHE?o@MEVu9a^;sGd$1rQd#Bw1CtZa*%!$I zexE&)*U?I?ATV`E7_k}cfPe`u!O5| zc8%%}4}hopV$6e)FN*IUjL>}vzXL6UlQ{b3EfQE*0`mfpw#4#&O`?#zN!T-8`0|E) za;&GQ<)Q6$&qn|}`XYo6hBeDTYb*~Sp7h7yE`N^{D4ZL0f|0vHjQE}pNFw&)bYFii zwD zgr8^t^ZxpcaS*0Gy*MY{pdB4C@}<#JLAW z1EV4Na*Li^&z(Qnq__tj$CH&d$cPtCE8rRod|b=`5XR}Us00IXmIZrGS6Dw&TQ1** z({7wif@;Qem;EpXv@ndL*|ql^elt(gt3`qO$O&8(YE#~W`u77x8@*x15Nix*)?oNz z5kOQe^j6?|<<&>LRG?0TP~@eaY9&=W%p`8LM84#tp zdj9rWL98WwtAB00M)G-UA5vYsxb)fP_1guE$wwZWQ@d;Nu#Z)c?7l>Eh2KX>S?nk3 z>vt-*n%G%y02@;v{w5l*{~KHCSrc&S58gwDEvNAdWSvH@^nsxGXHp`{m*=I~e)}8n zF^o&34>=HOk?0SWt7eK)7I$pW28bJA57+g{T0rA^K=ReG4taF_)+9-H_nTX^J8}8p zf7X!(O)I8oF*EhpBp`Kn{MSbKV4;9_|3!ej(K{;{b9Ly*ZKBCkwy^cMFT>8*!?!eO ze|17qqo9e8hm^#H--!dtzEyMDbXb?hWvszJnQ39KsNYA4$aDLW7};~@G9sKqYMcWZ zeuX@r9RoqEnIgQ(?(6sZttOjt=nC0r#D`&=Ea!l&v3m%2=mIeFV~{r-2V^BMKzn+m zN_ojjiW5PC^)p6%E_7}o02^&mrj_ z&xvWPgpXdF%=_+;%9O<*J^Q#(rzoY91)Yg%s9E@8-DP)UIfl19GUp1Ak0w#FcXui_k^Fxyx@BwrM*AI;U-R!^_qhE|l_nZ3qkPXU?x)^a z{=};aVPr0OX16MA7_UD{Ri2c$0|c`eJs#_E{47}|k?yzjLK<5-X9`aVTjq;aY6RUj zEP^2C`3^Sx(a&~Vm|&8r2oQ4?> zM;OR=8Rs`0`wqfC7}3AeQ4Hh(Mc{L80=cyUKHKppQ>C`ZcVt4N#_gcJGV=Lz!{9Ee zZv9$eH+QQzRD@tADKYSvM7(7ln@`FUTF(*h?H+8faN2wP5IBQ4UDK+;w;PZ=*5kHc zbVTO&QPjIuk|w)t=J&*@rrgxe>uKnsI~nuc;RVV&QZDy2+nFXvvgyo1Ya7K>D1?w? zkHI}7U)6X(AgRJ?C?%o`SNU+|uJhU=>i~;Pjx0nwQEq;V(q){%i6i)%-|l~m4Wvd( zj2(i!D(-$~Z$7617_DbK3%IhliI|8dBA!ONB(fPYebR{0I6Q@o=`_yB(l--ju zF5CZUs0^|kB`3S>+NmWST9p30`RRV?YEGa=mXRUZ?OZWUFE45L2WOz8y$f0dn%0*( zQ&_Ih30*RmM}{(t|9Gg+?708oz^W%~u-R@q%KzJt%UWugNu>7D$B=HRLBd^yEzv6O zQJ^;JsxXGIFHv;h=<8-YcXBNIG7@U{7D1>l#LlmRd*CvPAbNE`<)b7a6Vb=noK?{{ zc17Wn9?c-)VBO)qI;gDu)rd6{my}7h!e98fE^=-^B@z%n=m1?=97p%_+Q@XH+3`De z^E6oGe34acc|ZU{BU=hPdHct>8r=`>n$M-!?^efa!`HIPe43EBWuA z^L8iNh5AOzlKnxY6TNUnaj)lcu82a|>nW%MUQhw9eHq<%aJbOxa=kUWSXuY|D~yn0 zA%F{zZ#-MFs37-IhBOZimYfy1&Lpq!|rljduV&AeOKl#0&+vWpj&BCQp3dzD=&ue21aD26*0h7x3tSoAf zXZ8+^gd+(Xl?VbYzE9VCloUP56scGj$=jq5JeQ(|{Lu%K%`lEC9W1FVm1#l{Ll@>z zSGKigcWL=;s*8=2E^ZM|gyixK>XG}suZ#MvAT#7m`Zo^iFx)pA>t%4@zWc(s z56TeY^YrOpu+~?&s*p8yiH4boJo1CC5A=E!p?yQSe zZWld;J8a}*+KeS8)wyE>A2uQG`-M^1XiRtI-*P7&`>0BY z{)z^2$@Esq7ae^L;99-jEjZPRz&c2!^a0H!qI_0Y7^lMTkTkVbhJ9xT7}g=Aoa+X; zvlzf&)n{4LZjxUl4@~_unhXT$k?g!Tf-0JFHbb)?0BdDqFZwFkf!-7FWghAoeaUC+ zBPz3U^}INZdOLM*)vGjHI>;Dv^A>d5zgU%^KL%NFc>^Nk;kb8~hy4u-ZO-%45c_>d;Hc;k1WurlJ5`{v{k6ulJDc)+=ma zW2lnhQ`{=y`KE8c~pb%M~mBf4KFkIDJwfLZZuxaaq2=Ojx2g(3oou) zxR?*UzYEhb244A$o5gmg@cFPRP7CbDGrxct9I4St#nS2ud9$!s$uo(8n~(%!zHMm7g* z)-t{)PllaRC>R_vgq`=tIR;q7Me3W$t*?qEJfKcrE5@(OqyEaNhen}+Wc2TVDi%LB zQoCZjQT$yP%jP6@F=!*8VxNBW-+%678u@@woVYH$>?XMHaSH-;pGeW7Z4J z(iQX?$CQ%<%`dVp(S7eVZlZ5y0sW#t!;s<;mNn3AN{mU~|?WbRd<1!a2-semYu!+FQ_WRf=sMG-oeCYeS&gz_W*D27G1%j^r6aY(re z1FZ`9DFoZDsOd90tot#&;rVm*_BP3*-JwdpO08yYBz2m+HCz<2! zgJO)^xGB3Op2>GjRYeTwy>K%IkZo1yBZ4>qa@My^4p-^an}GZQ%Y}&vQCtAb{Pod9 znLhrE7b)`2qs!l82-)nNdo}B0tFP2f0AF*4u4%(4Xq)s%^hf|iV&7xZdMuN-A?eM( z*zTVCcB#D>amI3Y_kIQ~L4vrN7Unmx%bWHyiGF)m(VD5%9doyq3>g~EuahFTRit}Zmf5=7uD3b2OWMhmDfLBiInPz@OgGo9dK7M7*97KoC-`FW{ zVW6x?4Fejz5@USXVKk^r;F&j-u}c*an1V%=M^nleOPY7`dnoTY7ci6h><3vHaHor} zT}`7TTfK(%YjfU`OLlBTUi=f%x9rqE22TMh`(;%43b~5yl4@_d7}Z)eQnk%TNU%1q{xN}H8}C8?vX+QC z;u+)K^1H2S6+K0g6VBw41?`H?vTq>gDIDlbMD=^pE+z)4Hpuk`Q>U(T;RhH|Jb+1H z0JWM=0R7fRdEd|FDpSso9Fnk&b9tcVHP3`$5u{3MgW@Qnb;XiKC(ht+ma z&{_zf9BF*arQ8IM^ObK{Hl459=+?X{-S^HbAQ$k~x;%6EvY~SH*3LT(Z+Zl;u&T-=$#7sO*QONiC}stdrP7sP;B`rirc;6e18OAc z<|lcz^o|RwxB@Bh>*PVuV0i%?GgoFLGQI9Nj??!JJ6Y$S!%40~yq<8ZLb%8ffPR=!Ij;u)n_W0 zI@wZAsV6cF>J{W;ve!4u31cR@^Y+wo#j>P&J9YN7wdDd)ijgXvThGlM-~ z5M#7xzneeGV7_p?jRt#4O-@Qe<-ggfKsBt~UoVIjC%DSs<@7?6*V_Q%_7xjd^z(mvXr6!2*Q!}=_eUw8hHva+PphT>C1?NuVj4l{e$LZKEci8 z&-V+a8r*yG@XC@Qn|lr`48P8O=_EG|*wTMI#q!J+S5R%R+7#HiiFE5x-t=cY0Vbz{ z6w&#q_7mUxHm`h_p9rB64zxbFxR~18SV)E}Gs|OU?@;6{{A7w}ckp24&THoW5yfjf zvpoU1`T&aYK?sOc{O*6p%J|Jm6vh()o0P*kt2iO!hRD-JvRcRAD%7gub5WEc^Z?<% zRX@?xYklfi0Yb=yGJ;P(#c6;nZoJC(B!4fyLh(-;dAUI4Q0BE~kt_!JM?mVL7yFt% zn{ldb4%?~`m>r>@wQ)xSt$v(Umxn?~-;zQQUqJ`S(3lkIxhd14LQmPrLL8o(IV{@J z+_AuWSM3B1{i+6$iD!=mkYv4<5dm@v9UskA!=PXge?`x=hBxNuperJ*yaUa5?d*M*fxDLH~7B1Cn@Sq<^Kg4dj1JzU};^-gf{ow7V6O*%6Pq2For+&^b!GZ zM1=zDGBo5Um<^!_CdWtKVsX$2qG3mmDvpW0kzB-`Wzfk6aSZZFCW=J*M8{N+v4Ot6 zUL0!2x{OiZ>_PeYnCYZuU|_^po0em2c4&v!sIOw*+iD zqXj0F<6`X((`q>{+o|Y)=Folb?Jbgpd6?!$QDi^&ONA0-q3b~zR&W(w+wuF>S7KL} z%!oRVB%m4-tiwCt0?gOeFTdN8v5tB?%4zuV23rcIOAd)}c?aEx;-mXHK+`>c0|Y6?Lou?(g7q39`NZkDqg8u0nb6RkTQI*gWn~zyict*TUorZJ6R1W zAe!Q!+$(&j>obLH_Ii1)D(}irT?)(Pz7^?EL1;yxp$2C@?M>Hs$7_FaDM{qZ?a4mh z*R-?k+iE&$@;$n^UrTHvy>)p5bH1xYw(~jRvCEI5^Tr1z#^b-iw!Pk$M9fUU44~}C zeNc!9k>r|Fk}EI04ff-Z&@MV}he{cT`@s7y#Pj-3^RAr**hAULES$|b27_6)o)E|m1Mg!S2)jP#eSfN*&$pW}5hX?kr&o&qQqU(gsbjr| za4IEk8g!iU7+l=?RW+PsarN;bQj-vm;?fhE^XIAfT2<4&2E;e;%s7Te#JBM#J$3XJaAv&3hNy`K+K;;PhQ4fC1ja&}Is^{i7hU3ne^q;a5?g^bSyA zseGz$+WD-<#jQsR*{;s(71|;wEOqh{*o{>h8XAwvX?!+o(9Otk+uK@v7mon2{ zMr^S|vZMBNuWv$nehpx^dQ2W-hIhdvkr-j=tbQXF-r?jCa&%^;qFqM~-{h z2Et=H@|zg%^&HDHQ5+lxU}{Cr=`byvN`F#41Nq18FH)%&S3t^H)&K#peX<68)7Q7e z0Xy|Rvi1-}(?3d&PiDShw)q+1d@2#GSg_a~Yn1Yg-5#*|SX~*YXYvRIz}^R6D}z?t z`}@^WqxWGn5aV>x#Chr7u#oxSy#Bxdj0(*MGz=s2&6@-p$6I$er}~z=e=oxzBzzpG z5*Qfhp0RkwVJb~^6X*EDF9RCjr55v=zJ4Eg2;}~n^WePmhQie#;-PKkzaFz?=%&U8 z*Aip@lf2=t`Z}(GscafHliYtg@m_v)fW>_GkN^n;lixDn!Y1Ud z@7qkibhaINFy_s71!XRMF&s)fW}5oel*=)b2Utw=4$smh^1G4fSkqHo@|Mco#&LKp7F*RLm7|owLaao(y4V`tavIC8RScQ z5?x5e6oN}i(aO~G9eC=6}qEEy3D!_wLa>U^Kf zioihq=xhHv*Lv);d2{ajBC)?&-SEx<5DdzT?D@z* zLC?g_whxi6v#*sQ2DcRYMmGE6e!}~u+n_6DGImx?2*;m=OFXfjK6aZg0$-$ea zY{Gx~m8K*(Od|!a{l!WN9o_I4Oxq%M7q(xzKaEdxA7h_(-@w7byJXbJ{nl_bUB5dq z`!uGwC90P^a$47*JM&f6&`+v%2=2U}@?H-PBJjGii%d06E)^{N7n$o(dN$w9iUFV( ztInM8?`WEmT`fljmsG^N;zFgPuTK>NpOU>4}sDy=GN# z=ZjC>n&O-g!9TB+x?CO6$saDFWd;!_d9zeK>zW5p$%K;Fd;ep|NTFh%_LyOFhSGcg zN#MO|<-Kn{i|%o?0T$;IL@($gl_qNp@()c1XrzC_;X+1Rt5damcWcf&7OBA3T=*$}k46=j*}rw!&_v>?|wo;*J-@6sPp z?U31>DK>*~Wjk3R52_P7J>y1zf9>#>^&2|W>Ti~Tn(m{=HV&A|Vm-;U;`wH%ne3x+ zW(j|}!ta`)SPgjQdEP7MgW1~HpN#Xc@)`{^6jLxE3ht{!q?5d1Wh%F9M17GRQ}?_tT-w*`aCE&C=lUV`sVaGGnIm~)MU&CJaBVlc*1o6gg0GG>K8`p35o zP7?qm5jpq%0 z;u?d_>V@qI{OxzWGw<7Bp+fKbNAEsAl`Bl73$NDt{G4MnX=w0;+nVsb9RGdAaT@wt z(W3uQ11TNxARQ85o@DI1^Y%75zZw|By`Y+mN~|76$%=OR4OACZtzwl*?gB`ll^_W% zL2*7&X=SM67qf@LiOjG^==sFYKJ<%MZJELooHDLhNIV7t+@DbK-ZTLdVUoLX`Vq*7 zTu=ndCXFN3F?|QC6!?dGdJw+?2|Bnfo)-sW8#KHZpYZJ-ptyqQnOXQ4)9tN>y~u`> z#nfz493bK)fY5-OS{OIVW4F_Njl$oi8VzqV=b?TrE{+5p@L_-fDc}u8F*kxs0ZA|g zEs(s5_zgc-4XL&CwN3^<+o_s?V(U>9{hrVH=FNIlwv70q$1h^Z#BP9s{D?p?zdRDWKxE!VgV&xdZz7QBz;8N$!=4u& z1^)Szp$1JSmW`c!{h;mQXb_$WYR?pL4l!e0vGcBe%+#XH6YH_~Klw#Ezk?4LH;D8? zbini%boDzjitT?ZT7Xm!-UFuA;b5lRD;m3qMM{K*RZ|~eaWsIEbye;`HLFgfkJ66+ zrr#%NI%ZlZ>@nKl)1t>TSucFTZr-`|4ho>AkdprPGUtc}hl#oo8>eu%zupz?;r61^ zlsRD;%z`j>eG{6lcHr~bniATp_g%EuOy;uE#Ds|?+*0fkq|39P@V)!<_7kXie;-+5 zT|;cw|B@pJ1k=l~rfHCBi!MNk2~tM3ZgLW_P}hFZNibdGQyA5tvlH%Dbt1eMFRE(4 zg|DCtz&Qm_n3jweBF+8(`v}2f`6^x+>$MP6>;W@Oz>yEGXq~1E_KtIkyvGj{&+_im zp?fyT9orb;(X~t;#MY0Fel7uiPIFC!>$P?CKjZ}bc#6P#^tcslN77G|UTUD;BlHUd z6O9vphGkSVf9mK#{8Mp~za%Owq$?`g;oy+dwW&7*9HKj@ifR1*;}HG->pzEp;ra7G zEmXLRAW#E~vZ?{q3k>j1RgVJOXlFl(4`fTz@$-)lAtvxqGl?IoWd#^C`y&d@#OLm8 zYf-Ca2*-k)T2&8AF?3%&YQP|w>O0;i%H08PEZ_)jYR}U_R(Xc7-zNhw!Oo0+SaF~k zVVQM*PzOVO)=wHcg#(3folgqHKr(=FK-vrN()U`DxsDLHg&zABYzEjL&VH=FY~%()@Ro%5tRG(U zjd7w`kYr-wq(GVfemAQ4bgeov+Tfb$2=WQk%4%+NBR#->H9x;-?7v3dKi~N5kNQFAVW26wtGkx?`{Q2__X+j=s zR%ERce|ZCccU6mNAd+kB==iPuto)it6mW$F2=jUX)>ssy$o}WkdcALsOT__+gbtJ> z{!@}y8xK&$FMaG*NYVURkwEJSh%~>bi*m*L&yIz_-vHe$RQveatShl6vrfwRdM69t zI|hB{BW~{DAB_|LRUsgMn^+BMz_`)KS4z&cMO!-5p#J5i*-)g&gRFyL=&qyVK>ZiD zex3%L-~Z2<@P|IDy#4j-*MGD_8?QguxrBHSbU*kooI+8a;s1Ocnz(qogDZK+ALJbB zWSS>Q{4N^nO$|Y@ook*sk^t+g0ycPNy&3m^sN`heOA+5@39lbEjwteN^%-(L_{VX@ z1#iaw`8F~c0lazCOyojYVk5=9W|BX~o(%>IiI3&@O%f`SO5TTjRW3;p7}wlNEJsQ3 zCdr6>|GoN_CFC-xRg?+(OT#9p0qdwA_m=4OvSY+{LH^QHbUCemskz|fSPs6KdHtHP z1;Fp&yBRz10=ZxzbbG+Wcm45N6`XLAXlI8Taa@+Y$7{aC37{RK)~81RxP; zJIk-8`!dBc)k5a^Lmz`JLSy2>1jIA^r~qCQD`dn%e;oi2^8m)1f#L-HDAa!H1X$>( z7a%`?FXOd1HP-eM7J_0$KK`4{!AI>V zB#!7PhE2W#Ep*HN)L8sz0l=-B01I(YFM1bc-Xkd<56OIgj1>!BPoQh%O47%2w8yss zW{~ga|6_j&f!`{3x1Ms~qdpt*smiAd9(l%52^jnw)?xWwpC{9<>T^N5$|)KtyN7^P*YZ3UK|-u zH2Orn2@Ys~8jgwMr4${;$rP9{i7|@o&OL0@H zZJjpZ7qv^K(3B1z9wq1S=GF6dm0mx<@S;O<3qgugSJ^^W`pn)#f3)Tl8xmq-`m6JD zNr}Ua@R`=a|0-(!UG@P{I1Lvn#!KVB)@xlvR(}-^nA)aq_K7YHTn5$+b?cTdyiM}% z5IN$F@%EUeW7VsU`OIT2pVOwi01fRBf-IPF)TckjwZ#oA1pk^M{HKmUF9J(Q)OSTO z@$o~Squ}we&pb9J>oAArua2AbwiC_*ev3*GXIB-Tp$IXZVlt~j@cHOGhD#WbQ6k!` z`ahp%0%=Kw-H-w(=NNFetf_y65g#XqE6Poawok6gRU;~S>>m!8j7{xydH$tMsC z>?%<0N>pv@y6NJegba1_HKa}`@t}@|XM+2hQN5vNeX3H?f2Kh|CTv6YLgT#Xuc<}rm&cY-XVrs7b>2H?wb5r z-BdKbdS;!Xz#H~*3CtT|O&4c*e(;~h(_NKU0JMnfRTW{46wei`w2==7(QOiMcVk&? zv?rRc0=o6TiRY`NvH{15_{em@E{_?d3RC6c_ML*h2)mOZXe5{YZ^<}CP~-Ufwc#{3 zAojRNthEt~J|w7b$v6?~XnRS!!CX>n$u^yzch73@`D-qVca5Ar$h8Mk3WymsbAS40 zGHrF_^8^#T+K$AqUAPWuVn5NafR7}ca`l_N*A?wHocLxH=c2ihQkj-{^6%}VT|y7H z`^2u0I`LdEH;iR76hvwzmr+77fV<$3Ow;Kqv(T$QRE>F{L5Lp3; z%eFVV9&4XN3`3MC$KvM~#cZ6Z(1%zIVt_-J9xXBpfTsDKhs~D?^j~)yEqwP7N4>L| z#fE}i$4;4YaNe(W0a4Re(6eF#`RUb*;0RKlLHA7{b=kFVKAH!1=uKm~djE-gMWxFk zoCKFPO_cENJ7mfZr$slgd{~SJj5DN*Q68XX?Sia0%y{hd1EUvXMFPE@vf3pvM2*w2 z@|9cBc_Kz}Hx!e)Dz_b%+(oY+9EhFCn)iPTnOGSgJh+1@{&La6G8G#wtQa}2*tb|d zge#_^WW6gAvoXa&nXKWUL3xa<9Dq^&G9Jw1UzI-_ga+gRu|Os;5f}}oDHDMNel7Vo zXn{NF9oz@Nzpx4!Qwts}-HhK+I{slSJpiHRmiR?y7LBc)IH(iygUCX*Isn#ul5vdtSKCpC#1K#QqO*c zP!b3lVAO9QckJQ+FzNF{CINV559?EZg584N69S{ z(F6jQXFXqv-lXQa9(C(ehu6C&F&;eVT>MD8;M~#l$cBLdNhJ{`dV&2!H7|C>f20RD zB4?TNg`f|!HSk9HdZAE$GF+sN@SiRpO z4?c#XU{mCN(|{R)p6zIYnB#2Lo40S}Pn^v+dY+FkC72rONeyuqKd&9c;98m7YX#a3 z0KCpO1iXFugfKWH6GFHJntP8xglz9mJ*%aUkoow&8>erZ;3qd9Q7iLA zEE}!LcV=>Akpc|N&ciZDrxpj+s3^y+vtph$NyFvWnQ8l5Vw_jgIBUA?K<{rzdu3#~2 zGQgt{uoWZW#Lwd{gc)MG7x3m}CoDVTj}mW{s@ggSi>x#~o|&8*l{2IZ5bn%O zTh@DoI~gO62gBLrLht^PZWqn2-x>-v7h^S|Vb=KyY*-={Qn)|ak?}nLB0uMYz#v&E z71a%RbGeo7k}m!UflvuHMYo0A{qO;*O3fS}%B_^u4FD$N8nec&UKS$UOM`vR| zgwBtGY?@sI)?vk}6Uo7me$ey%9N)telED%ODzb?A5B5%9yqJ0XFs)&;g2P%yzlCo z8mTv%UQI7s`*yZ|zizjCbt7c`%S<(Mu6hE^;2`>q5yn*2*HdOC+Z7PSdI$8gs=ako;`G=EKV5vC+NB8xcWiAV^%XR?y-Cd!HuPrKgMfH?{j z=IuL{dDe860UX*xLJn3(V}w@6s!{ps&?giK=d($jD1EZyBF;S5m0P7jaT#~jI4tQ( ztukh1=h}zR80`KD2Sv`g1?SGIDl`f?W}}7=!XuU8YgTq`RJh{sWvws59!|8rO`FT894|Kt*00wFL^SDvbV=oXt3JDaL9RJZAEnX*-_R#d|*KpsfAH-C_(61){k3R%6A8vgv*3eeKI*|3Nc2)dmFY zOld}r<}n@Mi?Gs(zY&ShO;=v8R9-vV?LJLa5%cmh&s`t68^NO=AnHGL7Ws8}@x#C~ zzd{sQFm7wlR;`mRtXL$h+t_qqCzo3nusp97`qe1YeX`cwpb>%+jS#_fjXH{^zGuHEBN^F4gtUL&VtGs|{Y#bceIg^kl&Y?2A` zKitHPp`$q%p8;bs16=+oCq9_z+PhWw9WD7^KDH>wm!;D;@il_`wMnnGChGVeN!)F zGf`Rjbcvat)p_wm6#9)7i3@(*sDj&wquf-%j|{cQ30B-F|Q1 zfhI&OGq5pIXbU`Ks8Af%Qkroii;PT-znmQ=NgPd^1_kbpd|_tn;nGD0w?=Cr;im*& zw2W%+gU_6x`4JL#UEY2Pv$VG$H}9ErJIu*9bHDw4nEQrdN`lOgkt}QELi292c{R?x z#{_QW@Ju(m6E6$IGgiZT*Tkhvzc#4WA$9btRCV+2CgGboxNsn7qdYWwpP_c${lU4Z28Ij#DJ+ z&&vDY-7>~0q!1$0{c3wR)uB1Alq#}`5d@06+vS-S^t#KJrU#{d(R=10*~np217xQU8ZB%s6Tda8oib)TI9uu{<($3*!Lls@{`XP zyS~6G#OYK-D_8DLDrYTl_!+GCaHy~Hwumc@yiRP<$ujtzmR&MNS;)v-O%adm@z_(3 zU<=!J#C-rxJfCKLlXmfqP5wy##Y^x1YPu0#|r7N zkqTMF_Y}o}(sHSBWG+AbbdWCR0e3@tMs_`RhW!hA2fr633O*fP}nV**1SC zSD$NAl6M15a|#_ZNJ5m4+tuX~^wDrlTNp2$b04dT*pTwDHH@?^@Y+`Yu2hk7oCl*> zi4B!2ksYE}&h64vg;O)79}idV#SKuj`IE$D=4ePn?(j#h^wd|IKEYmDP_F;<*RC!= z48mD%d~dNo09zwuP}zTc+&%)lW9>NlLS1XmKv1CqXp9W__DBQe(CUhG-r5^(tj&*Z z&=b6>T2HQ4V@p2s^^i`&hjV!}`Q`P*{9wT!H3&;>7IKbfo-4+gLB6kQCF%uMAx=yc z?m*C?3G@;1+=_NNDM2=@!`!3+P{sOWBiyV(VCCFM%yzs2c3Bk2@5ESV`=R%8aIi{w zE%meYi|&vd*~wYT9)fv2ixlIGI{GjEVlB7Zq^lo=T3po50C%AN?zW+B!N}|`dpmBs zQLT|1m<=A9+fkc3pG>5^78a6h6R-(I%Y&REPt|A0Qt1Y=^-qgHpv&MeR7Cq_;iyMH zTbqz#b|9OvF}+oc&1WTG#)Rtw38K`{J$xsp+Ae%2%N7ZY5p4Se0z+nBGibBcoR8uvEZFKH{g2mLv`M??fL~m0WxnL+|gzm9j z%Ke8Dsu}Ti#!B(Ec9UI2XL>NIIr?`NMA^t%IFt^cnI`fx-}jcCwY>J`OD)IGedf(u zwAb_!@`rhEa7JcZ6;qVAjTT3B5ahzYfB&v@`a^8P9*p+b==4X6dJ%55CCg!KlKCs8 zImF7l&YUfyFfBs6$x<%to`F>*k;ApPO@IQ{4lnHKYHXB#x|U6R@~mM(n0ZmG#l*neS75%uOG_{aeud% z&(S8&5o+beU8Cv4Md<1*^Q)j~+ieGY^`U1p-rI)IaC&9{L&v1U(l=4b1VEz(A4YHBiZKa=Cb zo87Ry(EnJozCb6RQ;*zXbPz1oE!Iz7QbZy_zbs4yZU)w1Eyo&FkCZTb)w#u-5%qr@cp{iGFQAwxu>S7`0@GR3!82AOEl=DN3x$_ zqMRW+^6UMDtL$59&g-#D&D39>5YD;)I7W_Z2FZ!MMSMwJhYtTDVw~houOHLoY=*T= z0B0&IG0+`MUif7l%63NvV1zRy{Z8SGn)xu`ZD6*%eaG43N3ea;iu1--3<;sINd2E$ zI3$tJjn+P3XVkt(iK0u^pl)oa zn`rQIN#?an@jW)v5zwSw^!eeL{;t!X(Gd|LXTSwRJxpGD&)9uck*koTQk-lVe9GCC zTeDS7dL{rhSikrC>U(C$xObeR(YVAMOOx1qbBZdO6|}(tNHd@*`0+5YA%@b803a7) zC;OShC%HCSQtj8j@FknA{#m^?YceN~$#(a^fO4vTHINDl0bt< zo%QjD%)2IT6eOmls2s<6x>^4~Zn$#k;A4mdG&}%?g%n<@y;aa-6NFbg zMNAn$NeKk3wCI|Wy7Y70ke#rh#{(Gc=#FV@lhEa~2g}NWyr}_$>02Yb zgItB8Gjd^9q-_#2GL~l!YFGUbkgMr@q4#sV<4*bfb6-W1sGXk$jgdAcQFqLJJDjHU zHx~Hu|Dh7c^)1cxSb9lTtVRtr{I6d%Zj_t)t}o}^x@&<}#E9idOE7aM%?9}BkM=di z7ttxo!Oq~`L+Go>W1IsdEZ?Jz+Oi!+p=M1ed4&k5s#Wai|DlWbVS%Vtjlc$82$Zf^%oh z!F;I~O3&T^y6$>Ezl#kI_ie|H*{2+A&lY#EC$3GQP*CyFB_wGnkCEZ@L5-%nE_q0mgjM3arREvuXL3^__Xa8z?Df-IZd{eJqV`bRyt z&Pjxg&e<{ACjL2n(SKIv{mVI-$1mmf#fxZ)tf7TgaIc#EEF37CUouV$8LQkZKR;6A zoHD0|)k@x`ausLxIeaH7gcfKrj>s^sCSiL1Bp=Z*ogZYp0k4b`;40|qoJoYeQEitm z$taP(@IF}Q0B$=LP!i@XK1MXgzo6Hn;DF1fF=uX63v;2!JMn^kCfw;R$d9u_+>y@H zsKr8D-JQQ9%7_V$TYaYODJ}#Z0Hw1boha_PTcrsSZ)q+s$1Y6I^bOis$n$1dAMiPn zi*Zalbor!;^t4`;vHAG){-%}b1QV%^*aSqTzXG(F5^B6QRun7dKeO!X=DB~z0e`C~ zX{yaL4L5u)C zlUm3N%!C*O2z85!Rz537VLjbpm}oe`7x4e{#&8ozaBO?UGYiuP%s*Y8o;m1RazZx~ z*mZjI2S)t>4VThwjQ^^L)CmCQR>nP>^?eqXR-Kt;h{ad{iiJw5g==v;QIdvX5nr!PyTk6~KG=I@7kY~W>{lv-`WRGCY&3ETj z{qP75J#8}LO9{g|4rl?)yM@k$uGk^r?tCeB$HPD7ale+**2Ubl*$j7BTwXbTjvp*G zV)Xi~ODCU6&wqMjj};B;I5z+zbl>-Y$ZO-k$bDr4U3cF(K zwMX;5RxCTeJlR`tLCnWxGHX3%hpzUb3S%r@jbcA9P1s^kO|($n;!>=THH#WHZ_n@f z@bZhuz>d?&%&oq5_jjIls>PR(4*`7i+-c9;_oz7mw2>s4-H;Z@`?FCIT(2}b>KVm*?-#IB}R z^%nB`Kx0v^=wFLB4mxfHUfZch@T*Dxq03@paxs4cl{8_i=DJ2##xdp4-IKt>qpoHA z_DRwZOgu_pL8U${fQo#}CK;{+IiH?j0LO!#qnt!n8u`v=|Tny|tlVVSQIq6&x|MKvYaq)X-Ye;O#eWn8Ru3u z>T`jaVvZ2wAbl@Z8V7UDPGqCa&ojAwWRcYKBkIlLCeF6G;*!vfg<=ZdDDWyk+0cy5 z?J#Ws5>#jbF(V$t$F;3(Drbr!M*ZC9u|5HH=UDqklNUIz)GkgT9-Bu;E9K71?Z2EI zSDb4%e}@4(4xRWvU-jh&h>zq`t~jeO^JyAoz(kl}dy`Skq$0xQr*4+a!Mkpe2kV?Z zhvfRrD(QOmQJRf<&*aT5sx~;*l-M|%izoln?BmP{#xVDX=ZrZW0U>3v(#PG~oB z$~!6my*86rwt^NK#9PEPymgtpWE{=AF)921Q1{kRRjqB`D4hxj0xBuesiZV2-Ho(L zw=Oy-p<>do2RE_I{t|J^!9D&N$y6o9$pt=A7%!>%OjEjAT1! zvoPMj|JZHy4_ny+Ks9w9?f};Lnw9Zh!Pi85x?yq8Hf>K-NeE(ES~@Iz=SK+f{NMaL z<5hNOk!F1VY|~=|0i%w!Dz^mfS#RIIy$$#ko~{Imtz(T4WVjFf6n2+yXu~X}XJil_ z*nRmI7vg#(6~7NPAop1c_gD|IBlM?N)&O5->9d>#s_bKi#sI^^O*Q)wd%324?1^y|&fyq&Uh%DADA#z@0pfQ}Gs6K2zsQkOTd$jF8N&{O@pCdqd){iVuh2MZ z-lKI*+HZ^I*xuo3Y%5>lW9YhO@wyCWj=#1zcYH8^-!QGLi~1`sz<5Nf08=<|6JM1I zr*oy=xPTl8VOh;92de~$P28S-YTmey)1qgoYZ%+^pVXD(x3lJMK5>Fw(Y7LPx+`)V zF!M=c(luo@DD}HEACLIh-+TIIjts`QN}GkSk2p7#+Bdl4V}2f-Kjh(c!T|pK5s^K31%{&*==Ae z#&ELH(E9$^+iB^cnj!9SJ$bc|L`HM|yH(A&MuO?q>J01AWU8s(sZNc=xQfe3s@E=L z+nduP9@1%2pADRxh1Z|&jR}it$g>bGMdZCTu5tHn<37!dqfTBPGzL~s4t%$Te{lk) zDc7W0JSR=^>N6ITCB~8lr$#QDUg;AdfuI*0jxl4uw_o8ipU*TZe+==X=D(R_y1eyn zaeK$$Tk-4BGn1<4UrUreO^`kNz~Wm2x$J!!>iDAL?z{9wz5;UBpAtYdo6WOpjLi1t{pU|Ut!Ul8i`}(4afd26G{VMxoI}#Z*6adMc4>g<$gM)t?^oPBwT>EgDiVFOZ~D8a%bOl<&y_7qZa33) z<*erELz-DodKlhD7-=Cj{7e z?!~Pm4mPjpTQgMQ65nM14hdj;gfqBpD&nS>FVYX@11mWL6P)|iNj%F74D&{0jR!_G z2~_eb`zPke?A4=W@-}1{rpj$@Vnf`k)bzP3lC1wkZZZBu5n|^jJ21oYU6%40_@6Xg zk%wl(*^ymOQ>x4SgEru8GRDoZ+*AZKq>pldYSU)Jh@QAikf~ z5_ymZyq{;(>p(l95sIC!@&WO%hYN_wbyj}D-!Qkvs(8W;e=DYNR1rkBOzuTvd$)UK zB40dK^5h#{JVn12={`o&9=XA1*Yz;1R|$V)W8Zp%x}^Q45RE17KO(Y2k= zPod@64TrF<$@7yt$*Kn(4S}6E6bzgp_bx0}VMPP7Dt@3;p9}0kXE=_}_6+MYwHAsH zsKtApi{V6W!w01nZEq|CC#vm`{0_a3C2sJmuh-OuuTGT7t=oH4ik466{sg)aGY$g@pW7kKR?< zFkyGrTdT+S(+-%ifIx3b7e5SrQMZ-3=LHRr-Cion)(Qutk4=7@)Pl z-72J1(H3-SrPS`8)qa4TV0QHkpGc9-U5}gdPI9%JpMVnNv+G%qL~=5-MxH{8amJDo zAXwb6T`EUYo9^cejz4*08jVSbpPjd7*%I*Of*}4#L^g{^n~>!geT9NHZDPl2##*m( zbBMA?(qJw5@zU&Pg-KttG8SFJtz*4A6cj62DDv&>D1 z54kI`B?RqkIyAPrjcpdyW|&S&2)6(JeLE=AF;QfT=*Ez}%GS-9o_o3b6Kfn`;x&?g zDxu{osek4`BKl9OuwVdTp=cneH>_YQ-uZGH*38BqzUS%$2r6m$CMQ6c%uURG`p4UQ zYh4v+}Rhfrsl?G7|)uP6xT9+buhHkRZTxzt^T#}kZ6oU#C*$G+7 z`RheNj8+x;R|@Xb&VIYIgU?1HSFr(X*%ITq+uN+agHlO>*aY$q$_6tni8srs1zgXM zR-AeQa!#@5NetEvEsvrOH#NI{~TR zb}!DeOvP!$6|Hm%!i=4u4idpTc<0aE2+NN4WD>FBd2pmOMhfIC^r=aH3C=O9E*0Bs z!c4mv=*Ty`%N>XN#Ji)wp_7Ck2ob;(H{-6SfZ~qWsNeVU9=Si%N6%C*UZ|e>@Y9MW zI=U&@?-bX>kB{%PkybYhLd0q#qs}KhywKW53-@PvTDgxMZFI6<@(bLAN=wW2l(i+k zs32u09AZprcDh7;^TzoH&#_#DSv!rK4>q5&*~BbzKl#Opk-I3Jzh+rC2gE8go6KKv zKKW(DzsOvXru4<`cQ??vX95{E%?WeyLHqW~8pholp_Qjf>^x4RkazwHcCUDMVa4o0 ztEtq6C(3C>biL}%p->D31Bn`N9an7u5{4Nuan?zX1G+_2N3v9?MA3~lp7Q61TGjzH zYE|Nt-z+db{`@pZIa$J3x5&uGJ4YYlZ+8W_pXp(`1;{q&? z*sNj>+w67krP4!F#Xyy}>`bV<+_NjGnQ=1ZAO=7#8(izH!I|v@;IT>0uVW|xuo!^D zRyGs^3Um6R*AgftBfNR9Hm!|($ww{qvGh1?;-ewF_5Vx-V+5e?+8@##XC5!ojhp-g zF@r+;2vf!@Px7wB;Wa<|O=B2Ly+6`}0MnEnYQP9^=_WJfSUzrmp9@`G3Ru8UxQ3Qd zDC&Due&rUwylg0~150_eU5RQIKwqYmJ55fEbNht)8wtCcFb`F0eN$mhSyZ$oJ2A=7 z*J>Tg2!ClrD8v!1T@mS;K8oBR*`$OYc&s>alq=G(_GsmQu=$Oo)?-jr$)X)d=)pd# zAd6YnX^{Ou@|E(hdRQ_vP793DoqWH(n*h>h5KuF>trVhcGdEkUZuN*aTa8O4pE5ee zRZ($(w1XM(Z4(ZnF48iWKbi`q0BOkAA82UcAoDhmRgP2(D~fvi+vu zGWY}8*8-~Hb8l)v`V@>@K8vV_AJ=2VcPK?3XvQidvDfRqn;+Y-VQ)BEBlkq0v%1yM zs-9|MIym1v%MQvShPGeg_2s^8a=DOzHP(+g?4Fry!g%l%Nk6xzktw1z1xOsUxdQF4+*>;3 zo_q=kTy~`8FSTIg>=rB#fA*UC|M89KDFTZ2bt2Ze^5h4v*gPAK?2POQB@>v&o6R@X z8#%jgT6&!;;y%kPCwO)f*L8rfrfi6ql^K#TaGa^Ax#^!TI~hO3nReg7!S z(}9bp)8&leYXJ%6F&NW~4QQWeVR8Y`z&`+>{_Aw&%MAapP=3Ir_ATrw6wC*}{~-r1 zKUmbD{O4*$3eW;?dC;;W1CZ=^65!gOa-lWwr38#6U?3~_px+n2AT{7`0c{Lc39o(` zB)T?WDZB*&?~2oeO#l_IkIqU!@+K5qzSWb?eQ`CUz!^4O!=^BXqY58pL0$ofLXlEa za{{655NBg{`YAgMB)4${u=5rE;2=5vK)iTYxJ z#61QQtANj+?^Yw`-kG-lD&pi|3vKzHG4OKucLX<8Vk73-m_R@5uBra^y7+Z57TA*+|Ni}JGLT6M2YDBf z9{9mnBz1&ZVgiiux89mvG4$|o!@C3cb^$*qU`vgcFJCh5*$C79^EZ+gf9b&%#-_y# z=t|}3FkN$M1>+W4Mr8FrLHOm(!%nbWNGSos>Rd!-=-YLW9CE~*{D)iRFSQNmS|lSi!9UXgoerkV;c}CzM52eXCCa`PH{&-8)TJw>g z2=HoP?JU5}VNE4!enBEXhyn5k27(Orij0uzib+W!iPsu&2U9;7!|wz`Qmc;a!TE zW=dqyWxn@6bL=g=6n~?A%rbzUIA3U>R9LeMw6uirM-DfbRbp`5g(7qdN4} zJ_v?eSm-`RUuV&U-Pk;%*Dpb2in{}Dzxirx z5B^BVtb}S*O5)s;|5{uwh7DsH`1Wt#Bz{tJon6!zt`+(rs4)pcWyglf6$(@u0Nvul zt({cZyJwy1)ABr|nW=<~0Px4iab~<6opy>62pl)utw*z>Ps+@Kqquc6-;s00|H)qP z`L9PE)Jtu2BNYcCJTn!(q(!j#+BRE5GKC!VVie4A%vRl){$DsI^m;|26E z=PXRf?#t&{V|W>%OPp_WSZ#)`>=z}2v#a@7GhMY`bb-X=XioT&UAp-I^R0heIiKNy z4Kn_U$9W6TaRwaO#{_Y>qDL3t5`4DLzLWTI^|sl*R}UPc`3tn=4F=e5@3xVLO=&{~ ziiu%^)Np|Hrnrfxk^9S6NdRJxPDzQHm)MHpeEeBGX=#G1A=qtDZGA?%>-F}(5s}Zs zDd75QN&ZeeZN>;hk&!_AK;iWjEX2W&pGwoRw8o7O_Bs-s0M4(D<<)w<+`EJf^uy($ z=&1Q&<_i#}4A?Mzpsl>|Zy;DS1-nE^S#4H#EE}=-f&XaP()Vx04e{2McJrUc=W#gH z+kdGfm=EL)AjZ+GE7BFoIdIsqPcLLGH_-!t)LI+T)Q?T_6cy?0R+&C5)472Hz8M#i z)iNCs#PETcz5&Boc{4QKsQMQ7d%@L`;)0uf^3YEF&rFLoQq5Fr?q+U~j)C{B)FvOY z)%48VE`LGvX;WyYaM2zACR)_X@tOXAhQ$;MhFoVJ1u$WxpqUwI@b%6x$pks_^_ms# zrQfMRVtaqgfG;PR8Uy-PkQQL8RCjg%1%$ICPr_9WhY)Jb0v%_ziJItHR!HyHWw48G z3U5o4MVbs1B~QKvY8E>h$MIXd^Fg)Vw;9jNgcTY_D15}$wW_T(A0k;zjN%1q)d1p| zKE`v0Qoh^7>3(ezld~m;|EF6%7c)v;6$6xgBZ28ld%S?1eHm}Uq&~>>MZjrum+Ihp zAf*Qk=^%4ZEZPPM^HvzJjEQsQjli8Jh~+yDV3(d{BJo>`?=UUn7?Dksfb<(qJrjJ% zTLAzD+g87}YPLO`ZRGTj6b=ri1_^+iqpY7{$+x78>YEEMu+WxX7Oz34Ocsufr-Oq2 z=@%^vPudbZuy7ko986&@O#+=SVE;a`Zk(ljlg%+nYw11`2j>*%$ZpSsN{%4cMW24d z0*-!eoN|`VDfll<8yLJPPe>dpAE{ux1W>B-Vos)aO8AlFtZM16d)C{Lpm(GYWFQ!= zHV!xcos^xt%-*Ncj{=a{IT)h}s7aIkd8fz3EudVnx-UX#e-53U3a!l?{p`2to3lO8 z$<@uwQ)qnL>#CK}J*u3sm!;D9zJczZkgX?TA*OVGzR+P*5D3Q|$sfz^Kl1RXvKJ)g zhK?Ox*$WU0yWWfhfg)_DoIX14mmT!D(P@IyMl(CyZC-5s-NgMa(<*hhwGl2*@j?Rc z26;eru?UB%!+|&O2W?K%ueu+LAUJ`ZOu-W#ov#~rMM;iHCBDDH0VGC{%pW&C-op^j z8wY!XIj}7#9Ybv$?b-<@0&Ad7CaA$@J-%!Ia&Wdh+!0+(4W^B7Ug(OO{HQYvG-aXz z73fXZI|LUXr$m6}Xa}UaHUGAeFOc1uJ-abZ;02UaXR5zioz3`eaKVJ1d{JD0fxE>< z4Ow9)Huy8ygeL!h6>8si3ntNKe>nHOILzR^qzA zFuPsClfA{Zi7*oVO2>K{DiN2(&r!T)_bobasQ$xr>Cq{flyN`;l;_q>T*T;o!OMt{ zWD29^2htK12V4w1e?`ce_#IlnZgVna`n43ks^uwAkit2&;WzIz6IG1TzzUefeT01B5e02@JjsmX?(6-t)p# z=FVo{2@dyaH!LcIe@#8uP#WaaceFp+=zABP546R@)8f3xu+dabj;Ip;%Jb0*w*kKj z8oIe2Iog;?ugMmS*|8@QCxAG&{Ug#CI2bQlNrWb=8%)ssHdRcZ3SNZX<&WRwquKyG zlec>V8h-ypGpR1NA!{DGToUQ0u0ICq#;};j8TWc$cIcuK-J-PKcAUTFcu`!S3)^Q? z=u~H<(uoJC7GcKur(w>Bf*nPJUM~j4+aRYU?01ybVOXZ+BK#R5I-@d6yeb;Jq27VN; z-B*jnw?tf*vrHeeOdI|U_-DJeIy6uMLw#*U`{=#cgAhy^TawE|2uM_GNj2y3AIO!R z0*mx!-`KnvJp7`e|mqY;|(o295Uo*8jd zOEA*6s(Zyy%9d`U;u&v$dxJcP)H6Y#YVgb z!A$F%B4e4G%f?>0^;G9@*@803Z_4d$B_0vOgP}?2J-2seX7hWVANLPt3a)?0y=u8+ zA80@w^l*1&Ad)?rLjhETEk3r2e70N@84ZDn99b%HjIfN*o^^68Q>)wR7~2JpV0)qR z=7*TrI5SAJ(>MKdDQliM0%%?#(V1Nxejj<}loYODWwW+l0xgKR)>k5S5g%ReJe4Vxfc{Z3H|1b=c`geZP^0cFDw4QY!T)vr~ z-8>&&K**gQSu&egQ2i}(UFb^S0UYx%6R3VN>K%ETl3$-Y4F3oF0}=WuO%LED_2_Hf zk^S&Fh&IIGUj0gGiilEP!(n}yc`NDCXl1cVF|`&=1sn{oV<9Y5c+>=QtxeCV?=zLD z(1&qfbvx;hao3q`8+fi??icE7(H^TCKfuY%rE*vORa*7f;8RTaVgBwI5Lk^PT~~%i z+iFgb>2NvoDw>GT4=k(rC$#PXd?Ba1#(WLCs{^CLU2uB8>{G1Oqp$(xCn4OEe~SRl zOdXUt+a0+z%Ue`x>(6AJL{-zM^7M+Czu5MZTs;C2sdUWAN!cpj%>q%^bq36%{m4Ns z!wK!ltyeHmU6=@({SP0GetWB#u6=(J;sJ_;G0~QYiZj3c46*$&=LkM4RnLRJ!E-+ztE?H&=@{r0pOg7DaB1>W-^Dk%>uRp%Q05xH0!Ug5?0} z=uN=hwL5admdjF#-)q<3xkoN^B|G81;%7Isdb$+xIAr_4jK&@(v0{y`Tq&@AaOLXZ zYTt{H&aQnvAQfft-Or$0T@x2qv=N$*Ti@0U)tl9eTz@fwx>=$2s)MHV#!->vBEsHGlA7y_?V!u40qQ=Ds4GQh8tShc z-KhP;aJMO3_`nh1PjN%gK9*X{15&5;m_iQ?f~cR|`XFjI$SIm`d$9Spx8B>m?20sc zv*iSQN-4%tByZs^7FX9Yscw8-aJCZGNl@-FV^ZxhC!6&;2ls5>C&%Y_NAQ?EUg&)% zL4TUti&o?vt$yRfi;8{|A$sSg^ElS};v)g$`7sSpMYd_?cR>sUVnc-D1ZYP`2~JFv zHy_;sREoG9?XRk{2=nU*>Y?#t!fx>u$H>GJRMYp+KWhScddTcFPW{IJA!sO2?U4NX za5wRCvXb|*H05dH6 zt0Y8J1~Y|q3k{gkWj~gZjY%BNk~K3=TzkV9_MHAalMj1a{aX#7kHt&nv$P!8`k_mGC6?*&!i_4dk z?V!0tEpg;ahQR>)o?P=4!RCsbjgKl%wEF>5ARDGcl)o^mJ>E#DlrH?$4PC4`=$t8b zNh?#bpAUWSdu%=_W`r4LJPikFM!fA$mhCw%m`Umw0FE41W3kho!hO|zcvEifw98#F zk@t`>Gl7YKW?|fU_QQwxFAvt&1Z0wK@S0}voEvr-*G>QF5WQGzC^2>@I6r@(Hlr>I ze(tmX9YnZuQ;>0`%)W5#&E~`D9elUmSne->Z8p_hnh~e#ot2X%aXv1ABcE^6y-{U! zzD~1qlh-BoR0HWAdg~;esuwsHJ2eg)62$xO^#OqemYH9od6TI1wHwM0LomntSsN}- z*7Q=?%&MQP^?ZQSR&xe0?=fMsM$-U^`#B)C16A&$o6x*eM8A`wZwL``| zSFjH|y%Kw(0eEd>?g#(X9Z*b48~wTFe*5_0r_t>y1va1zOuHaayT2L^&V3@#AhCc- z%~Gr)P=j*7$*8~WZ8oCf$YVUR=GG}&{=A1l*qm2v@w>^;gS(Z{u+bXGBJN`Vy$>W% zuT#+4@iv4eJq&By*4Tk}G!&8Bm(}jfv%kG}mv%tZZ?81b*Kg5$HpL*6y^IV)g8BKX za?U4VcbS8I+82NGkZl}CBA$B@oQLAl6i!#JUEO<|TX4ScW^tu{LsxN}_b{`%m7qVv zKtqB2hY>P%><=(i0Ibi|tN7uCmerVCogKbr{b`dQZ;Ahs;OyfRb%!Q2&d*oJaec1K z95-zRNBlNitPkQ}!svaEA`0|pAOQbh*k*Wh{co{$W=;Bky`ot zj4z0idoDtW$^AH?d!ep&e0Vr+W8&B{&exLJBZhv`G+&4B2U;QhO@=Ltd9^b0wT%^J zsJV*gT#aikTu`2E0+NtGtRB25TicD{3S2EapI%zB2@Gn87n9K~FkqtdBRQI?$U%LT z){_kLt^a2c@sGxi;naT?UwoX+kbsif0VZ`oNa0nPrIOul+)Mv5jJSa(XHh@jwVB!m zzMNg?YR}{{oXawJHuzMN&(7q}R<`HbjqNz|Ty2eQe#n7U*fgI>r0NYUE%PG-gh2Ol zS8yy=<}&>>g&v7~&U7By2Q^3M>F~R$sI_rE0|WOXR9q8+p_E^5>cP3Md`gzz6%LEG zSPj)YH2QP9p>J&z&rAUi7qim1ewl8$ltpg<F4%2tO*>9mF-q?m{`&yw&!*FhVq8q&NOGpCetxKn4Z;O z>uNPScjKiSCV7q-U>Z{M;Ests8LfmDLjrNqz2Ysx{gzn(0DkbL{{}vMHm3Ip02b5< z$|8$0#AyUcrB>aRG20@~H&gU+IO`xZdP7OMO$5WS??!{~_FUE0{B!a4vLt+AkL{fu z!m$kGJwlGTjzfE!1L*uzZ+)zuqqQHcYKo2d_=&y5-(5`XVzNnii^3xLoaS$m<^s&H zEyLV@z9UbUPC0HyU6Crog&6Re@3p%|-4*_06VOad+Y+}+)xjYXvZ}c=3b93qIMHT@ zvcR%kBpM(k!@zD?a^l4tcXoOtzn@gEvKi{=mmui$@)jQ1&GgT@!>i*as&*;j3ARY6KfU}Xe%vbZr>tBe}RgewCUV18C! zT^dIC$=p(;m20)fk|tywENz`o=sTZ+S8?wl3j^Q9QmKUhNe6OwA=tS6hVNsqg@?Cy z`*+cyXKZLKaR%Wqv<8pu`PcJ`55}LB8rH}V6z{{Thv5?}{S(784y`QZQkUUv^#{FH z@=g`Sqo2QWk~uJcaWmPq7z8`kok`$3pjm zN)p-Li{VMjPQ}Due>(XGU(>8DqlLN)Htq4(0@-NcpXO`I0OTLZYkOD-Y>*Ve9+P1Q zA-j5T5HqrWw2tIP-=?YmPFNdhYw4D2Czk9AQ(}KC=Y5^SKza|TI;;rVNA0pB^s>)= zPhPw!TNGH7ym^$%sqt0vsEljaonqLja&6%f#~rXp^1J=vaulmZ8Y`t0$IMdS0@R&m z#EK2a*Y_BopY8?SV2^utk$>GMg;jw51X{sY*Z2sDj5tEZFnm=!?@|Q1hz_KGM-e+;>>Fg8w;?zxH?d zC1%*DeMA5IxCO_|TWR|gk1Fg)^Z-0OvCrd`vQsxo;W{gH4XzzB7*KQ_KRx&Btx*wO z>aEtQx|7TBEeh!CM2e^J?MTm3YjlnlIZ!FYaK>FZbcy9OZ1`5U+TNc&;kCbdTPI(; zV$xnB6h0pUO^PjTT!3JyeC9oJ_V|ZSj#uA;+kMf&j0-$ z9FS0I1-Thby=L`Gp<{jLPYnw)K;Wc%7oCTPXUAP!Ip*3Q7HD08NEqb0#%RCe0BIrxu=fy?zu6L0ZHudjsg zH8zO@WstjC4^69{YoJP>R0403Rom9$lf!L=eBELo8gg5-)kPiZ3!L=h`!cAdy|4M; zA>(E9Jkv@6cAN7qaQ+aSab=GBhXz`JaR!mXJ8hfU$dFy-Cw3WxgbPw+)){B7Ep;I$ z1z6iNsYj|iBkU2Euo40L6tt2=ylKP=XpmD3<0~CTDNq3kA0LmBVfB2TJo) z@KlHVRiyR}3yi*u_&AJYl=z6ul8qLrPc~AEs`|y=)2cWr(E-{!7M5>`1kCvDjj}Bb z&rCXh6!dLAeZ*usQ8!h3-M-q(x4C%S3T!uo+iYX3hD3J}PisP1YDiC|dYBZ1 zFM>QgG0NW$+JJCRI3_Mp%Ud!QMj$!>b-T-MMy}KK@{EYsbF--$_ZAW16I{5Jz^NE(d9aX!mX@{@B(ccMpGaaR20eN> zev&|7J@B=rxYC;0X@LEfZPMb1$Kr3T9%8k3Ok4Qc5yBDu(|22mZ)MG|ZHX2S5 z8eFbLTJ<%q-y9soU7d0<{ZUs$>>yJXu6QBB{n!@s`>rsS<%IKwLU)@{qsamBBCu6q z0jWlz_Tm+b&TjLZ zq3kZw1av;Hk;SFS^Cz^@6?Q{|OW>gJy5FW3OHiiU@wx$ldZH!3B@#JM{$teirc^3V zJ2wO1(h$4LIyWW#d@|blAopNmI>!*-g+zA-9UNSEP=sJ%&`QKi?jI_Iwl*pa5YM-~ z5f=SKaK;wTk?3p6^)F@)lJ%PRW05`uvpUCTxML5){nRNc0!fD|l@;?nx{plBiC z{dG1@;WI;CrTxAi(lyX|18RL#%`ra?xl8{Uoet%`%n!tIu3nLkzq`cIbIO&<O+@3+vEYVj5&coe{((j0I(3tqDQ2K9;^drP1>KD_~L05?B5Q83oong)s zA2eSh9Sy=0)AUVi;LvNY=^8h8{As(rPdUTb^mZy41yb&ZYLelDqe2QFb4?YxM2fGS zkV(hzfcT*g<4WiUo2PBWdMWP3w53mHWI=W})@6B2MmA^BZZyIGIZ%oRr1`?H-H{ZU zyUwAj4UsY_UU!V^iGCo7s!OAdUyj4`+FQR9&7sTR9`*A*UH2yjzq*49)v~C2>9ZPT zH;224qCi@`HGw5`u`82qpfl4%wjd`ykJqHmsu@VdsLJ|D$oA$)-4?G|+e&W+a0Q(& z6~5DVhrWasRcKr&TKEfK$$q$T8)#+lLl4B@q6oN3Cg-oX7m7quQqrbBCtMpS&pvF9 zfRFFb89#5#GU->$yeycQ;Sk!ux!*=%?rm8-+neKKVq(-+R$m(_jvIFV#FpxqII_Kl+L%)lh*j7h&d~E^=!tuMl`fUGEAcLL$`FL>%PHh| z-$eKJgTR9Kq?-JAlh(?P(q$X4?2vzMIw>cxwYlF zO&d5_>g#3PKb=l4YG@Y9a&2wxMzast#qM0adD7U#djMt^7|l}JSg5F9UN5zaP3oo9 z;_5uMp5+1%WR=WvZaD6h8}#5*jOQCn@2Pg-8Mg65|_PamZ5#5JAQTL#(jCz zw&llo{>XBhdd5dbR}cVbU+ujCC9$aycO8MH@osy_sw$YM9L+EOnW$WMy-?P}B<8e&eiGA3a9*Vnfh6npH>=|fI)l`C>G+~zjGI)tQl%IGv zmE$xK1I2cMe!MZ?KBigW%TGD3{XzByj^>-|yr70cpQg)zD)uThx29o52_+!4C>VD%c(jz_% z+RNuo4a&x6dR1Q~@%!x)bWFRr%|!K(TxO$fo0S|&!+~+tdKJGnUS~1*OLY+bi8*hJ zy%!Y4uHU;vM#1@53bD{-0c{t*NwsKF{u_RxW+7lUH~nfS6Os829D{RXzJZNKE;N2B zS$p}mN^s?9`>H-4Q$7LhySLJk>dHBq%$M_x!7J_pFPNA-5aK+TNo~dZ@gl&(R-`QZMO}z?_cIK1bacy_1z-iJ_)FQ= zrf{s<^iRZN;IhQu_ed|-NI~}F4M&mChqcra5)Ipa`?SoTv(;0P=6~y|ACxX>*o!PK zpqBU|cx4FKAcJ!Gu(@x6=JW5hFaQc)AV6nWhlBzj-JS9eC>tEg6(z05yaW>srq5@I z4JOjF^-rdI?JmcKkcfZp78hVJaDP)^Qg48AQk`S~cu#HcA0uFJyl=C)p4Fe8_D&YXC{|sA!elB?3)HO04H$$( zmd?@v6823Jy5YX7agB?@-ugHPhi-v-K$Bjl|IXJS+=Q>suQY`K(*RzU>TEx{9d=!0 z_VS^z2b=F(B$MH|$aJ!BdQ{#)-cmmC#*Mu@Q*9VWlbWNIPRKxPLSQ*_)3}-~ra)B5 zUw^x_c};fZnt_OvY@>-wJZg6caSZJ_ES;Sl69F|Snw{BJu0)Xrjz_`1%q(hN3Ao5T zp-6mf?dRMA-eG=lOapc!4-E|9F3s0ROAJID=ev`6K>CHRcxtybJ!YZ59dcd$6Yf7S zb>Bx0;7su|l;h>|D7$al?H|4JGJ?0P&psw)KlGA1avybm2QnMLXB{1j;ZE4yrw=_9 zTpzFK*|Qu4JTv^Xa{)7YEJF_t(}0KrwG=m=v4aG0L=Y}ME1~{^CHQzjUj+Nu4Hc8@ zdOroynox2KaF&SMS?PHOLO!@a*Btp_EGEvlmgxYnN+b9bN#nFro!^PXg-U zrGBSghk)+Y@rD%$Hur$ybfK-RTK$`v7ph=*sG*DJhx($aZ!^+r*19;7p|rB3nAa1> zcS`$!TJ&X36mCi2i-U!Nc94P&Be6r^IG}tUlboCk7=)%Evub8p?zi@3h%wXPEb=AT zHGs4j5m0{4_2a8v9ujyU$+<=zLwyYH!3}>MWHFJecPfT{KY|t9NKokGr0Tf|7;4;r z&2jaM>iLQDV`kNpwp9+T8toj0w1uv&BtE8EwzL89X$&jec7^N zn3VFfus_i8G>CZ~Njr{J-3Gd+6d;i7d(9w2(C$xs)ZDtK&h}poX!6ou?%P4>-i|KT zX&s(V*Z34vg)CQ51h*OINKa3p#H6aME|DWCUWkng!725-&jQewk?aWTO${MLR$Q53K^It#&LdGOkV#`f{=E??UZ zZmvmzOqq*&QILY>`v2s?kRJyAc(Fn`FvBXKf4;wNEbO`*MINt6b(uH47(>qq;RQy% z)Vy^pjf?-noP+)u#>B(~DG3QlNl?rG(gWv_rnhK|iU1}+ln6)FTX5f{%{TYp(QgJ~ zC5d@55YvizJg{0Qzg)ZMg)X91kX-d8R)bH}B%ibV*T3IJYl$C9()}M?6kb(Q6R>$S zF7ECBgZINbkCXy56C1FWh{xD6E*t5;1>^GZ!Jn6d9y%eqbZdD26g=DLU|Pc!MxA4W z&YBjwK)@3E`xAndY4#gsXx;@o01NP38sS^OCIv=-8bD9k;GscxZJ1BY+JX+`$j?;uWhtgE!*#*~$^Q z0Qh5Jp>0w9$9w|+6`%tk{=SzRxBz6sh9z$%;dLOR2}ps>0X=ALeRuYcQqtG}GDP3d z&~Q!s!wLLpiCOb3&?x=))02~vS-|HHS$sMx*TjSk^!Vw)sT|Q-qh$lH0|7bs0Lv^O z8REu;zV2zX;-xW~gMV;*oZx+eOH3XpUnIImne4`sRi1RLFV!|(jemxDk0($@`(?Du)nToTI-0@HC_smUFZD}O8Q zB9X7L$vn1a@%InDDjzI2VAM2fo+rfkDec$)D(B4cKdWCkPiCo3LgPcLo)Ad?1)7*0 z7Z#S0|B8D_>@ZRTZ9i0N*?(a%fsYm-1^N$z?cm>zUxBBg*U*Ps7afm70$U}}BYD)& zIS8JM!{cQmUgLtLfRBX%`i3B53iG1l2#Vn8y<*H~3!780;iiCY4Y=;}UtPHi1{k8= zPWIzPSK^c40fq}}SRgSx49KdI!1YD$ozDt?f$+4%ja&64@@q8)xc~>=Z>xC%y|*H8 ztNt@5z`q9J_0nr-ESI7itp05=kWPGsH5+iuyhy46c|$U&s;CLDl79{jsjW{oZq$5y z^A}VgD84sczm!4vpSzLR1iTEFh`=<>X<~UK5nX@0A17iCMB@PZg%j`uo%2+k7dnw~ zWtMSu_+(x!y)uN4s4H!DC_E1~Q-RLJL)AnHE+D&xA0B-5KktV}AL$!{2NiK_9&DK% zdUo+_1CuSpLCdvjm!-+3Um0r2Ch>wcoNFU>w_*3-F~?nERGj`PrPT3%tt*$?5}G>L zmYxo-PG>4jFSxhO_H5)26-0ZvuDta8FyXn-MH6>0FoZZga*Z6i^K?-%z*2<@27zZh z2{?fwSlrj2w=+GO#d6yi=8)`6;u0>iLJ}YCzc;!~4g0cy`ob%$z2rK^@0L!H%;eh& zK?qN#N6Y3Ew&FnM|DR1VO>OGi&-dGZ{R2v!&d<~d>jD45N9fc$?y!&Z&}SAe`zY}t zS4*d0uhqOIWz_sdE(6?{2^%`QaaW!;U!P|uZy{AbUpwM}C<$pm8Z3Lq-(QLFCGF|o zUX@bIIhNd%q#@gZsF#0F0*;>k>S&ru{+wZwucH*L$5Ft!`W`3XeO{Tv>Csiq4Eev- ztRES`lm)N$CbkJg*mEGdB(3ic3JwkiXw*mSx&^EzCMMT7SSg%&pm9&&OHj*f%KeAS zjB-6@o5v#j7yFGI-CUyx{%|?Y!IxSWu`2w=Mc_#5SF^v!B;+{3|IvEvb1LvdQy`#Z zXM`*FKx?8^WWY4lDjXD;D^Q@QF;?TMWKciR>qv9j4!(eJNzczx9mD;T^%YrI>@17 z_&2(HtR&_B;RB7R?l@V8b{T|!^vb%9h`9(*La*GoD+SWPRL{=ekCALHmJ|%Hkv)Eu zttR6}E$n2H+&SSG=9Vagt}C;kGjHc53p-oFOf8?2t!9w9t06fae&T>mgGvvA z?3V)01%$Wzy62aI`JvK(&DM*|muw@CtTP|vVWPESqkvF~3(Ei$n{1(%sd z)a`oxr$-l)&NXb$i*p-Gz%HB|P&;3^Ceu3t*+P_W zXI0vT20#Di0^BW=>4%$K<6bEK{m^r2iDz%oQsy{x+x!FQaBDIY<01gp^s2<=9NU}* zn^N^1z~oIj0kYd!q+Y+0Ej!~C8hj2JL9GG-0s75wt$VTkc#RH7k+Z-#EpWzA1V{=K zqfBAvPh^`WrTtG69S;uL8eWifa?Eg8Fgiv4ipV!{&=C4g6#5jdAdn!z%Kc7B?@oj( zkRfFOp5Wo0u|mfS!o9gOwYL>ToaYNV5*nNAp+-AOz%N#QBeq8sLM_e?fyzZC;tMjZ|GOOFd~ORVG;K!$bFA#{@5l>PRKK$SZj`JSAw7z2AdxP{IqXfju; z)O)|oIav~VKrq|Q@@gQ$1S03CV+ZcYSCYm*#P=mS2sw^LVG}}p$Hlpe)28!XS33+F z9qUP&CA?7bex{Ws6&rt2Eq!Yk-;#RXT6tzG(k}AFH1eRsg=De$68aT}LB^~cc6NH) zt|j203Isze{(OF#DpA*S=Nq6MvV)Y`b($-vHjd&JpnIw!2Pw8_nz<|e;&07RjTzEapFQ-SBP&=vYw@ted{|A@l5FF( zz9>fD1>@hL$RM-SXA4TttXk%PjcXm8b2hxN&MMYKmCrlE@5nJ_dXea9?{-IV8!Hmj z10&p=W~|8@J$@9UTRW7TQF5Vl3QG~0+Ks=<&Zx)FXY(!V>%13=%7M2*jKHSx{_VoY zUli-=eP;oRP|0r~TT6R)sX$}D5tKM1knbt@JRRnic)lK&V-S`dOVNeU;Lu%eR&SWm zPu)R0HG+^55A6+_kpua0;P6f^4kfs5X6%Ge3u|VnCbGkZh9FLs25Aj4F{()}_ON{N%r7+(@ozTG6_SCgm6Mfgp`E?ec0y!Pq>PbAY1wkLd`Hd- ziR?O?#-ubBiw}3Z-H-MqTQZ7S-V;d^Bq4GVnzm*dT>i8I^J-eeW|&&1$llk`fyvRj z9s`LpYpEQeFt78DWXSSnb};*W7LPXIL+5h{nPq%fo-I`qxsk7WinEvS(8r|II(1fW zs4WK0YdJOcX{dU6j|yUYLb*>Kz&k73oq}LwN^9NuPQpf`%U`CtXjBTI;ps+K4n-22Bl3Y)U7f=rkiO;sY$(eK3 zbpnV~^X@mqOkE&Jm&LHdV-scZCx|G=Ny0DzZy$@?_%*BUt@k0N*uzD*OWLea13-?S zGfpdlc^j`ZnFp^94_*8?u%Vm7uwwQ7eenfpdR=Uo^d})9p(%q+9R@awpZL`GJ=72rl?{Tg)ezJ>4m%^&$CU>iD5O>)R^I|arX@OZd#(_$EXM4OI9U`1?F*j9i;R@ zIDU**srr#g^~Ws}zQ>*Gj0zDg6UL$r`klvWWtIuUCA;>+shJfH6JcM2lDaX>+s!7k z$?Lk67gC9vnRibTy^|Iwd=L=2Z}-f~iX9X)=ypEX`!Jsnh9D5-KltnQdPy1>QLqLR z@fMlX*BB%@2lp4~Yr(lNuf60=u$v`hShUB$wK+aAbx<1kCi?P}f^<{TOSz7ci#&Ob zV>XEZ$DP?TkI7|6(D6wOzLH)+4qtE>E;^FzH|8yr=KG^+DL8Rgbf#MjJ#YE^IRAZ> z^>MA+66gDYC|7WJ;QcCadW0gff4lC{1LQAu*G}!*=9>~VPVTC3akxDcuUg!6^KQ+O z4On850pMx#tcbHmop}6Drx_1jT3DVYF|k_!6iPtBsc#}%W0FBu2Cx%aK7Yd-8*T^( zs#@7<2_c0}*(%`(TL=g9*k$?${^8=xdF$!%z6!m9fWVwg9$}nXLf9hFY+43wxwt|ii_P5|Q3aEqAE@T*$B5L8YRHBhD97{L zUiZjZcgf3iRCn!7{{?tLR#{o)#il@40&{2O<~tGBCEGx#7s}e@m-eyedO+!4`5rKByrg}{qD4p&XAc;ID{SH2PGHofdeo+K6 zrBZXIZBUwJj~Rh!@blGwb3K)$zkR7IqFB^uN1It2&rkQ!m;O}0*VonEZ!h;cbrrag zpAwIK7;*wMYdB9F!hMQy5@>XY^&5`ohp$L~Rl7Cp7c(^{rXl2qi9nxMf(HE~HFz7S=%o z>AzC-d&?@=B4dtsr^(PX9-o!gt9h}LMXlS% zeAZwY?cpUxCv$#wY@De!c~%PQhh;yai}bpvg&&GLXt(_V4pKmdQxw7cfhs@H))p%% z9g(S+SC*TJTJG!aQ02FUP&W=8&i)T`e;F3l_r8InG=c&u79gP#(n?B$NP{#82#9od z$0ih%l9a`z`hME5w@bf*tbN%0*7w3g|q5B$!z1OVuJoj^lpv)&4 zu{UYPOpj&pQA>#H3n+dmtN9rhTS&xA)GYezp~N9|KSj#vRV%FUJrRrZZ0_Y9hMW|Y1ed0l}t8le)T!TqQb=^%2A9} z+uL~)#yl78kEp*-)lB>!rr1P@v-gHBRS;4W38!kitt)d_Hv#w5>?p#NtBq*!F|iP$ zh|sI8?>wFCwi@9-$^}DSv6qywK5;a-1t0+r#X_c*{(G*JK52duUSIFOx4P8-rCALM zeQ8@^Lnn@h>h&5yG3uQeFCV?cU!H=+TTN6T_O7z3!?*;JmzwQ0g8wN{p=azF_{ys} z>-%rGg?1xg-$kl8m&k(m91N(vw*0hqe8A|xO1J*e8=Sud!hK(2nUhAvofSPT*7>*f z>ebk+G&!XM%RcoGQR^dkZ8F`BJ#xMf;B{zKSGnc73Aa(dMK9ti+`p{{1bq5k1ocH& z)&n^Tmj2qg@~15+W``S)P^RhM{^e%md6%aXa?y~xxZHayf=vfPW$5cWk_*Xoe-=l* zbMI)2`P@k%#f#rX zOgdpGHFZv`V%6^o2b{Z0LMIdYUVI})7GN~|VTKwmKJDwX(pM1C`SwN5XKlj^?_ZDyEQUgx-!E{%2w;!DlMP zLnO9f#AAd+dY`UWM>0LCB4&ycfM2sv731+nNYWp=DXhe_v!)TG}_NI`V+_jVv+j~B1E=GJE)wT4skChLRzsiTdiO~9<9f3{TAu4?qF zP<`hQek4hghXgD89Sx`>RiHX?Y-}?|{}tl*g^Spmr+(8AYBKaEV1`wyu4W4SH3u2o ziRY$M{?pm+c_fs^A#v+@iu$V9mT4XHt$CTR^~AYhirqK)j2jQ>5Z-J4DT0tt7e_?u ze!StXYx{59x-KixQcZW6Bu$nK|Kw)khlDUCmrJw?0=LOtdImK9FNbqY(;FO6dP|&4 zK2H}MWzO}}gW0TxSGaS#fK8n=(6Ca*>F2pvCpNzM?7rl(u%^`>%bC|sF?r5XI$TKh z_G^+i5DEMEq$et~&Qoc3eEQ=q;N|6=Kj4y@95C&*+bs>< z&BV8EEQxzeDdJP#rPS%$F#K$(kT{Yp0ee!9Mm_DdN})b`&C2>C1|NNP61exg!9C2s z5!hHiZRag^ca%6WjSG~-^fsm>2JW!U9cXY}2mbScqHQ07zA3X?+>pV9(++{EEMy~P zX=2|S?b7U)-xSvX_mnJu(HcZ|#T3eZl^aSnpG}Cd)9BJ|@F0@4d253(hnKW^xnStW{V8D$?k#VpQ2A8w$7q z7d{aIHg>v=uV|=b(sBDG3!mhX@c(h@S?b9*JrMF}(GNt`>-uQBN-tcqqAiFPIn|+< zT+9^wZ%?P;!5t`|n@g1ua78i#vIYV7Dwt>bTZzmceuA`ca`F1rcl#jr!_8@1 zyebK2Essf&Tx?&B^Jn7aC1;cC>t-_{cir4ptF&LPx$ZLc7uCfqA=ZCs29tv}aU2cQ zo5Vj8^T!!z)Tgf0aJL0#a`LDoYvgSNWPT&-N7ia)G}SL^#9aP09gIFOCCKq8&RA{1 z7l}`MFU`=1;?K4^-Cq-;;I##~2l7*8V<{&)JkSaN3)xQXQ7U@4*y;tZwXB@KQZBBp znhLKp752r=mCp}uE)V9AoUP=AGpo$GGpNoGGl--JKzeb7#$9`lYdYgg^P3JY>! z0->5CI2NIJee6SA)Yjf|bdy4EMD7Z#v~03B(-hW|_$a90@Fa{P$89?vd)4^#tC;mD zqfXTqFxN94U)vM#e)du|^3vnn?+F?ok`TcUE@@!(E~khflcuES7chJ=(==q8^J?*B z6iuXQXpG`#G?(LM+AmzkzUkEZ0L44ipJJ`j(X`RhqwgbL zqpb>QT*`M?wK7cN5rjRN3T_nAM9R)wE6n3`@K~2x3>TD$E`qxKX;6?2%*$K4%4?I*L``|6!g<_f ze=OI8|Cv=BezL&gVpF28!<-wB_6A^&WJKS|R zfN)-#IjnQhad<`5_J1412hpCj-gxE1?QQ#e7`NtV_UEDg=02$o2EdnQX}4EkMNJh+ zxtfnwDQt;1pHq#Wv(!GXO{?7y7pA1|j4OS@ej2=DT>C#(`uz>l6T2UD4=>cF7+5!M z_Hk~zdw3MyW>4FflUSO0lW1+6{jw>m{|%Q{n1P=glTw2kN;pdNJWd8YWl{O7TbdAC z?t6WuV-V^a8@}1_$hOM*)ZgMdEN@tqd(}xCcYUJXeXwhbbeMBRv_O+NhPU*<7S1@W zfYNJGOfmBJ2dFI@zv9f%I=bxu=;kwKB>Oxv ze$v3i2e`XrF2iba;y(43#GQ4dv!_>B?AH!{SFYk%ZZ4C#%sccI-6L4S{0;DcSZj-> z13hk4eNZqz(mevmad(*l=uT=Ck|b;I6IZ19HuToH&1BryhihOB4Sb8(oOC68AOoE^ zMHPFf!}c^gQhKqsQ~49*(T|1}49md|d-)TO^2GVYwHlZorYHcgIaRe+F){$lERYPt>6H-6 zFrz-w%b@4hEeqTs$@8ZDg>J}{gjt_Wqf42`eQUj%$}ONkHP&lPG)?eJK$kafgx^m0%xnvkG@?sKebrr|!SvZ1(`Ad+~H zKCC;j;Xbb(7E;Gn_^6;aRe-^^YLmu#tby?a=A9&9+fd;Rg9B)3*k^aQ*P@*j{xcpe zUAj7v#-^)nj_yB?g$)KYiF?i!~vgZkNWd-k8BArC0tCX3|-VbNp zS!pwfDzh^8hraB>KIdHd$q0tee~0EXIBwkNv+P_2zU=BaHsie0QM0XzI8^A-IikwD z&S`x_9Guqk)19cK%1OwY3_bZrHQcw9%!l`HC>%SEmeBYKda(ZP4UBk#0GdwLF%g@NR+!g``Yh9Bte!B9C0sQ2ZHwYjb90_qv$lDfR0y{$-s z7X#m=Ya6O!*$0vOr{bu7%YE$lTe>DQ?u?K<$Fn9OhT=rT9PSMpI$N;h z+*R~{k@g1!`GF4)jU;ZSaHDmoM#PUBb}SirX3+i9U-!tKnSEVSHc`;2;PQK-HSD0@ zIyAONf=a$EU1}|2F!5@4YfwWQOi1VQsG(0KOrN9_`_Eps9(Hx>gQ7+rBWwc$meZdl z8Td+AKH?>X$iY>z49Sl5$!dLaZgYC`K@XY0J+iqNZHKiDxaY!FrbKYt;-hrjn}`k= z1eI#RYL}X*bf?k$b6xZxId!}W&*ao-i)MMth5?WP^wGtRo4dOVu>P6@bZiF|iMr8Z zRUM@5-s1v|thhv3o%k$)slaPU{WoNn=1gG!9;+}n29?RcR|9eL=?1ua0x;j*e0SAh zwuJ~iJhl4$RSYl)A3*xR%H~_^PmMq{E)gsO@4l%o)hkGnLbQ5DMKCM3|CcXY`PfUP z#>}OE8eKJ@=aRm@>bNVTMGT<4k4It)tDMtl8jbNzCus_DQ#b%;0Ey|VV=5qbJR6{Q zkuHnD;UP3jJAz7@z2JNGa_u#UTqdphmI zes^45)g@UPIH!j}X^-%S0w~WNV#| z;iGUSYUkB-qD|Uaj)!*iJU*7H)j0AwUrR*4^6y(S?NU}2jwNPy^Yx08&Um1br5-db zlvDisX9)Qe$Rq2hKJ%bP`2bKNmNsRn7?aE9ARQU(1 z;|z9j>3utJm8viD$sAe^3ti?et7QIFHskcBLo5=t=jq=3L-NpZ4q>22<#XKJTi1vp zt{z95>^FX+DWV#@na*L4Q2Fu-dJ0^-VsEj%eic`k zb3cmClgCmwAOF`t3!H&XHlCm;rzNPdJURgIoJckR6ONGt|s&3JgIxsF)XKqAVAV9330*IvA|WJ*vJesEgr;-kn> z;Agz7_O*V)whWqYOVh;_6H2DsIeUMV!>vsT-Qb=7Ar=`7n*9_=dAU|!$k2S&ert|A zJ5~`B!bYd7FINNGSCyHy_usFIxbHyOWLHMBWk_oPXCsuHTQ-I4`rZ2b4Rv`NHax+Fh~PbiQuRULI;ViI>oWXQ>Uq;7 z?!g-3kp~Y9klgA#Rn7IUU0sS)q2zaZ0vlddU8aL~#Iv&}4!13MS{(4}>+2)F*=J3h z*#49Q2%y4@65+lpLlrde85LJIgem=fZ~;3nXI!%pfBODk&z?}mZh4WgVJkEf**}QL zURu;uTCdJUp`*~(_9oT_TSmOrfwCNfrZV;uP|siI#oe3E1_S%J)`;{E#8*0E1vEd9 zMWz0w0ihN{|SmtK&Mopt1Q`E!dfvKwe0gOezOhuUDDKv2Hh@AWFx8@moYHEG= zMo*H#yQ=wpa^-euyPeAeL8N=Kj=iKq0k#{g8Nl1Sv6xx2WNnHl$N9=*Orek&1LsYp zlGl9wa-l7f1K6X$5BAZt&uKB)QXH+!&4OG!*~RW;O()ba7zr3{PQfkA2OAZf_jU}x zu^zi%!6SYX!s7AcrsQvbZ!KV@FYX*|efnU=MJ9SW4qPb4lIM;Z>STJbrMf6ieHb=*oUh|3sCeQ;9eJs|gx%W_0s_-sp@ zv#`TwIt2L|$R=f?1o`1Ay$uTIWRH# zy+6+!W843^J^sXX%7`RhS{^A*1_L$8ICgy-ldr^jQB-@m@yHcmkEA8`;K-*zM@N)> zdj2uS)_6Jphc?8$upTY`O0U{>q(e?zHt%5GSv=(}uDShb%sUovX1WrRKo@ z{*}mj`QIrddjDo$XG6M>XU~vy@81;Q*0qc1hXhH5bWT`8w5KK!K=f!__Ah%3CsR?h z_ddW{tooV7mk@*JLxdy%PJ=X=1+$fl7;eRSS>)i|1foiZkrFzAATRoeq0l9My51y- z9WL{1EQlOd5&wv*3eeL2v510$|Hn7%XU@R(+3So|nI$})Yg;ZFkcN-4M(e21hov&+ z>b8HV{oj>W-m!g>VD(OZX8(5AcWZok8j=IEW{F;3GVPPa^kxqGznUhRi32&$JAYi( z`aqHK9m_*h=RM)=zY{DY>V21 zq+@YDBl?_1AJ%W&{W5C?I=@7aYfoWf{0Bu~_VFu;W3B!1|4r0)^#4cHmpU`Le;M$Ce);RBxE&Dxv!0Q#V04MEjMm#TdE5_)e){Q- zINPH>Kmdx;>c-)+MPeaffM;}UXqoSfCNG!|ef0yplGlK-cf*3}lF|CFytsJXf0UwV zzfbpGh=vk!=(}`_xWGn~@r2HO)=RyB=D%BXS=3-){UQ8G!i!IK!F|3aO4Ea; z#hS7c-4?$v9>3xR`imdD5AiSAto9l}vG~l>JpbOs_gDU)l^P8<_%4~#A7Fr(_X{B> z{_EmaDOgKQ#4~Fa@pW?$IM>3PgW<kXnmwqT#IK_&FT6RSOnj+BHX;+y7Ei+7^e zt^jhx3$z3ln@*J$%U+Sj{_oEn@X7n{zui1MWYKDr45Q!|G#dm6}Hnmid4{p7)C+7h?o*#dkP zK?D0=%u=ZrG+zAcN?7k0cR2qK&-eeEvW?5SUk2gIRTs;# z|DoO1j2Hk|#42JW{s&m2FTW1M0&JW};J4PYCi7TG5>2DKp+P8t;g@hnI^oG@AQ558 zRTMwIT-Eq*hBh>B6#Rf!vNXsiDMq+y{~r~BZwLV`02O~($U9qOZKo9) zo&FHGT(?)g#ff^27P3`d*y%q8lz((c>>Bq`oFXO@VF;Ss{q}C?KNtTiZ_w7(aJ=-3 zM|t(S-BWPw>otGnix2<;oHei*%SUH8FJGFC9$Xxv`=l`BLi}+B4i@qOxtHVx!=JF} z7n8HAe+3J>O)sGLlI5QOE@pH2o_}fMf8_vtCJ>feWM~D!H6RWk1=s#! zyElCKuCFxdfuqHfNEppQXM)~>i)()4X4L|Xg#1i@mK0!_kUqQq^<5M2yFP~N{FNP) zX3c;$!2#Svh;^bDI9+$Ugv)ve>*TOx^Uq~lGXfm^TLE9ZgLNUTwL^2-!@Bx>3Z%DN zz*`32b`mU2*C2UW8(lkfJ>+J%Vs1~ow3?}Ey}^^2W@8SGz1zlvTaU)*oP2}y9vs%v7naKJr%Ob`t2nbvmUpb7d~ z!fLQ@w^_5%5uxJ-9us7OhXHIrD$uw8F@ZJu!6nPdh(~XOWP&e%@c-#{DZjPkMKXaM z1y~aQzxbgK|NEhQul+hf0`7cg{5+6w{8=Dk76(|vt2#tb^!F&z7h1l^pvR;t7oWEO z`-Kg?R6F&blV2yrs$3n3FXXmp2XrE-Lbu87kqmoM#zPA_t$HK#?2Z|l=*R?8hy~Mi z1N!E&o^%Artzc@@{P4rzDd2oDH8l9_0Q2c*x{6Gfxl;f8F*8(H1hXofXzuE{$o)5k zF(aLF4X6#B?Y+Hguwakexaw*5(K)hbZ+z7XUNd%`l~n9TKS=MGIzI(udn^v0wfL9n z-a|`I%bmx0p!P_iI?&P~3JlwBonxUTnZ#7OJ8>e&Pq_-@QYe|n|H>xb+d$7zFj0e2 zilRHP8@w~X^6wP4yN!9;z@)nvA$%lSsBsP*Y+$_%-XWSfdNesXP;hhL3RC7=jGIg} zA7npf`UZETOA-($;{@Eq#+JxYR#t{EZ%C-fEK6n!&>}Z?sbFi9>l^D+-ISiLjq$x@ zFwV^(R}-Eo?*(^D{L14z#dIEPjrIAT?W9#c$*ok;3*AY0+_qn1qqI+(L1N!rp8KxR zdQxTiVLBvu4Hp=Ix{z?&vH`$&JM@t1gU6TpJYC0bZk7BuVxTV%eQk;?gfxqo02z3% z0Bz28skCV)YVOyrOEIlsiabBsfRXe9_Y+MS3}a4%uRPcLwL zl6r5L!;X*>(Jx#RCok%1J29FsSiU`}LTGAzbzM%xCiG01U%3CljE&wi=sRaGm>^(} zC|HrA5Y@dKI?#fAV!+fM@Vy%CiT+DYe$!tvBq$ivp>aC4HCj~(b@k-j_3Tt5O%DOT z6r8TowOG*lGp6+xh{>7xc5EPTY)lgGtxqv$(os$%&pgpqYyi2xch#+0ec7Y|*&WKWwBFvxU%9uwQRrAtfL94$_KH0>of^KVdWR?_HcUb`8gxFwb)~Qe9sZ-^p97^==Mqqz4YCA1YFI7kfJw zSas%NABY<-d_jOUcCXP-KGJ0FBD@s5DOx$dXw+IXqwB<@W6Mcex+JcsAI_Vf)X>y# zxvMVZXb1(cL%$MY1lKOlPN%*JVAx}$CdB$EgO;Ij*mN!ZtZ27lzWD7pNS^^pC?F2- zmV>0S{t^NefRDcK)@`qzbV;57|1Nf(e_P+E@r%e~(;nSVh!EcKMi9I%7eG#D^`vkA z{p{k)^`o)7ap(+m;7x>qB_k&at24upl27!B03RySDHP3=t1x4Q2++;E9&>hkK9cZ7 zMB8FYO?<<$PZTt?3<#KUbgF=ByVY*ZerIWJ8z%U>jAz|wfPq@3{U+VnNtN~Fn9(WY zdLrO1NgygMtI^yy z-x(L}v^>~u;y4vbCZHDw;>v-aQJki}r-B~YGn$ejqYLK-bV7!2!y=nbmAuc{w$$Jw z@fX#fffH^#u=w3+nQ@#^TswB?(O_jK!43SVtY7I?1~4{R^LbO7`9W!rLG@4Y|xKG0dAfzQ1n zYYy&Xrm@Ob=?&*|yrfsSjzidU>W?G0{7#dP3d#4LLET%NOR*1lj-;@oh12&wf?%ER zoY2PWnrzAam4Pa6{A0uRoBbs{ROb6%Uqh45Y5dpz^#`JMSSJmq8+Dt2G%dci$fECP z?%PG_I`II|E#g)dgr1efb`P3Lj?L|YjQ3o|!8QFrujA|&vw;hn!tEM`M%f;MfXRi< zhM=)J{pe7OOtO6FdKY=ZEJgU={Sl=J#~NEw{`?#l?fE^&A@W>s`zPrR@Q&{UrYC3< zxX4j`NEhwM&*ydYb#JL(9QfTcL+AUN_eaUc)$n*TCHyM5Vz_7YBTP zR&yBkOY*rdH1=sg&#nJG3{jdpSc)Q($|`xXbYqmmi_wi+b?+egcp~zl%pAGJyycFe z(wH`&Bc98O5Gmu{DAdtRgEkP--$kZi7NQX*2mN5vw2%I^^XYIdBWO37x|=?fnjw~1 zy?nQx(<2n!8801stDjj8Gx6EY_@)k_=6xdlAt>eKM0`$_GTkn%1LS84HUFRc3wF?G zCiTKLz!SGFKV6T~v8nlmhvmKf>n=LzEAg3qCj2ux5srsbDU6&u8)V4(*!o|!DJIZa zok?H7ynj0#B{xi%$G_1%VXChpKs9F zHp8IE!IQ+N*0N0kMgDv#AnUubz-2T+VEPbXFaW{faXpTSO|~443MI>$7Aw)8s9y+2{>0-`gc@jO6sWv%{bEaR+D2qp3U{qQOb z$%~H$t&ShiShV9)d%c;?LR-`T{Sikx=78j`8}fM3IAEFl&#$}s67Ht8+AFRy;Dz1M!KuWH)AEN zp>H5A<;)zb{%+C%k@fBX8}x=b@gVZ#aB5X3dD@_QoQ?~-S9wr3DK1Yyc-a-+^KLP1 z{UM!PQi*{=vOE)M=OOKLs1 zrzsoHG@n=j%%OUpU|+sd&}KC;?Ix$1?DuDk1KCX{IET9zp@a{`|CGXi0ti;&t~A^NB4VPf6JH{YD_n~lX1?hf%ke?Z18;{l9i1AHQ|Ix zr_+rQX~I#sxJr)PT{~g=3!H~m*Y7u^`Vu30G2L4On3u8x1skvJ5b69!;)qs@;xUq? zcZXB)ywi{mgGx*(o}(F~)kDAOd)wL-O(}3sR3wOXmgC=*bZ=dErmc(^>bZJ{t-{%f zuf}m9s@8dP=)1H8g{H^;G6RERT7~O9iKK)k#l3Lki1m2Lf!1bGy};+tYwpHM***jC zRU#^Wd9?Nd?%@;ws=`2qCs`{vuI^M<-+6|6 zb!`w#8QR9PBwOyis4!|*F+X(+l3uP|^g4u324S){((cPQ7BvJP3Y&e6aXohKx8;ET zhDAnk%iTd6=*JXrSRGj&-0$~bZ|LJz!BDd5HQ03KxNw#JTv-6jsB*DwWGMkgbimJYG`&1>qxVf~=Bh zI5vdc=l5yZ+^HP@dQq1^pfKt5=I)Ako}2u2m2iGffQ0n&a7532t_W@)&2kv8a3w$8 zZvIFulb4qsTyc-+QzDOHAZ6Yl)}+yV*OG~jLdHQ5eHcZ1w4!r4?>oNLJICt6+=yV{ zfyLMyNE~UU1=CBMlUzO^HC$ZNjl`Sye1ehV|4&uY1R zey6zo-t~A48eL}q?9YG4-yr#Rd&)Y*L-pR}oC_1jA7pQ)ONNmnX41jHczh-u#Mqsr zYUNdeOh>$H+-7}ERY5|)(R~NxmY$|Eyr$27%3Gp;xFvqDc@&H3 zxZ;@8w6AbmdQE+YHPwISpI}vwhMNW!P;Yq>&WG8DM`B z*VLG>_InTcEOC$bg8&ud^J%CPrkftmsPFxN41U5CQCn=0OJ=h&<(@a;W{4|ekfQPB zY|v=zSQ(ud*DGimxBbg#`Jj;sK#T@D>_O&N5MX06=}Mt%ji8QKhHM+?V!L&fhYQui zYTbGrbv5Em(_vZx7KyXD=MAeITpy%3PnO)vdX4Up3z}t^Qkl-oBeAWwB5J2!7v)Y^ zsEAoS87-ghG>#-_>KCZz`f4_j;QEl#)zrs(h$MD1Y36CFCdGR$Re$pCr1MIlTp^Er zo-nofxiQPq{WR8W1897R+amzmwKkMCf&*;;gsmNeN(9c(dTNJwSn~o^%U#44GO|%M>3x2-NX&Pt|I;(K zl<1k=T7}n9v&E_A%Glb#Qf|~h6AsRNHV+v3?vD?GmCm~Hjq zFO-6QYb{KvNAQt?PEZvrh8q#I*-%u%IkuUVY|ZVX>EQn3xBaZ~Y_7sU?!KW)Uxne? zbfdlx7nvnbqP<7-M4Pg0N9Xy;$!mG9O1U*eU2BQrczB6qKZYw2#uzZextVFEldT$j z8)9C$wsYxK18ifjnB`imBa6Vue`iF;_LtIW#~g6%MwEO9l?gps7szEjrUuM;9CMy0 zkQlSQmSz$jY)773mSb9{@rYOX?53vFrHJ*9E}&fhW?P+DB%OD@@lj>%|Mr;x&*Cvc z{*J^&*&#Y`_sS{;&HQLx4%C2L(4nw;x;FfW!{O)Z1IpNA`teXa)Dop8zpd_Iae^vW zewI5dm*XMM*q&l5nq)-^ZjMniNhx$NQQTwArrDmusC~ z57pHnpX^%nW85SXtv^XXwat;!-a$V!K#)8h)Y#Yj8 zf>RBtP6xG*6ytLSA^oM1Nf+qO{x^@(#b`x_b^+T)GFDM(I2SC^gu!Ec?{=wH^jp=J zi5p!W`x}-p9qxQhML?yaUcdjblka}A^;;@*{py!Z*H@&;PGM9y>GLb*xOFtG@nogw zfUSVq=ji)Es!tvPQei2I$(XP}x8c*dfc;5|SEE%?_q7Vhs96M)*LZfLdFLdCj^pjh z%>sK}Op0r2)eg$-32e?y#|i(f4zNVs%Ej>=Nr$<{OFr|`@2??6bad-R1}L&BoIL*= zXKYltz=~T+e)mFB5tO8Gv30OyW4ZLmO42kh)8pW3b^(^p8fh%dDc0=Wngqt0b^m(_Yy za1*&>01~)Xu>eb{I6xu|s@EBtU(Q#~LhAz2R7S0OH~Y5ED?wyC3DfuXOFh!l$}Oj` zlF6j0*^{OV&SF&;B#+c*p{YuUPre;RaZ9Orj%Y|UsQ zOl%0M%%1eDKAQSEtW#-q10)b=N#aG%FU4uMN`4#k)sT zHIKn#bInU^9tK@a3?%HY$_CY zrYLzOrzk{tRJI~G#k-R&q+>g7oadD2R8Ntu?{0&3EBMygtlEIUG*?|z+#{**wq4zH zM|yWEZk--*`S>E}lW!2E?PzXrh;T>}A^U5`Xa-R^4Y|zGAG!n#TO^TvhlhbgX>4^~ zIe{ayNa!H>k?~K-lu(I9aG=n_P5g{ny=Wxx74u zYCtl?MSZg;ZvA#c?ecgk$2vh6funK(&R6OL3=cI<3lf*P0zSn71$&DHnN3d&qc^$x zOdQ7!09`m-e~a&|ovY`#2~X>)KU*D!BFGy{#~;CkU(r;rM7}E&SR+3@2;4&$9KBYfd$H`pK&Gl5K#Un7~k zUALK>oc+^YPqE&)#2ZEHubOQ|_|W&Wh$bo=zA6Kub$g%M`(BN>*f;uk&rCPIg`K@i zVaPdFC~BK@o)7Q0?GSJ5j!y>pFB9}n9_Kpr(Fz>R#(v)U)m_~=L9Ez5QrZ~1n~Q(z z-yV4L%IP&xsH3gw!9OWqN0?o|;&N^ps-4PTaq=bQ$-TQ?|I&G%TEs zdIf$nLb6WyOpO|)&#ol1H6Vo?WRX+uN<3@)EP1`h>6&@ndi4mTv2?QRiaIl!B2WI~ z(S=}L?5JL%x3vEnCt7z*>!OoA2-XWS%aHD;sr@RAfEaJIta8fNPLNM<=X-HglIZH7 zXuRdsTU%NZw$cO3W0VNu!TCDZkG(Dg>jOe5!By+AEcQ8;p|%TMWr^wD)`q#EiE}*y zHiz8pi{FGi4`uUudVlvWpiK};dWC0URF5uEOi`NJCYC|ccX10)ye&(F-lK)5C*zSW zs2s$*7MD#%>y6-V-vt)(nC8Vrq2WTp+hIMQkv?}_a zWBdAmz?WX(!C$Z|3iDqk6^z@Xv^u8j0!+OgW7o(tHF)GcU`*hEkXYmIHJ>wHW?Awq%Jofi&k>!&1zjvn*#5yn(rZ{_BYP?4sAK(zv|P^}MAfR>Bm=yl zOW?!Lu=8tG#q@o(DoJd4Sg~HYVTRG>RSduo^Fh_F7v<14&4`!xb)cjo>Ixz}>5+?*5(C*m{dqn`?m# zYbaj}mYZ68l$W0UcZ>+rf<9g5Ztpomolg?@lg94M5E|)EA5C}Bp3vA|r47cQp)1iS zE(Fv1UXveWoHq3nqZxj4op*2e{@J|#EbP`;_rSU{w$$!QygkG^UL}D;#Me25N4dUX z4zQU-ftomXEMIiC9{`kK-r+!pJcf5~Idz|c$LbcmsFi@rzLvO1uh^qWl*H7#foeB7 z<942iyZ#`&b$L>D%=lGCCS#asQcSae>pBO*l{ld#Ror3u?F-)5sD$HL?YD$u{qvrq z{dH%CI_2(=@d*NMS3$bQ$CJqz36A z51;jY_O>8*mCMi0cELchh>2lEYQ0?3AG%Q-M|jF zI;9vj$a@whcrxKx&nZ+UNF{U!;>$4@W{Ndl2sPj7S$djlS%rI0!fO3#urb{yjiEHc zO`KokJjThb-uw4Prp}_>?+qG?iy_PPQY^HmV&|yUJA2k1juL`Tf7nY_za{N1w#WUk zC`og^)U^7FKD^bJ98pnRXnjiTvN~Sj(>3+@PDjZ*PaOrXDmf#lE1}<{OW0-Y+ZqS2 zvu@a5BdlJFa(+u*v#I&!Lx#3rS2f3a>+{fP;b}py0o?s-b2R1rCK1I8STYF($E%e| z&xqdI(2HdN>XB)z?f$A*l}r@zlIME6rPYa`rpJb~o@dV#`l-ms(J13$nDZ*9rmZ|~ z)~9{|Gl6>Js&Wk^8Z%CrlfpGa@D938xACFjxA*lsaWLRE7OeVZKk(Yjo@^$Znxc7 z=>7DkgG4VNh&WnNIQ3R5H{HbAUHHSwt8)=;@-qseflvnChJOHV)Vro>#YA_diBI3{x$Dw$op{+hsvZb{i@3;vEta;IGMajexz+Z zTlIvOV>Uk;!M|uf5^x;=eTDzSWb0|>Dy7;zXjEqJHr%( z5<_u+JEPzH@)cST39BSy%l3BIk*zNcdodB!gJB)wT z>X;(PL-&S<3S1_OsXf)wmIsatt%AmxCjqD4G}7-xaWtcN0Lqa=G@k6QAi6}r{)V=` zZ3nrrU=q|;?D#w2=S6oeT7ENMH7|T}8mJ~Tz_{oAtIfKD%91eJc@WwvVK`g;^bHUx z=bCoQIW!X)2aHXHwlg|oS!QEHSeb*EAvMXiQN&P zeJ>XJ2X@hDU9Ioi=hLd$QEXpw4p4tW^YL>reZx2%n6{aYk0H-i*^_JdEbdV0ho>u~ zvFvXao7xa|{k3%Ho2?kHobS~9eNtU}F9l}MEa^UBghWpmmLlQ|r`>frVy|w%totYw zB`#02sE@u=eLmyX)$P2~UXP%wcUV3NY;5 z{6q&Kyok@|xTK_%oWN#E%;>N=sj`D?i8^f|6zdkJyLkRvccq;$MUyzH6*tp(;N>Rc zTJ>`zjU7Ng{}o|VA!eMo9K3qaDdK;eBzDThu9V@MT{EE`=t_AG`PxUGHozu* z%}?}+@CvmngN=Gp5UQ0*pZx(jS{YI6oNRq$e^vqUgm(3xFiH-l{g>^xA>LQuRxdcd|R-G$^E~*2yeKt@#vS^H-5sL5Dw$N;@K9f#wxQ|dzNonegE;Ee(CXF zwoJPvBy)26o4H%idB$u}elO_bx4D-K!%mm;(&s@MtMF(eurV2z&L*X{>3^t_dQNXA z9kF~@-{-b@{b6*--A5<(>}pRul1w)}G#!6;J04kV;ICeUH+|#BO2XU(1C|&N{qU0? z`x72<7?qIrp3Pcby3fhVTHAc(hKPqOJ@TtI)%_>BP*T01!c_9EX6Sd30@a*Pbxe0- ztNDjx-J7Z7gh@sp9xWD9Nof2>EejXDe3Ar2;WedV*m666enwceShq5)WSL|Sm8@2R z@u?n2m$wKKKi>pcA;+6O=AGt=8kZ5kV3w%;&OS8Hw}1DnywBw{=er1)U8X`_C)-ju zsAtavFUyAO_%Xn@uLpc2%~8n-Rw=R4N`4?yEfJ6bw(Kq!=PK`3h`7cM=`W-1r8JPIxHwAt z;6M!e=2h!>cX_Hfxm{antV9L+3CytEcXpb@s(Llys;hs3bR@@8c&B}&;!0l`pa6Hv zpe`J6uS$y@HzxSBj=&txqC@Sp-lNhYa)GFt0UVlts`RbU%0XYzA6L*3!Y$#H%plCT z^4dMKx=W56&^1o>K3}4thpcC&$}eN~8>6 zjFnr64dj+fvS|pGiMwVVkocbeUp?t7>Q6`;SCw>R6Tft1gnzuen7$t2Kq)_}MJX@Z z14aU@bh0sD6Kf;x^SvEEXbGiYCx_~@5a0DT#z&{VZ*KaUYiz6`VD9v*3U9^^Dv_H+ zJCm-{ojtmBYh2baPg>dJDAU~QcopNNJ-vdK;{_EVCpPSY?4BruB?^l*R}^^$LS%bH z)f4{FV9M{dz44hQ7j*ktVLm7c65>s4j8Ni)x0!BLbiN}XMTdq%u|OC35f6*5%Ax;^ zzRL8^LxdH_`P$JRshaCXic|>M*qEDP6teca@!LC_h99k`5qcLJejn~lYYZi@+0GZ_ z@pr7!!xS?z_w}fS=y)GUwdq_Zv^K)GcHmNWVKL$N^TDT{?1R*&hJj|TZ`k2`QA8e8!d~C95Bk>& z4ynu+r~&5I8c0MTd$=(PW&^8V@s7_LY$BHbT1ErNH93WURUpK0DC}_*ZwMFHq$kAv zMOlS#pzsQGAIeuR6itWo>mP1bgg^Q|4?GCvpv1tQCI(~weu`PJ;O=-7|uzy zT>6wt-u!*`bE~3tR}gRGBzf$0pMOm~eKs|c{6)5(!unX%4fruj$$Z}*aX{~g&+?O8 zr-B~lOB^f@t5VsJ@fA5MajHO54QHF#fvXoufB2xBbYq4cZg=A|{*0CGg@G+^#E@c=(i0ozGM=5Le zWf=Ruj(uPM_o(mhoa;Id&XfQD!MV;u<}&kf-*dn3_wrgUNX@AB|ESuXyUa$;Xc_N- zK{zza_Q^d@B}~o#nfyh54Q}SiF!2!dP#Ly6)tsU#6;kIkkN+LDS1`w;w_Z%ToL(s( z8AbJlYeh;bwK-yTC(YBX?n48!u@w}{S*H256cICPZ?`1FMHLfx^@I#)ON0eZh3jX) zJH~f}tp2Q8_XN*e(Mra;R`rYwiSJzJj2d604rLq%P z3F+SNsqhC7q`C+nO-F#Pl}HO3987; zo#)`e*R~-6o7#eo)y?_I{CJwl*qhB0>3c%JYl$-2>R(Wy(}%tLa-0gTr`~{%dlYon z+}~d1I5_&GxU^G|L2%RJLMo+d?c7Jcn4q0XWEOgKk2s8TnO!f?JY0AHvO!iSo^63> z*9SyXMPko(?8-8jOlkmh3H%!b&wgB(;=Q*Cpk$4fr+R`>tlSU#_NQDmM{K~yei_Lk6fh>37Z;{nG~@An5p zZXxK@(3EpW-_^_PSNdcsU)IEvx}qACYGay@$R_%VG{$m?N$%-fRi5rIu*xS~(GJ5( zZ8HizY;&=XQi$%W$)TNg_;sPZ%11I<+r*2pKxXE8JKC*swPV#{XkQjXx=Vwj1 z&D?7(wr^!NJq$rdmD%C^TCAm`Z`pXUvz{YBH#ZO7FV$yPrqKr2i~O_Qnds*I$Z*AA z&Dg#2^_YrUJ3s6Z00~`i6=IB1;qPzFo;(#(Uc=z3S&khvM~cz)npdG`nyI>fR%qbO z9WM3$Ca3FtQRQdXm-l->^T7?$o?0O~H6#q=_qDoOPym7ozGql`Xn!5C5X~rIV$CG% zo_(;=(nrC`fF?=sIE!0(W;4#8nBcKqzLj09g0+&g(|Bn|{!?e98by{m9a4B7cy{=j zIG0sU;=3ymldj87`s{>*o8`34wmwUEV6&5DRNPYJJnJdRvsvOeo`T$2wX;{L5LwFF z*%yaOU&2e>uPCY5I!7WEIzL*y*0ExD$PDs;S3Vb6nn_`=I3=NjxqG&9!O0njX6-y_?^exhP?%V08l;= z2_goFLSAVfwg&GrUDp4=uZ4-u(N6##RI9w(Z=)K~>Z=wS0$yQp?}`>1`PS~%VEBmj z6|ctIg7#GLV|oAM=1>;wU3xbE1w7w+YHo%e%wkSEs}4i>>-0$ip-QM`n&BcwMFoQ> ztJDO*GVAB$j{9QGeGizfd;xs#j<32r7A{F1nl|s58Oz2IeF_3&6bef;t@?SdsdlY+ zf3pn=u&hn5#s9kcx3#R#-FJe-ao}I4mjpF&#Q{i?1sz<Ot-u{EgIgfA;2#)V|al zMCl}3S={R6QaRxzV@sqmD@IchLr8nF_{YGj^O0iYk1z(PT93JHGZUy_^(i+0$$MT$ z3(&g3=B@E2)Ui3rk|na&&NVNLlaqXDig^$yon;R=Qk{hc&lJAe5>=AJl>ok+k4_@< zU!4vv7OfH1$SaT7l-EV$8na(GsW-Y9+P1ZZ@SRmSr7L6jP2|5+=D6}DfuXr6U=(U6 z3fQ~|V+w*FUlrPayx+_jelgW67gpT$p7EVrqM#rL+a|I3Na+SZ=syoAzkC1pCQ4!q z^^ua<>u^2E7=mPZ(6Ls$kQ#Ml`rgzJvd_l@BqeTh@MpL9pTB)grxwW{VqD>Hp*_t? zlk##8k*CAQ*kA-$I}_gfHHf8iqL0rP_1C^7dQb3Ejq-+HjFWl`zxkKiK!R!m5P9n6 zAUxXf6ikSU=*{ueRi^j+Uk1-D2EgEv5VbxQ%%=uX70`XUN1r_ZM-2jsK#6S?UqgyO^_OAsln;pQKjNU3q9F1jsQC{M2AV1Xr3{Qq9 zT3Ikurf4|O0T(nbS?(UDD?084asN26{U>r{B5PTQdd7kiUYx=m&Q307UjIaKJPibk z>kC)*qW=hw?JOX?@WbIyiS>-7V$HF2`GYS(w4_gY_4E5Qh7@&pZOne&nY#2 zmWvL0GplSVcd5aIKEt>Hopk=Q|x-%0)M0~`Vw^q(eS{E(_oXD zxV@*iV}M}!hyODl4VhmI7m8UBCCM^LBh_mA0w6zM7f<#O|`*@Ku;^&hCXLWOS5f zs14{yMDcLldE1WL=x>GaKj;(Qr>N<8Z+@yJj&ksd9d(?7H%5wG`lccC- zCx}O9TfaZk_)7x=9Bid?PukVa|D~di5ed61?3are|J^8-Ae<@lJSG2o;k;7?@#>dz zR`314TaZM&oEs>HT9pd}F=rhR3RT)|3T5`PoQcAN?_; zj;5-xmA#FzX?f*~u{`!1Dtq;}#9evKFj9D96auo``_waA_YRN;30AG8kKeS48)(FD z)ar9zf$zjEeb=+7`X!`v!~2=Mle&HR_$r=|O^=&&&~mq_J#xs{HalTMOapQ#LCATm z62@{_11*X!w88a>;)#0)klNvM-I-019Gqy7nw!P^sB4Q`szN`l@o%c~KEkI%z?UWb zQ&ly^@}51t*lOk0?5#SeC@yc0;#t=U6np>!#K2`8Ev3wSFSDkR7mA{Gbu z0CDG=_zRFo0UZ#_);chSIceOM8^qjj`Sx3itZ~ghu>!fs<)QMHPPH&&h6ow=u{>X+ z>GXWVAm#&pGwqm*(BTgHxPiuD@@=Hh>G>P#j*J%?>41=tmeE=V^uF$5(Yr)cu3ShV zpT>u96q#vJ)AIF5Tz#qHAuo?g=!bz)r>2r?O953Ub?NS*UWz<^%YH9)nh%$ze-nOFWO-*gKC;YhXtdbQxPNtLe-Rx;<;WPV0H6&CS1(L_In5>eun{wyl*C+| z!-k(th8kDp9+FDq9maO?1lxSJrcsQ81jA+oN#g5`mvBm68`9>E<%el4c`kjcYP7(E z`1`m^1;vRF_7x1c3;6GIOgEs-v?9>EXTk?cotvCnT_Qk?YlvIIqQSU~mk8TdoC-x` z>>}C45pgr;HUp)HE&(asHADXh%3r(=5IB%lomGCe_yWPldLibAE1-jneuKT6FeFcQ zShCrwxTndSMQD#2wfR-*T(xv}9JBo8w~X|O0U4c7gq_7bt`ws!&UP^@;Px1;>*Rha zRt@<8^u1;C?|902ZDz@JjXmuHGUEeYt%FVvLRk3|ox_m(4QZ>E^F>l>Id`GS#)zpE zAE%M(o3mR^5V!~eT$Xx5M!4+R-!@MNZ~#)yyKr6!nN9=8aj>IXd`%_yPOOV-_zNVw zcb&(nVm9g{k|W|zdY;AM>@4~mO=xkic^Y_#$-4ur0wEA5X%lEh3YTV=+Lvr?Hfq7I zPvtZl74^pEzih#XjMqV-5m|=$GLby;t1OCVPwM=9^rUiUVQIjDsq-bpE-gE*2&sUr zOKDv%p=1|F#r-JC^vazs*b!+YKF{Y*LuldgTl5aeGy*xfgDsn7-@*A-6(8lIBDw4C z+!LUb$JqAg2Iw2z($6*(!5KS;xFA=l(w&Xq?O8PQM~6l}Q1o6hQf7rGMjEPWZWDjH z-)pH&yupiA!@j$XBjr}e1dYFjXRqB`d)blKoh&u-tl|Ba``M4XE!XBKo29P#{PcRS z;O)sGJHez5{Ek~agYR&M`=-h+tlAoYE{xmT*`2hQl=aZ>fEyZ1l0Q6=?WPi>qI$;j zMZm-KhlYW`J7mr|VpmyN11L6tF>xKT$5G{_X2K7abvY+3_eW~3^IvZ7ZQd`u`-1Is3Ta|;U1*_TZ^GQbZ;7(dC|3T<#$8EftPf!x*Akw_pL$C5 zOd+}MpwP%*rq-#1|CX%FLt>RWCR}af7Dr{M6ivA=XCHqWKvIp%TpEf7K^mmq2%$v; zW;@a%oB1!dMSmZE(Ey~vn^(JWrjZ5|w9BQF$l9%jpY>N69a_4zbzDEZdP7Yy@UxNW z@=n~MijhJB@qs#Cpl$&h)3C)s*cCl{nK)T^S$3rOTSQ#1sdPc-NjY-O)3>TaKssnz z?)qTx5iRZx-AQ@QOmGKxY!7Q&zTlLqpYcd{UDO~aZWTXo zU!OGWlh1-$_kX&yx;Lh1>#MwB-7N<^6->xHSf#Bm0iUdhn0$-9(kST9CVw#WhXzDOV<(KqrIk|J=PI|xrUp{aZ! zhS?UvwCEDmJia*AysgS0XRni_5BGm}h3eOaoU7D0N9RFu6@?k#v8@hmuS6_nbOqaX zYb{SbhvOR%oyyI^=0#R}n?c)ELV>*}tz<6q6ybt*QYqaNAkawXQWgi;+qv=i<-%g z~{NwQThC(^skhc67sZNSU4Szg-T#S)KiV z-{sRM<>LkRmb#?k4gQSLD{*^JX-b+U76Jn#K7dXwzEv&-Pq_oFj3K(#S?D(^)0T$v z`xTlK6ce^)n#NcA>*P}}pErB4Jmy# z#BIVc4Aq;{W|UBEuM7fvN(w-pYF=W6#XYV^h6wStd1a zPltAm%nQe=lD}fwcIn_XDs=z;tcXq4Y^~IB(4lJY{R`u(F#I5nC)MZdB^);x;L=c9n?oLiU_@YX1HL#Du(pdKu4=3snfqk)AkwTzD5Crk>z>0jo~|dund0 zAx&0cv=89s^}XI!p4<*z%KqU{LM?-`F7xht);IKZt*kRjhb{E+{hXX?z+Zr$VJu?; zU8v>KYsdSsm-51#4rW(4I+HQrPC(dPH)goWzVDuOi{zf%Se_-|j?&3Ej3my+LpGni zlT7$d2(_$VcdlY+#*dq9#$?ex2Bp}vyzDb$e8FbB!BOp+R;_=78PY2M_UXA{MSMlD z5;r62+ERrG!!V>1Pw2QlVvC_t3zCxH0I(wq69GQ^qAWvi?Jz4|+#StZnr^qbbEebn zYPx1S38@oJu?87>8-rTO`X!Z63!K;{H8RMD1K7Kd&fd))P|K}vp$dG4P68p?C*swg zq*#pN-76{wEXz(M13xX4R%#GQrJr(BjSp+6%VXnQwiY+NlwbN!x?=77a2i@~4rf)l z?xw%zdtP{n7;}Ex3H#ez)b|fR7K3|q3M{*g?90>QsK$>%4G>_R;cX`{-UDWW-la6E zY4*M69oO!5l_6`xgnq6$Fgn1xCBBJh0E8y~jx-)*G@O?`Uk08;=?HkgL}QxFk>&F( zH@tIxgv`H1cqSACgIM?P6gUq)K8|&Y|HV2G>MoM&XBl7ECCg%N4nF(!VJ;_h&E&zy z9Ft;&``wyoY=PxDG2N!$8ua607J>V}RmenlH@zD4xt8xM_BTK3^6&^%x&+%jQHGh# zxRiQh7ARm53~lEmtmJBj$XW(Z;9YfXsd0B~yP^di5^t?C1p-DR$n=IM&CX@kUk?E7 zgfO8Pb?47`m)7$fL4o?|j!6iIw7@8sdT<3$ayzAJxgU}3thHioaY!9kWCu$#53HSB z;@-141GDJUc6v!#&MKOi^-_*5JFAcnXjpraXL+ve0uceb+EB;vC6Nji z2zMMWG{O;xH_{hVg$2Sdw zLrbS{D%+FhSM0H4$5+gAo>sZfloA!WD0qzry!Re9pU_{d;{y_hPdSyh+_61gtrNAb zw{KJyU#j^`Syw;}JuGk2ql9ZFe4p_Sd1=12MhcHlLc>m2;C0Lfvpe3#)u~l_x zc5e$>3VWEQgbJ{$iaSj=#Bj#6g?hBhF}1P_N(BB!ZHHBNz=W=!gnJq}jak-z(F;FbbNQA%rJT6ZX9E>+*PVN1wK0e#mN@4j zkLIoTDdUqTs2P`odvZ<XWI#7V;CCRCs8!6zXpB%?Nx>?LgF2g zGFAp?NNEOl8-^g|Ee?4!TxsSWY!OW2wQ!)qGBEGT$0aU z6%V6Cjar7F(q$00ClouGg163;HbsdP2RVwV&M+KAEc%x^e*rV;j&h4LOWoieub^HS zs_b{6&+p9{pL#5LkTbvieH`%^vG!#j7z3=%S|l`ed}%W8p3|FBEgt~lJyvWLhs@zNCf_25@OP$9)ua4QCh2wJwzG|j-&IY`qmgNV z^JD;6370`aPDfZ(X#zz%9A{Q`NF12+1)g)uSPM|fB2U$hy@LN37o{`^&90Z2N|&7m zp(xQ+uc0Ow3qOXz@vDz)=aNIOFQH7E1zGYeafwenx?+!F7UIm`* zZA|sG+#;31f?*#sVdttn?Wik8FYzx!)reUsaDM8jg0r=Aeez~1CWfFz4YVT_p96p^ z?2Kg!B3xaUDsHdyn?S#hgAh~}taIt_6M*=0i^F4h9vBH1pL$NORIN-Z@TzOJjIS-J z69o8}tQg3v0@$;k_yV*$#cVclVwTHv9-rr(lWr=rPKZCB5^n#xlzK+tiA6h?pBal> z7eZ2e;^WQu@*Sek1JNOo(C^lJg}xDe*=Bmp^h^yP6cc?g01M(zIyi;`J9^` zFbNmayX&#v5W0${vU3jfS?L9UJ>(jJ9curOD!M^n5b+JQ`Pn{pP2t3tXFTV+r-%-E zv(csk3)lAA@m9(uJ;;l%%{)~}OX%;z?R_7w)3EBa)cfMZ*S>Y0FsEDYZ`+?*n06Qt zk-Q-Ja4nT}RV~w>Ft@%t@a<`G){$3bsfuW$EAHcW)Sd5IK?HlQ^9k$Z>-!kFbAs?y0upKIv|COLj|x2t58~7J zyqDzNf{F^ag+S=~^0UMNgsun8QVM(X_9?2lLq(Op`dWTN`xkbl;k$s%`vh8X?fh&6 z-$DAgb$L{lb+orvu`!^wpIC9lx?I@ zXst)MIE6Vh_1Yj<)ZJ+6B1uB;Z4{C+x5#kP2Lx5uOGCr&OBGetMfQ7>!_)v;f3@n+R5ej1!AS(@3$29?`;zHa1@mnON=!3|iIPW-K2M|L~(|Y$Bt$J6}ZLE%Z3E-CA+d(qs8>Z?e4!rGP=r zkLxHv9|2q|mLeM^b^amt&(^}As!zQ1aF5#|NbdMq6kGzDIVXb0=}}){*!{eFh(-Jh z)nZQ>hnfiez9ps3* zM8%188x^-iT8i?$_7hu{09fnk}8V7h+c-Rkag$H5tD%d|7~e}1_$|9zI3 zMJKIaYd6n)bBFoDMy>*w!~lK$Gaug&trf`x#FE(4eHqZJM`b}K<=WbDa#nfLABx;A z&q#;>ibb!_4?(?*6uCW3iyPYW3Y{Asv4rUmoLOX^w6Q1@D@;db#$sC@(14cC6}VU5 zQVmi5y7i>7%W8G;_0~dMcZPnUVaC3Q${tr$slG_x_vii|S%OF6Kj1*LXZ;~Aiw_Wh z?mRBGv2WebX@<~IDqDjBlbSt3OFtz?vwwW+huOuY#pM55lvmnPCUZ}UQlVKAM>#PO zg%|oxD`I|b)lxIbnN5WOEMcYRR$=}IVNe5%7Hs^zqO0%0XRN!veJ-#%_?jY-J}3Yh zW(A54@O8}g&0mFVuHJI0N+;spCIhXsdXC~@PP*Sv$b79K>2x{IkLR~em2BV%Jx*~4 z(-;R{cOnjQOve|uxSju^VU#b?sCsd;1{zx>n*bgXv=)D*_|%%7T{r#AM2 zllH%K8k?YEvB?`7b~xCG&+d}??YT9?15jpqfOLN$P5YA=%esI44g1R3^klLs`~;8Q zGPL#!heY~p`Td@8XW9m-kESS{4MFy?!uq}7d8hHR_K$7aTs)FYW4hHi8;7fzL9%9D z(?5i7&u?SGY%V^M-jUvU)Lfh^$*a9Zq`Ywt;l z$z}%NzXB5OPay5hU~e5f>o6|Y3~YuhoL_lxIm{6?S`ce9F?}{l4+0>ChhJlG(U<-3 z$`guvo9dNbixGRQUBwJNIYz)UjYy`sZThaBGKHbQrV^{4op&=?11*Bi&=WE0u>+OZX#3gf|B8|O8Tn-C4xt|**Ph>>Wxq5xD~Zf@+4)Vhu@oFS}-l7~@= zjP%%mY|wEt!RBX#T5laARk>^r7*jRqR$IwIvQ~K;aq?bL+&@11`O9?z?Kc7o?Mo%B zgUeET69ud5>_CG#h0u5mJYwfoGM_cJ7C_hYIgaYy^W8jgsjqS0Ux^Xbs>vK-cQpSL zd(|N4q56=QTb%i|^yI-ym_Aj?8*kc=t^l{wN=y2Pgfs=V$rsn&q%x2I zHtv)P35uIME{nkI`l8f?zVYc&r}1T4<#nQPDC22PmI)0?uzF<0hl179vWW?H^>LBUQe zjDcj0t>alK|E;kkCwB5@vO3O_!S&>BR|7~A4^Hw7opkcw=KKa7`OGRlNgNCUHv(#zyYoj2KhaTmR*_j_8V(#GPa*HVAL;%C2;eODsl0{t~f z!kM*j^O-4Sazp9UnY7S2U4T#`sbcPF79soCYH<-QE&Tv#)v60%dJ^axhty1FCL@xm%%6ioUxIqbGp%>t>U8R~Lbm zGK24n!Cf1Rm=>>^(HaA>w(Fhlcb?$aS)2gR_tc6*5OaROd9#c76eWbBVUudCNe$Zg#7}uiHI#jIbZ_ZhB+Ec^_Oy6aEJpZp z6*;Y`olV!|xzLWGXgXq^6_(2rTY=6T?D2jI^DXGgFnkcm48YZcyP|Eb43-?AIoSa{ z#Tfm~_2QKFeoNA;)qS!8I+Ww}_W{eq*Ox-%VfOvLp6Kf~2w%)aVolaL-IB`ma_C4^>xw!VrBeLV6pHvcan&R`N`kt?@?xDWLzVYIN zUYqZA1e9G)tv#$0cv0OXxG`bNy)%DFFCekfhl{P<>FV zTZpV-+!pEd#!&ey@z5<2d#1N~N{Zsh-M(q&aWJ}!3}muD&00&me}@}oh7TI94#EGG zE!YDvQuvc@Dx2M76#N1qpq|<3Ih4q!`hb^&vbjP*^XH&?8brC_UyDrM}@M6$KmgD zFQ!{e4l^3QN)P9CN`$x&jO}lo~mqRNUAw+HFWmA zP7{r^F_%j<07>?nZ`<(Lk_>&j;Kx^~KF(P<(V8$Ex(m<<$=dI|A#@#6iLt=r6DmOW z$?TlSd(hkHKRCm;7iNSD5p{2IGZP9968*O^xd#(q`t(zVxG{^wjTsE*dmUE&%8dCg zkzN&>De>*7GfeS47)JURppdm^-UT%psE>dQdv)T$rxYD5+WZ$=^1j`hAutx"Kh zMv(o6$L;6HLa;l7OHZG^It5G6#F(R#;Nh-znH*hb2P^E5+2IJ!|L0?j9m)XBQ0W%{ z&^mH^1Y|I04;=ND6x_?!#8)?@ha0bT>IE^=H14cs3a;KNUflEmvc`VeKlautJdP!K z%8HLP4i=#Os>AZN(0;-U*VN&8tGu=SB77Bg;ZDXM2J&DfuFmFgDNZY^;hb!mhiy6Y zu!C%qbMoU2g;PNRQ^~UK%i-!ktg!M%-Rw)1T!za0g`IQd9#!Fr9dl1h56k8kJjo6~ z2Vc$K>N9nTYrH8Cuk0w=c#hb>q^HfF5^bc2^npyovt~@BFidL0Y=`@6qDE;Zu{}58 z85D@knQqnfCeJznS+$)elMv-khS+r*x!8IQB)WHt&&8uFj}}5UuV1o`ZTaDM zk_Rr6u)^cG%&U0<)Y}&L`uzUvy(eRL`g^JzJNb1~kj+^Eb7B=dTs<* zhJ}@Bth7h9PH%l}lyiHaWLEbn7HTq*uz%T4>StILi__}bXxHY_dp&(fK44kX{-XRi zA5U5o#oc)rvF+s(vNOu#3CXM!D?|ZRr-nyQa(6y|Hrb=i^RA=KOr*JsaRqW&dvZdo zBUhj9KLUKav8F(aS7D@t2j%StX95+f4yG=}lGdwA-msqUfp=`9PVzx|84p)&(JlOz z;}k@1gNkc&TJ6DC%#czI6i(Z*2`iK`u&^RK9^jnj5KplYEn*~PxZbpH>3Z}i?@qk; zqDSnT#+YazsCeCVHD%ud#uk_4&5XV7^t#!6GI(vHUUF39jiV!z!VU(!L&{`tUSjO{ZF(xy zIc>V{=3FDaNfeNnYEBSk<=yT18gcmY1yYQ$tyO4)RieOasL0i3c5`g)f?$cqpv>hd zq#LjMX_dr9`3(TLl>hN?qf5{nm)JUK#e@DYyq^&`7tH~^^XDqW z9z*F~PPyE85^c%g@jm|q`<$rSIwa=23E1L;t~?En-$_ZoCk9!s=!z z_P-6Q{3BzA&^tfn&XRn%+L8r8nO9CNNPEM#xURNQ+;fL!>tn?IALiMl896NOnZIZ?Q+XsQIj0}*LR5< zC(dVnQZ3^@IrL;Tc#}#4pTv1EgE_qR?A;2_Rgf2Y9QmDj0T2=q`b6}1yB!wz`Cai? zn+lO=#xeX)kIs(DvZv_A+z{OUYd50mLhk9n?xevoJD16V&bm1|-u{>YJE-Qr7B268 zx)#a3pRxHW#8%Tc_#Of<7c)PInfNdXpWi)W^BvWd^O5Kn(_g$4{&T9X;@On;v=R%? zvwYgAo7_@Bd-&6P4*v09i-TTw#3^Q7K@uI+D=d`a0Y<@NcQ-!{%IUZoW@>p30l6{t z(md&a)B&vkC+OF}N|DcjCj-(&JxBbeD(H8r1$5_ynhTFixiSdxfYo`{6{h|#+wI&` z`P3pw4(zrl6u`%&vC@*uaG!;zXA>coGs4?$dimHWC8D>AxbLq3O?%vQxI5bs(`Od=Ww4>?Y-- zU~nLjr3E;xcD|Du)udkw(M9MBelR`??P}5)8S+T_X1f0K{4h?3 ze)^G=my)u8oZa`amlc|n5xmm~_#yOmBxx7l|DQgBS^EivaG`+SvYicBZ4z~5?K?TQ Hp1k^B*a)DT delta 129765 zcmd@5byt+{_XZ3D(xE6S(ioJKv@|Nx-5t{1jN~QQGz>V>NJ@9j5F*ks^w3BSHH>t_ zbD^K_?|xpwz1IEDtTk)RoO8{&>)6LWc2fv>`W|VSlLJ8ZKv!)3stQ+ok36Z zS+A!;;(C`>dh2<}@aGjdy(H*uHtRh4aWZoCO<9B-bs#FFM;;ev$Ne^fM;)_42DV=M zV+r~umghjV;4=+LreImub&YL^~LUEYY z6W=B2aL$|9l@Z!l`;+Qj+p_b7f2t+zgGq2dR8S_nn`R#JLc`xBidp)18uGlo+4 zC~2>)hO-Lq%H4Xa47KB*o&i0MacuH;kCFHy+;dg>!6G4A)I9C`?c;Th9)2C(3Au*x zTABD8`^)O4bHeFFQ44FkGUh0eq{H`wc8vjF|@+zU{BRDJyfPCDi^zD-NYarGUt=$JW->oS8}Gm zK3bgoqmz#*nwa?w4jwTxC0op7Nj0Wpbj}w@u?U8d9PF2Dsh5>DZptar)R@Z?R#J2{ z^0b_|rVItMNLVk5+E0%5Ct94VtoYApR$J*QY6S%W++MML7o0_Mx3dh-@W$jx5R8Mo&6iPI)SPOSuk7 z(&9XPV=Q3qA`zxHWwuv+lS|zcCe3mWgRdy*Th;jJ!kEN`_o5*zkq8%`QU)jFb;uEW z?2(72koRTKAsqj5oYVZf#k2bTm1H0_tN$zS`)foJ^S90xQ|t*#1SWJJ>lIl<4)L~c zsC>8sgb`NP5nYbMhJR<~rPHp`3;FMTl|=-@hXOT&fDwcgs*uy9+%xA)(CS0=7U`YG z3g*zZcE+>C>F+cHuHVyw*A+ROD@Yt@L|hx3r)p+seb0u)PmF8qEhR^PaP$G~^Tpo> zs~m=ly9_I=WA#w&L}RN8st(1{(@y!Oh%XWYi@{Fg!_t^r&Yc<0`V}9lw6mUXODA=Q z0hc~{DE*a3L6T=j=hmD~AG~mxu1TtBz$A}4qzV~_zu0O}lER>bp9Oi#Zn~ln3uCU5_VDd6vULxxnEOFuGzT(dYvW}Nf&#c&lD%T` z`%P-?(m{>!+;Q@>Iy|gdjP+_2bvDPITufmd77a~j`KV1%uObV^RG`*2l2%Ik#-1eq z>w#gtv6|WsjrUPz0g*kdq38AJ3eVj~LaF*cw`U=TlbqAi1un~02kt_Z6=INCa0euHlGBs}^ zd0GIB8}e<8Tsn`(P7#vme%y6X4?Mbu;ef{Ey~)BSBYgUx)FB)rc4}9@o`cTTLlR|g zd%ei+vrG3k>CuEbOq*RVG2?_y!?%@S_urbFA3Whe6JWIh|);`-6Gi z#h|ShChTk>iL{^O*Dk7S(GX7aOt2jr%z(iWKNfiyA8Yb+sww zh315TgIlYGVaGRRaB%U5;4&xnM1StQ&N>~-cou}_S>c0}Z$#@fDYX((q(Ez3AQVv^ zYVLIQ&OimzBR#>o?~Vy|I`ByrROZX@K%UG*Oa8CCMV}LRRVkyh_5#a3CTMnSPY-9A z#Xy>ClZDn&jziBz-;t2mR@#*$3QO|*U~5sa{k6cNTvQ|{rYgYcz={)4jw*(frjfw^ zlp_mU{qN_^R5}c*C{8}PtIG1?`nZ&fp07X@%BUwrvxwz#jlLecjmd->0Nt6mM~ zq0h$+gV&_p9?03KVw?8*`+UOZ+F$~@ z%0kQ@AtTShAm(YDvRs$3QfJ+~oZPeGk&3}G&kTY zW(FG!<{^D49@5&LEZe z3OBALnYL;QyH2m(1H7e#g-oND`x$E0CohX@Exs^0&j1e1yIVHHD}DW%QNP*H#Jty* zyW5`TInUB>&_{j1rLgdf*TgnsH&1%`c=Dq^H@5pUwmF;rb~zk4x>;g1IPyZoVrwE` z8gaQk@hvL5mLSl6pf_}M8{6h`8r419%4Yg>LyzlL+>gX=7J#hIxno-vam|Ytaf1Ou zb4Pomdb^u%yXB^I&sQ2w>*bXo&m-AD3+);%nwOVa@W1hxTI)Qqo>KfOC#>Jn{MuHC zbd=wV?RvC9trV;6Emd@ad*dM{ZJsgez{}`+KX;214*}^fz>HCP>%{MCip%>2A$soI zE)Oe1&?^AYHy-F-Wg-OK>2MTP7?!^UK0A6&(pC*aXlF~QZ&v5;M~&3L;`)+UEqsu( zi?iqY#UJ}3{nr+g-wWB>qRt5V_vxqLs%lBeyPG-g=kWU^DP7q61M;=WJ>EnsW@?+B zjrX~tix~!`u%$Ch@d@b>vFDt6-85d6DOuKlAWi2!Z;tX4GcXWDlP1l)fm>cAKBvlM zlY7HEc6p0FG#GTIy#reYrfoZH@#f!pii;mel6_X%*W^eQ>fkrCWv}Q=<>%Qkhnr>k zfuM36@qWBXa-QLFh^sQz_VY|dTfITP7E5Yt6JdA!XJlxVsIYKLQKD+CR≷P-9g) zQR`26wc)d`vw8yUkZB3&!0{+;Ud!{oXbvH4_@^lb3=_vN0;SUNFC9Nee0ivQtK4~*A(w9jrUe$w-}+Wnx{ z5QcfTYhVx4OXLTE2sMw`Mm7+_Ylk$a?3?sj*faFN-YW8$xtCaOU3pf?(e!OCI$+Fu|^0yj!2- zUfSiDJaK^}RRiehZ7LEtKIc?T^)I#b`==^X30d|c7B*AW&>;M2o5>*M8Co~BdpXhJu4 z0|43@A`!nNMu$a5R0)f^w5W!e|FUkO44#Q* zEAHRo!Ic3@KHvE2b>rTUO>)xl!(!|H_-6p&^t6oGy~8|Y0Y=)hoAmWiMxN#zFBQgj zvpqK;!&(<2@>BUq1d4#G<` zy#h(!LhcIWy-g13N_Sy}q=~yWt2Ui@7L63@i?kH%b!hdG3m#2;^E{^z={}QQq|>|A zOaJTUywu$^IIeN9C<6JjdfPN$jYp(_7DkinA%uF9CizjVJJ-~MBN^rvfh5oFx!nyl zef*`f^zBVR-_=D^*VWB%6zk6*7Hsq(P)fZit+E|!lWE!h_T#PK_D9vT-_E9$9aA6J zh#u}L)yBf5=kDo?m&ejsIB+)_KCR5v!HJh{oev9J`M}U z+ZNINm$RQg{2g4J8ejZw2chCw3h@D+`(YA*+okr#tkc0PNo<>h&U14f&m<6cxxDAO ze03jg_-(UBnzpA;H_Bgko3@Ue3h*=RAfF(mjtdO-MPm$Da~KP!JqmT|cYT{hAnjFe z`Ed9k1VtZL+aQ`C@(eq7Ux?bCVf9oN%>DUNt`|b_RkRO&0;AG6#@RWMn35BcI zZa8-S6e_`$(bo+p!zs9*@<6DqN< zIOR$JF>23R`Eb}Yl5;*7 z`|)$jqOEqTC^tR&i}a-lGm_kIY<*J8a$}@~+r`M7t*v+F=kd-8G$L_<_aSu>B65yl zKTZBJJBobY=_!4#Vzq_nzbK|8E`4)a?W^TT)s5xvYP>=d`_=kdy_pV)?xzko14EXr zrk#SH^5EXkQ~-bz;@olbOb{}^KOCIEVRyYS{7FY>5&=7+{{EjoClpbdV^*JYRZY_5 zC!umkiZ45+VqJ3&vg+M!7FJQJapn<)4yyXSno{Umt7^=Ss5w1mp2nWX zPdHuH#L@P@oVN!7#LGu40OvM#wAbVx4`@GTB5p4lF_$Yl|I|-N$6qHv41O zDjxf$Z$5Q58FQ0nCsUz}Z=@4UHGli=Zn7&dEM|UZ^7oS&J*9X&R^lg!HN5TYwF(U| z)t8WA)}Yt&UwoH7#LHKH)Vs0KaBJ%@_|eh$MvPIv1Baih-+E$8|L=6%^V6=NL*Gg$ zoZJa^w}ppepH@wA*n;kVS)GzN^-ZZ0CfiwPWX>ketFiDDs&$pm!#~*k1Te5>C7Uk2 z?-};``cauQZ`2r`GJgbHw}?3JT;r`*k+eq0;bcn+!pWW8(*8BX3U7SFa<{|7FJ39v z?ID)2W`Y^{`ZxaEnIf?dP@I@kdVaKHH~OE8M7%+FMt{mT z^VV64s|)5h(A(%s78YkqAb*Y%Q^J+&Y={;E!T19Gfl!%Ej~?3kbf-TSMpOWH{Fm%wU2_NO9rSAM zgJ=?-rrIe<+%O`$1NVjxFKlS(Z;plj?a*-Wy6zB`0Hf59c&klhc!3jRo%~hkOo@ard3Z@b5_Or8`aOgwkX>fR?ehQwA57EdIh?X zb4@}+2S_PZZNn4oSE`gyv!mRVq3@R0ICR)criA)pymywmVz91t(eLxPB(ng|phjT< z)AtkMz6`d!02?v|y4_q{6CLN6L#EVZLPF0k+AbTav?t+tE$J7Gx4L(Ka{zq0LS!)5YJ^;b@`=fW=%CT##J#gS4vZ;{Mu8p86Iy8#R0SG*SZ zn3_`RbC8u1y<(;28xEAh?6SG3T&2|w{q=$cC*SA)8tpSlPcCWQTt52@t?=D^eag;Z z;Y!(?B%R(B&e5V=N9Ak=Dtbo~@ zS5!NWsW{(k&OKZWPPc9hB&Io&hvV zc@Sd_wj40@``wk8$2mKQ2ru(?ZHw3*Jj=jL3JqjrD(_sMF_08|2JKe-@*cWgmpNC9!;0pXmq9&HJg7~lev+CH0y65!Gy=CLx33J& zDCw^wXaVrC;Q7C0>q+{r`9b_OeVD~}5P74okoRpcQ+tGs+5E)h&G^-eG^I4A^i5eB zRlIjr)xJKB$H4`9FqF=eONB2ZVkS_L%R3p=u6w$@u3x6YafVJKFxIV>30hmtW z_bKqIxy}$sgpoljzf5$2RvXgpe zX;qYGcNN+oTB%U%Ioo)Pi<02s<0Nt!6lB$6bni#V+o94+DzmqE``3$3nO-YYoyt(8 zOe2Jtx&m=Ol^vq-PrhrZGsv$_Z^7M1cf&H3@nG9(_XJxQx^#SVyj;(h{Cy{ zti4-jzy0cPW?A$f6+kTeRi><@8d_oe?ShAj3c9ypI+Jc;9~9750faWQP(6auIPE`ZZM$CwLJIC0PB)l-Cu-AZ|Tx9-eK5rd#@wj8IzcPRM>; zzEI&ZCCtg$_e*h3^ey11Q_5rN1|KZNIOL{}7NR;iyx97phjWd4>lAFUYMeaJRpJ>LrFl|VcA z+!QbItF#@eXd4~7k--+Jjg^X4dV8{;y_KM^Wa`zs|8#S*Q91Wgu+=|xHOg8rC{B9M zBKr1Jo;y=`?x=EScV9^eeH4&-cy~3+FhnYman2{rDmbV&jRi^)_Zg{SnSPg~HL~C~ z%z+hw{5*`7(6d85L(VK)_r|En?KO-|N=K8)ZFkE1AI2G0+J*bx+3RRH9y)F>8Cw3( zpUF{BeLoO8E6G0>XuFQ?t@rY9)faN*LO~+P=qs@PAg0`$BJFU00w6iRy~a}W&@*Km ztbOI%Y2^t-boMGjY172Ur1yIjvt7=~@E76>7YH(;|ypfna9K`3n25kH` zc=Gis|2e)G6&+Ru3cd{IsV5))ys>g-KBwu!_`e#6M-b`@5*7o@YK z2Afps&IJwZE~Pmo`M)^NPoBf9So=t;c3Rcx)lC@%Ryuj!ugki|sFb%yS`XhCUP?I* zUdT@wukIPPSG1d4GHMck&YvTr=Nf&7cwS-B3^?ncSUG0CjXjrX7C&u9Klo-+JN7W? zuj3A!AEID2)R)1W-!L!Db#MPGhG*2wIGq4zsn`0;g{V|!VMQhJJ`?q2A7V z53he}+6akgFKpC}EszCC&iJ4b-CGUMIO_ElT`8U($q6U-KT+(8x&OA-(4-xxb()g< z&?q#`(i)q^YYwB4l;}zouT?p*4J8kBj5({f2)QFG=$qnXzHrbc_oV5pGsjq$=QAA`3^ zgmZ%i>-sz^K56!88Wq-xiF+7)ziI1B!%ExY&ADcCDVpxz|M~W;ThNS;-tYXRIy(sjcGk6v6o+&fUfne4twz>X&q6tumj=*V*i;Q}1}4Qi ztrQuKrHf7W2yq)LObHji6rhL-gv2|~HMO2_YQw8Ka57}pGp0yZoSu&7qLBzfzqDq0 za_d9Od;jqp50sg|f~?g@jSi4(DjdFgR%L=yM?c2`t2vVaG<(wL^`gV>v`bMc$^;gyfv(j<0f5f7L1 z*@*mmd$SzadXgi=-5L%G5=_~QYn(~e*IeG&u6)<8E%B;2=$R~WF-GtHBHTeEdD8>; zLiQh8e7?mZ(Qo!$d9Is^mqRMQ#`cbwKR;Wb-RA}ANdsul3%ye?E!Pvlwbm<(W+3tX^LUH{zpmN9omX+ zu9RG}dymzjkKz!Qo;ptIuDsZ5Gn?s+copwxo3C443?3)xJQ}2bY;BQzZa8rU=bwnA?1iQ?@ z`wEOlAL|BQ#H7XTi+e78*RADd)s}fSEUB=)(3Yc>6Q&0<;4qBVaB~{Y?bADS3OLwG z=uH#|pAFH5>nN5!yYa0Ah|b&3OR$=Em}}*8ZS8+i5j4jq`g$`y1>vp;wmU4ek8}iq z>w^|~RR-7TpIQVlZjP4^`@7jUuifF)Q_#{-d<1MAdpT?GH2e&hOMkd9C&A%DQ{vU5 z=_OL;dt43n5$&x`dY>C zisOF-R=$jqnG>6h{DX6Z%KxJHH>^Vbijnekp7Iq9`ki4SNF(l*m>l7m$o!4IEi8;s zJg4pXl=7LTde<&4_Wbps1$j=ko#zxu9K5-z@ii<9(}tkZc2k>heenPUWq@!4f?G~QVB!nVo5 z1M{H#gp?WJiMY@?sM*!$!A_dEeG_)`1n)7_J)WN!my{Cc_W%1&04OtL+Huf!zE?Nf zg|{(Q3=KLxVDa<2EBhBA{(A{orDE64jfm$mc;!T+rH@;@<@ zY{+!I7m7p&X@?=kUf0%9D}QfXsYEa(j+%!Z(` zS;C{=|2v1c6g+2s-);luadqq?Ww1@R-Y$P~bq>k=9q^on3ugvciw`b4X^1rmB~Cn- z)Ll8)b|)SOaRYeLdsKSyzXy!r0v|B0t>fq4lYn>n;7NZ@Dt+*WB7Pk2qL-Jtpq?OI zC0L2vWXJOj(auxrny9qHWZjcv?jts-b8itNGHH0t*S9Gk9`?rc_uksD=SnZlDf@?; z3Icd3*M@q(*~;IPVNM{ir|SnZnbw3^xj69=TSjS6& zKw$&?Qs(6y@Lu+qUL4?re5ZW+`cDRzVZ|@_>##6VZAJW0t}@5b$Dbo3DRztu8Sn=0 zyC40r+@P1CH1VeAb)*Rlsdhqj=c*=$HoqGCBy*dXUfj1=jL81lErP#R_Q(IiZOn>* zV58DVwYBj>*~=hUvyBs$PleXH#DcO3KX#UTY|#Cwn!0w6!)40w53qA?sn_aa`3y3P z*e3DyN=!J6Yh5F29JS4pK_dv06T${7aUYWigZG$;`J`$7*R({=;4M!)xd&@u!{Z=j zo&=pDtmdIy&|mWrmw^_sZndEN&p`H6pur}i?(M^#>EN-zV*GIlh*)KCVkqAJb28FQ zRnRK-;;%-iufD-S^#8dZ29jmurk=dzV0YhHQWW)Ac&7mO*7|PIa(miyeQu=b{PZP{ zX&k+%r^3PJgyhyA&_h|gR(cup_-_3N5&w}a|ALt{M5D?JzYz2IwoiDj)eF=A8jt@= zJqjGS{5U!BRa3t0faJ51rrZQ?!S(4j7p2woG-<$0*kzK7mffohzdO5T2<{TlbAag5 zzO|VoEatO1Vn}N?s*@WiE!5&I2Q81H0tO#YcKb$IRG9|tVr28@Rv*{T=ayt*H2>=< z{{NnfI8{d02FIwTgxBL&`jS4VafYE9 zoDN-S&e!f4fVNGL6c>x+(d)uSkm#K z^hFG)lGt&3_ICvL|NOXC?2r0gY8Gv|EfS@dALC6wTY$JticR^_%yzU)rz>t}{uG<| zZeZ=KVEdh0Qv$Lhs(PglY}8d!CAw_t72MEY1r8yikgb#=<65&C=b6XLJqcd<((07J z-z)#Wmq0QT!MXe)yY8g`*4zi~hRm6GZ~D_jIm^q9iwf2jzaphzX*Z;P?^>8X1zPGO zMsvO(>$PncKz-SAIB#=2e~wZY)%j^ouUwF`&GrJz&JoqMrQhIecV$StZRO$uu@5oeLHna;G(Ew@<20*6 zLnXZU(qCL2Mkm$<-`~jb%n8%tVPzc*vL(Ela{ixR{gtDhUzWs~Bc$1v#e8FSL`--= z1R9avNr}gZf2)T6^;Zs)EU63Ysyo@54F=`zy7v{QW?U*Ty-EAYI;CdZiakVL_c1Gu zodf8lpYO@yB#njZk=7^mnQ9FAS~(S90O(wpRx8Ox(>&Zjxy6*K)B@JDlzR)iNPF9Q;IQ*k{?AK+x!E4@MG}QD=bZw+AbcEfQJvJvX;%W?W&mq*m#L1`F zdXI9z%Dfhsy@w{gD37kaA@48V;Ztms802D8%HR4QjEW&8xXQ-B#c|%|WVJGnDg4pP zu-kl%Y9lbYT%xd%z+u0~67ie2@IYegM>~i1a__g+3fs-68@vz|!z=V1zcHfe`GuX) zvN}gBmYK2~SEk-`IS$0hT~4Jpps^2u5Avb9Bcso-w*xSdB1ONMRQV(wmx%KX$}_#B zcamMlUcKrzf~5K&u))Ux$DWCMGu94sC8=w7i@teD5&ma9W)~9hYGuuUa~~Q>|EkTY zTI6CsjK|dIWN*#t+@>q8ZBZiPFLe2&5|U3QT&J@+uszJbRXaKcm`^G^Alrv=n6t}@uU zT@NsIZCJAv_1MYoBCJZoVHT(S&%D|9LjW29+fGx+49iIt)a;>20H)e=e^g%?>{N>~ zUbze;ihAX8nbc_zlHX&-HXf#_q&(GJA1hA&5I?Sa1Db&yZKKjyN1I`!68L+ZCg9oXD18> z0kLfIkA6R(d+jYzBcK<6%w)*~0Lja^cz8|D+Jho!y#1`s5L=wf*am50%wzlq!Z-?u zeE7knQ&CBkS@6Ekaq!Ngt`rXKiUF%yXFgK{u2|hd{ht0*M7CBNsaBu&@M!k7ZX8?m zVdtU{2z9{uSu9s#WoM+RYuS3nj9u{=B&XiXDSG6)rl}H;#MBo2Rk&|m&uJkWTjY10 zLE@3Q>et^zUZ*vf23L8+>WN(&X`+Z*ldg1Q;YAqdy25FVOKJ}he?}}SJ&142NLHKM zDg95AQhDafWd^8&VkMr#Vq6}`wE8AW*n~MpMGBqz$%ZACOjM;YsJ@85U|%ghTGR(s z+ORfcsyM*5s|xSbE!68-7jk+8t}tk2`TOK+=hci9BsMfrG^ajQ>^FbP>r+~%^*B*# z71P#O(k05HxL<+#X!#F;Yia$4s!PS1HTr{G>ER3{z2RsfoDsRr+wdo}NC*Y8pkp_r z&zSp?gepVdOxC)k$Bo(;|4lE7l$|kvRO%i^OJ9&|0M76+u*4D^+-3s2k>U-oN5GE4YG1T^si5ngc=;t0m`?-Pnv41NH+uHyHM# z2!g7;o~{hayO$4VlC^rBl89UgkP*#X;iuLQIZ z$*Bj!>J=V~tP==Gdj01U_Q8f{}uosJlzEQCS<}Zd+mJJ}X?u!``Na z7;lhMt(h!z9Q%GBRDqx?r*1+JL8pg3XU9M{Q*O}KP)>O6%=b8xx$?L1PiB5={V!Xo zS*(xbdu|lhhFx3-d|D|)3=FgPQ`XErkkh;}?O0s|vkVVL1xrOsJkl8ObA##7#_w+{ z#T=Js5~0(4X*RLfDky5x#OB9I-Jg~)o*rCtQL%(%TNbOS4Gke3E1Gw?*`D_*fmvz^ zulXyJzTgp?ReQwFGR(Z&rFLe;D_OTd=O*Ct#}Bkbk&4XZpRAxpl!|Re2{MxZubP&5 z0uNkD`f@rsQgLxH6+A$SpLBqdn}953qkPgOfoH*H6%!{tFoD7nos=U2peIO8EEQt6yFzA%VBPMp+w-WPG{WMFDx#W$0i!;BPjuT;4P( z>V3DdYaIiT>$Za-ZdaB%hdDrK_b*QQPqLzm=%zP^H(M255EZqRPhATtOZ7iC-#g7o zm{$;B6b+lby}}Ne4o)U#yq!+a$!ErNgF)=kWbyfjNIFbbYHNB#30G;;SsaRPansko zawZ2NQOBD@+t;eyiU;W;;(*c=idQj!dFH{|)E|^-8d~6LpFh|7)i`CDPR0 zA2{ZE#>{QI{H7pmkURm_XI)9W?S=rYOtxW2x6eAes50QL{T89?7{oY;E5pZUvz$wZ zFn~w~?RDALUz|i`COYDAt4m2L1Rr@MRU0(=`{aBh@gB@bA0OxD->99lE?nzgnB6+; z>EUf;PAzSoMX$qnj9HP*M{z!O4gKGx<7^btpHMHWyIv#80vGmHJ==>N+|3~Mq49F~cTOZAQ>Y>{fNVZ{><<|WyTiF$=wSt=$6p#x zEA7Z;gjx6>7cdR~OB}>s-lRdW^=QV86FF?B64y~j)6B*BX%B~2+8Cm~YNM?A@?r{Y zI(A?~BX{NZ#HBLJamO}4`Y5t5RrhULk{I-!3=}ojcIH=Zi}cr_{#;=o;0gqps|-r$ zQCl+5>BcM&NC0y_=4ohRX6B*YZ$vMzxm8Ua46@c`&shbfayea|s?&?eNY2(qkKSEO z`T7o*!gkGd((nt<>68)N0m_=|l2^-t;-Skypw&1ZV@w!}~?HTZXSD=WP2<`no%dCt}2sRG>+Bz=!Dw6fwrQPZv2;lJ8SnG#;Wp79QZx$K*VRH zH$}Bi_#=-{(u!XT_2(Hjf1RX4AD+MIHy3Gt*T8*XRG*6CqTG1e51p!q(5`iz zDe_sxnopc}%GjF|UEw4dqdN+AaHA{Mc>_D$1+7?yC02NJ>u|DnT4Voods+|M8Va8= zY?Laz_1Btx)XWYJDy;io(naZ4={&igdF{Lhc@_lx)_@!Up&&&d~pU2>?f1^fs>m5741)Phh3yHMG05{R1opZ&aHlu= zD(g_nh3w@TKfz2YU#g{vvI)T2j9O&KP+NPWQIC=#OtFw$uIh_*-N%kTH^<%sz?Wy7`#hMrStNs?a0JFU6H~b-YxyINn){aPe zF{RKKW8?{LCn|wsgo#n`n>3hRTRwwh1*#IYGyUYYgFm# zFGGlm=`IBqwW7BU>$%*3%#L|*K4|K?G`t2+3)sTA+Gv8=ok&j@xPR(N{_row9rFc$ zFPE=T69LYjc1E<-2^u#1cPBu?&gSPk68=}eh-n1vHGXVZ{4Ht4WY+vG&^s!6Oy}Yb zaDpkpeRW zyp6wfxy*d#-jF&E8ph$@!OW9{g1be1Z$@^%)iDDpP}T+ajWL(4aPf4umBWqR#EM>P zih+}+ot^LnMsVx6z_jqo9+n!WnkN}RCVZ{^Prhi$Skkvb#;Ou^FwlFhd3#Aquuqf=8=@gW2@W4D15U;YH zai!TgmT(wzYuLM76-h+ADokmnoQqV;7v@{v$TWC`* zQW;R<%K)0K!&8JX2B0=-&M4Yf$o9sCN0EeiQy~B5KB`E#60IU9@5Ykv@@RG}ZxK9M zQ>acM-1g0Z4*jB#@{e+>4imt3uxRRPJV;`6GQe`w!Kb|_N09>x6%5o-MTQ&qFx%zT zBoP&M+rGUzhuxexE*oQG!^@XN?hz~hHhw|`Iey#kdQ{=E5X|Sm%qk``$SaBh*sliV_aXvt}f449(t=h02yO=LyC(ksVb(~Eb!$) z=@|<3KqZ_%id^c*!CQfmx(EPwW7$cO(?~rN!jC)cj!8UBr(+ic{z?cNitc)ob zuuX?eDguzw8?S$QJq#y2Ga0UUkJ^7^6Y5K*msy9FIuz_w$a7L!EHj)I}72w#xtRP zsi%kppU$RK3vxE}l?&WEZ4vud8x>S!PF+zO5$%z5$zHF|L?z3Yr}NBR;7;t0aL4n- zXld)-XfhVNt%iO>HG1j$;tFCq@Pxa=IxANNiJ^QG!#(GQhJ}Gm`JXL+PI}6 z!|Xz852KF$O4lnGz6Z)&6l(*vzSQ=n{xO=Yc5(7~n_!Z7sdA_vMfVFn>z#!f9R)-t zKI|cl71iiI*ZQm^LcmqlVG$uy zTh?lw?;6k6M+@1)RZDM4lY{m5al%eBIy~DIx=cwd`};eIhO}daRmoJ7vY;^!L5TgW zP6ll`$HOfad6kG?P0N>^Eu&7$ne}|Xj77cv#1tLBO4)~1Ti|kX4jglCd|IQMoO%!u zjR$%U&JdTt3qncm8TcG;vPefa+B_nG@eraOFCr{FCaO-ugv%nPoyZS(P}9&p_4;XD z(KY`KJIKXKDTf##we5=OJPEtY@qkVRXQ1W9N4!br5|etoBxzA_Tjo|eiQ=$G0O#jp zm!`l5CAOi_MB%D}y%?xz52O4fg^Mv;4=*A$i!d zzsiq6z{T5dcd0yW_r^avRz1oPiE(hd)8!(qC1CoI5GUwzWol@|_nM*6jL;Btl;%yG?Ed`?ROJw^)0eL|q)rUP zd{-^D_<`tVvd-c10gL7Kbg{l9mhfqut(>>D|f&q7Y#q?{`~+0)(fw zeZWd@;+rqK!n^OusIAA`n!7;m*QRt$T_dYeNVWP488z=~<6i8x|9qW|vdNc+>ZU+_ zsX{aiq^%wrK2>_v!7^etKW&(B+ihT$V}a>O5U9fIx%XIhJToHYwojRR{8BjJptz~N%qA!nhIQh zU+`#5ZovhSbkq16wC1aGe5Eb&ZeP`+S+KxjH5dh<%ZHxS>sR8S^${lPsRIm<+vwO_ z|9LCJ1bz<{RWmi+m^!^MF6|_VTZ3eQOmY1MCvRl3iJ$tXx>ub)$}@}dp-22a|FE-E zly394{>1UPZ~r2h*zHltpJ*@~(*GG}dFN9S4zupi1=0?g$jPSqA?@bFxdfD%Ul-df zxc9V}n=C$bjpJjqi5U+N^ZtSJ>;mlC8?{t17NQ%Bdy@(a>eW|W%r!zOmaRxu9t!4* zqRtKpBV=VR@)>v1vmbM}K6gB3X*k=r{BJqz7L|Jnk^w#cYvoXAraa2~v-Q)x>g&0G zY2j?O5R(c1oPGwYQc8tfnV!4cDkArMY(Ce|D^-df1_G-s)+Kzw+YjZVeo22G_=ebO zcmZ-;LNvS0Nb6{lG0o^Cp@N=U&>YVdw@RC_qPe0Z>D|3yk^>nxRyzJY(c@YC%hj^1 zrzLLt^+63*V*e-WsC%<`wr+n)5=gPF{B$f<>djo}0?hXBA{g@s9*ycYQX z<5e6o-yvD*OVecB`ue0&{_*5XUjlG&yGr3!I~$xQ0qO^31q_HPzQw^xck%ijGjs~a ziDOeC(>#-`dz)U!w(v)tY zw}Vt^0UJ$<^iY*ZlK|2?8{{ZR6)B-eQJT^TP3bB<^d73xdkZZjB<~LAKF|G(@Ao&p z@6Y=K24j%Dvi6#5&2r6a&UG$D0a)~3e~bikD%>1$l?dxck+m=6{T8dn7E!MAn}sj_ zWl(+K*4=Y6{uoo8d8g>-_iz{o_U^+GorK;K36gfpu?>MI3Tg}-I_Y6V^G*UIYb(~L zy{83cwK8t8$vA!ZbNg`z=0TXBqMEo3%5-SoIn#poeXDE8oC7(a#ZdI9j&6vtWxx{T z_P;!^D#0lMgiO6c+n$Hs4x1o8kTLDQuvVBRznt!{RXO-OGpcQINJr`N%LfVWw-L0~ zT}kB>!PO~B+YCaJVqB$2M69bdCVzN8VPE5GptbxNF!0r^dB7V7!m%HUI7XaR!R)ON z8fk;LvAqu@3NN*@st!25J762tEHOCx9qT(tgS{og_Ps)dypN@ z=RNJJs(Uo+8afH)%eB0>{2#d#MYg!LGI{3s9RM%$Hl1rmc)d4{T?K4v@dYNIUQfB9 zdgeOZoJ#9xq_pr`@{R^1E3T^b{>oW~8dOgA`Cct18mCHU!tdJkvajS{F&HoI(ul9}i8G$QuqY%O`V3r| zbqO;2gH)+eKS6_{-`((e&(^1Zsj#V*Y>)rY2*smWV2`%Hbb)-uu~bJE^HbW32|Z~r z5QLIRX)drU_8l|#8c;%G{5pYZkHd`{rR$eo+y{vjFIJIFj{xpn!^4#LZ2~225wyUd zWWjNiF*E6dr9h)Q{L0x$Fuaf;y?iWG^6fr>fO7%C=?qzl*A&y}^9;Nl=Z7FWS~Amj z5>d>qP`q*;#?8aURzP32X2oKPk!mot`Oh?zM>9!Sui8GNvU(lZaABnTc+cC?R%}*K zpT+sTFVPtR8WC)jOjQ@t2kmG{c6?JXy7!(k7a^g zI!6WlZBSkG0lO~%_5_hO#9Okz;51_SM3GO=o}e|9hiVgmbc7@lPYhA}DR-eNkVTi3 zJGmteH*bj2dTp63C?5VSWs=YzrydMz0BF>|D1)s(ax5mRM?Y-WV@k%P7==xgoomY@ zr2<pkl2-| zbz$Wg{>YL=?Z&uwIc)*LeuwC9zyGvz(h*{f zLq@~?>1pzTH)|G~R5Z!c7eok?+=f`5019SHCF4om+#yZ zDTMxjs25Ue-V6rG8z%X`K*Y!wD5-^Jm67fbOG=jC>1WzoI+fOU)B|VDLV3!*PMt4+yDW$KwaIsA8Zu z&Jn`SV>4OpMMFHExuu`|h+B{{n9=(;!|9zmb(wGeeVm!2fqqBTCxp@8NjQInXPz8O zCdzCwc4;1O6gdOb_J3wuHq;Il7@GeIhh^j_EOli3GTo^E4s#cI0U4{pfJO2l3w8M!FD#9H;ILT|xRCu=*BR0i=|tqd6smXYq|-_FRxB zHJUYJC>78m?{?gAwf)XI?N2T($v?}mc8wnt)J41PF&s6AV`gRV-JjD_@;sT{_fDhX z5)rFEMg}8CMLsGwnHM0b;An-*?;C;yrzW^D+zBuvAi{=co6NuCT+nBR`j&%v;r)c%5sq7jOytTLXGpucl7~a@m;J( ziKC4lMpA4Z{Hg$oK(4@+m+}M7vsI^#pjq$jvd6vm%9gT!WzJnw?O@+ln;^&v&$*FN zb?GZu>hmx(p)p;yvmkzS9i(|I96^>kYdzPCz2yBH+>(WeS2R->I- zQ{QH)0G8GwUlHn+jT1MpVHY{?v}!UUu5L$FzRvVh9BenW;~e5hKK$z3ge&yL*xsEh zma!OAtN|PX(D(N&@(C}~DrX_!%>||pU;;^2 zLS>!P-mGgrkV|VIeW2u_bKLrdQ0-)jVk>R9B~s$RllFuysd`}bFoOdh<<4>O-yQah0!%`c zQ}|SJsMm#XIRegC`r>YH<@vZc4ze})r==Z0unP?9atmoLjjmXZu1UA3v%1W123e#- z#p;Y>0X+32n|-!?NGz$FmpKbwC#^?#yPg}a{`$VUJjGKsvYfD$jJmCa&LwB=(`(y> zl8zGwc~4G^G6J`Jv8Wt^;x8>%F^aTlue_mVD7yJxOVtY$65~ zXtsTNRwUo=nFBUjT2pQCDmJ~lH|=fHGeImGj#7m~T?G@w#E!h1jOD!c%a?rZT=-TX_NSa_Rg+Ep-%U{%W<*MEY9(ht$q;VQJ$B zFu7_;@&OVB4UtKnQ^50d+y3#VBqeu&`jA6O0IWac!x%XsFf5|jDq7G7LB0-}7!l(G z%hV1WZ`j@^3+cGppOuojWLsjF38)ZGy%R^AvsLbzN=CYY1S8F+_LH}ZVc#IXR8+PpCT#RLjf-?uDk1xo z2K%pErmMB2;p6<@wRZG7Kkz&Z^_G0aQ|`9V@$}vFHAiY4hhK3s;>>Gq%{i|v8*HP$ zONit%*)Ocj2n|nOYCHM$hbhW0>?MakaI}>O@Q&V)@UvlbfvgO*Fpl1t#h zK|mu&K3%_DUIfMd8Gk)*m3obo%w$qC9(lo&#eKy&q#}>)BlBrHDlXnG&?pyp@xy$u z{JAfQCY2!Yp?$f&k(W~`VEn~_$xYAoQLX*nXIAUaSt-s@ss7$TR~R8oL1t>wtw)h2 zGTuC1V?nQ~KfV%~XvNWqH3;{lNhKOv`VDkRr@s=7(^_&KvuxmsbDFHt94Rn-F(|`e zZd>e?X&YIaE$DYsA;V*G<88Z8}&VgK~2V+)qD?t_D z+&`m3D+AxXR=z{G;<+a`^h-0ISL@CL8syC5y>bmY&q){O-^b+uO61+v%O4X`#aWrj zmp6GF8y7q`bDfB(7FDm`WoJFiS5C!A7hNJV6f~|Te8#-zDIH~9(Ffc5t&89<4cN;vS zC)Aai-kTE^#Ge+Dla;p4ThoPzvEN+9uqmt8vB=+o#YAO~?+Ny%CoxnJ(C|0?2{(=oztC ziM&jdwt%^Z!H#3ENGWq4(81BQhSe^!&o{r=uEx9*aJ=M?Ltx$3{2G|6Xo)^)nY;h% ztqxR)l8%*!WLXxij%ChQhcJKJ{ut?2{wMj7LudcK4_WirM5qIPHzUbuc9iPWTsuRMZ}w1_#^YF9^@Mv7G_(onRq*-#8HO-_PuS36tu)FR=WoN-t4Aw zFc~=o)dd~y;>H0`tKgsd6c62^pgPA`s=Q1|AKqD7pK6DL+SORVQe1dd8C_y4f%;F> zRAf?>Wf%tTf1W;fh4e=V1;e+sgy{=Kk5V55D+`<_okVTL#f5KcsbHrmO4_yz+y+aU z&Fbcm-i}t9Wb{4vJAd&TB$80^3aBSZ5O@4GR8|F#9OqD8AZt7% zm0e#c#bDqps$PDc6(jso2Qg9-EpHk3X;AaOX|HfUc@^3^4k>ch>Pyov5 zpjbvyy2;OmzDSa)Dmd_;DLN6vE{=j7p^K+De@@p9e9)8E{p>Nsvs3U>AwREzi3iv8^ONgtw9&eA1qo38Z!%l~YfgDap|=_SMd_qJgqZ5ubf%FzFA z8%6M`5z3x_cI4T%c?1soY3lqY;wFLO&IN8=dhnqGW%qLk6+FdlI$$6Nq# zE+MuDvTZlP6Zi$ue7rjk>q-zfkQ-I`Uq_NLzuDBWm8Y;g*0IheHvhifCP;aF~>DrfDf5FGg zd_cls0lX$eS>W80bU7)2{uqzx)BIF-q6XTX$G87_)*PJt*Yo??tFbXxzCENct<7I~s;1)h0OM&_`q zJDj?BZ#^>I-Ps3<|B?E0P=GuE7|-;7rJi*R3Z03lI7~^}MYrifu8}OkPZ_JR^Z#?G zsn17PfinHS!~DOpHo$+Em__RuDDp@z3f2EAjQ==1g6FO~ehDs5&bVvwrmwf8)XCZCDy-!(1>WeMGwVv|{yComxTo3_bjmW0GY-2XRO;VFuVeOXx)S4! zu!#UCa`6fj=89iImMbL`5&6?SFuG@`uCViVP{;L_!Jg$C{(wpFb{@nbI zDXE(eza1MPd3)_ZW~PJoQCI4F8nr~uJszHI#XXo*ri3r$b-%w0PVrMsxeD>uW6g* zmErPvYsH%QtN)Su*(-daR^pHmCAd!=!S+LmF=(qGbU(tK@R+;5=<2=-%i7KNNk?+) z#NHTHEPl8HlZ z;+kt~8{pn?7bVOfgI_&q8@j3Zk9$scB^k_ITtwMc0mpyBNDcP!bO}8&vkIow@bAId z8@EW__kAXxXU+1v3;nKG*y5sDi_*0GqfDo|mZbDC{$XB6mXO?AWnF@W4-viTu!S#A zce@HpMawP`iy65k?SE&D!(yik6?p@JJU_5~6(&&8&qJ&fR`ltkb-5DSJy!9SBI7?x zp7orXq?-fqE1~pC9mWYJ?mbeliXQ1mCS&vR=VBWhpvdhPD6%7&~b_^V&Ya_Q)U(6p|2vyK{Xwciwf`Y@Wg%WC22ct->8|OcBQ3UxY{=6I_iBzn(dN^K1%gLmU0k?UNnsgUZmdcXY{uha2-vvw0->Kn5K)D`7-sA@fD zdAI6uTgi#h+4U*Z(IUq&Em)dUbxgF(#FxBZOOr~{j+4#87G84gxNYDHThS~J zAl;e7I~g>&X}y@}nDI_7pe0;E#2cib%)bw%@9MkfG?BkMv0mER@{Vh1bKL&drR6#! zzun=Xz80a<`VT#8Y#c&GWdR=39z!OM#Z`O1GjBI~`bc&XYBt<3v zKAw+-MT%61M~XGda@9MG!U?fa0@sgc{I{bqz4Bt50XsidW*YpJk}JRjm)IibHwBVW z5mtMKX6~_`zg*b1;|;OYt-3DvQjhR$Ka;ljm-sUOZydOH_s=uFwfWd0t5GrrB3WRO~ntWqFG*xwYftt;J}jq z7xRK_54R}~`zDcDn4DY(Vq&yI!|CDV?vIvtzNa3W`(GU3d9~%ABgRb3oeF)l&y-ED zGK|G%K3c{aRIq(bq|A)IxyQgGSFkL^2r8;Xt)5So(=RQ@LbJ--l&XGUn49tfto_(OTgYzxLT}AQ{Ao6nV>phA>HkjvgNG-ICy?Mk^`d+ zvO1@VBnKN;jV@8}5q}Q{ytj$hl8*7;uB~`$>N)$7!aHbhh=`(%E090=dpIjH=$CbI8y?2tkbEoIr^*Cw=r@GB*UtOrc zK9uXc0poR$(A_v}%{patj`6ed$*o+lzrHvKyvbs^CFb`~VtsaSkuSv{fNI0;J>}{; zX5qak@r}l|V!9ATl|T_O_**xYQ&Hnq03ZFI8MF z%%m{!bFO)yWb|r)db#~m7`wtO0iceS$|2hr>XVhA|CzF#^CHu{gTXh`9%yJc&TQ}U^z z$J9H%R?zobk~h4-U%P|1TK=Ji**so)WNNxOh3Sp$Q^FP1PVc6>aK+UCpZ1dV7t23) z(Tc@X8!FkseA9Vpi|V#&%X{V4C6h!~X+z#HtEFf@j%Ky#Tz|u?UtWIx;`P6{Gw(Fa z_^lhRnzAnIF#MPaJero>>$r_6>aQxk9(PxRxWET_2f_cLkGreYv_Ggh0A5HUxLh5g zeDmCsZi=2E*;TM)%4&Vw9=KU)%%<_+cLkiH;|nck)BxJ;S!PT+Vj;UaL&hu!YvR4F zn2q`oXx)>c_*s1@uZrWtHi@~80SZs$8+6U7p$>%ADQfh?XwxZ*ZQBsBuO7X(Dp-tt z4@RE5{yfr&5kpqdj&+Q(<46C5+1&u7qgIC?u?QrmOKNfWw2q9Vo7?jtr{!9*bNY099-b}gzGs@vD?bBGQ?!)x=cC-O7f z`T%U)Zs7VKxbC`$_QaxJqC_f!5tiQ%Xc-{kxS`r8^q?Ssos7oP zt|UBH`^Q7nG~;ft;`ADS4!TnJmOnBeEd5sROF4Xp)BH&Ho=It1&dn6KzVc}!P?z-i zke|#e^+xNmnEtKZ`3JSFD3`!1^p~#RAl|qORJSeu&~wQP6qndqleqf)jPmis%ivinh2z(cr8+7H8%zF38 z?CqJam`XqWQhQN3vCnGv0Lx;YKufIbKpw)IrmM^6FUv2G%Q)cYTZI|iyTaY~k7A

W=&yNbmdl6 zp-Y)fGd}t=hY98U)a`js9y$o~Ys~<1%PdXZNUTTk2PrJLbnhwv0xCfLgz-HR)hpj> z1YP>{6w14-0wr4ctu7ufr+Y7+s-IfvF0EnZjQ>0eoHY8wm~o91Fob-#ay)a?h@d61 zdk#N!oph=ZF~}UU%bf-7S)5Y4xoazvtIy%-0NCpxd-Z*xM_)X;A2soWkyiu45V8#0k;@x37Up5 z?6VPAERpXJ?O%$V2C#iBwD=l@<7F8Zs{}DAqw=4z+QjFa2JsZ{g1pg8<&&ay!^snJ zm+qh5qMO1J)cM*7xGgrv(gveM@-Kkx4ZlI|p*=JU!h6b3vr*Iuec(0gG^Q4EPnF%* zV1IoUGALZpaQG1zwS-Qu1u5RGVwuO|8?)}fGDrDb zgi!A!u}4?y!YjG$*L3rse^6|^qaz0moRqeD(C|+*9Buyl9bZOxJEy-e-5pB^;jpG@ z1GYeX^k?@&?-%(>xOTThLSx}Bl^0`T^Up;;!2(f-jn`lgr!vWv zc#T5%(&!w2>QX)@w5 z4)mZ4u7Un~4)l4`$)cEbIy_jorwoU=1Zbrdia1!D5K%NhnKtpy*MYD4?Selbt64uD zyj^N{Zp7M-T(0%ptaV6Q`u4!mZbit@4!bD-^0+BMe1+cF%8=)n&rl~NT>GBy%rg_# zEdQhFU8#?)wxkWnUA90qC|qP9TDO7p>uJRogS;jyBk3 zK9+kc0D)NVxeb?tsA%~jQ%BXeF{z9!Gl%y#0!BGp+YWTvhM-ply3v@s<)eOO7pNkX zv&^akETCUMQm-_j_n5vKbT19v2202^x(K)dRj%be6v1Vu-xqdO;7YzI5A|4rNQ&o< z3a!ySN$NKIF8$cT4@C#tz*aXRWgyJF`i1`l5Tq{sr=ZrRSAR6~0lkuJP`r(~c8X`0 zM(P!*JC5$AU3eLUxdHgTBa_ycp80$T)-|YO771^UuMmbvB{m>=1_Wt|Ikl7U2G5O( zvHEw`r7v#e2_a-=0wiWD$1?o!V#oRSbJHuLly#H{_EpOzx=tZF>yy>g8N=G(dV%;; ze+N}pTkPOM`9#ubLCLznybi0sM(&3B$VkOmfd7)kKJ_$x^m**G^%%sGLG|Ja7kF*r zy2)u;cO{+NNxY0|OS7g}WL?dyRE8{J;d(kxb#gk-m@aE<@I~e5WlK>59gyns>rKHC z7;v{*98H-yO>=mUE5%^91{MJvSbn<;-t8&B$>?Y+(DG8y-!Al?fQX0b1K)t^AiN)R zV##1I%Y?OCgQu9yN?$$H(7|@HYRrO*9&%#Oynu;+S(rzkQMXyES%vEY9d7TLlkmb) zOLzaVj>Fu=c0G;N+Jxa=MI=w_OTl!(7nZE@&hVsn4h^XV6WSmI2T)M4nm_AK7Ds=$ z^vgOsS~>#k5;21F3*+>&C%ox~p_gBOa?$Cad7UH#8LM>AhvE4otQXH`zOYj2y!`a? zyn5%ksC4|J!@5lBKc#;WS$^m1*6SQKv zB_6t?<5%>}95Wsie+cZRPfN!f7!zl4Do^j*dvwxOI*y5sn!}=#lcV$NQ?;&4xMT)) zX%W&y6caT-A|9$HH@ACiv;U?Q&Bme%C2BptQv=k`ewJc3=LtxS4YPbEIe| zf?5;p)5RNXSW-9emd$9!fZ!J?A{|Qt*ZNG;d7}+CbU;(wdsj3!>al~H;o^ta3Cy{} zHBOOkTJ8UsVe4yZC{Lw>VSV4fc2#IO47T&O-;2N|c+9jt)T*PF%knc~yqJ(eqqv;Q zcgn5<)Jmtv^T}~qGsK*pa6hdC^C&G>K?=`nCL)@Cahz5!fI4lTYT$`ao>c(hM+iia;!~~ne^yHwK3eM<&U^G^}MXqXQs}W0zjNa{g+fl5QRhW(Hnv724Ya7 zxv{6G5G^(e_4|7S45|M$M6`o8ib`EGa^xjZzh|oXih_F@& z3s~XVgwOCa*+SMH=8=tqKFvK6P9nQdv~G=+sx>G++^I9HLEq2Hp;}9V0>~VDkLsuA zpAg-Q6qJ0P5LT-Gu(}RQ3rQ6aR-de%_vk~ODC}>CWm|itR^Bn`DvH0$Y;5UOXvh4Q z#MZPo#-_Q8;_VE(k^;!KiklyiqSR#-QlG!aWrOw{%=yxn2PBCUHy#C`;zm4-+XCZ# zeN@O5q&Vc4(solXLzRbK0~!}b9T3s!IqC)=I9*)6_PXumXvga!J#}ayBuz=tw6xw0 z>s#x+&K#?7V$w)835CeZU+zRQIi*osh`j9A!BQjn4xG$d-F-;M2|P^|&7g;c;kkFz zs}w4{=KqQ{l=Yt53;gOA*ym*pqJN)DchHGZ0L30elr{F2xeEi=vTjmPvx)GhlffqlgG#vF9{y)urF@K;}ar9%tn$aH)cFX&8^j8@}E z#^JgSLs~Kd0APXOn{=vkQs_VWaUE=+DJ zg$?F(#?jC;fp{!l(yA}+a#Kmh8Yx+!{Of#9cfi-8ie9rzL)mJ4>jfUT*Mq^e((|;OziNi5_=C zq!$XiX0*X)h)WeST}%`&nU^N8K7HSO=M|k;tnWfuOVweD*%ccf4g{Da2rX0JG3mf9 z8wI~StEQD%;`ie|2A!qYN-C%JnDtg^Gp9tmXA@u?y^La&oITIH+xmK|tnmxgmX(*x-M}9eL(0I1o(o6}R8`s?AhtX1N26>;$1Q+b798mcGG&rl*LOY-V25OZ(q+e%{EXNd*3-G^hoKnxN&1GDh zC_=h-7w_k4S@dNqkzP_#G$aMR_v}K5HM1JI^4FXVNtb%8Ej*n%yLz1vIenRt2K1L&X(ejLqdk?+-$Vd&X!|AxsZ+|UTh zllLU$8l>wk4TtA~_nJqq!S@GYzBg#~Mn86~*BdKAp=D1eESY&MzxV#E*G_p$@STLr z(dMUfmNv=>mz>UfVzcMU!R3#D?|cyrk$2eH+?VL>rp%QFuV_5}Lm%{>TF)t|G}6o~ z18^8pigxlIFQ3a*;5!5DHMO$fG=AtOC664`pwgdgQKoA)PuV%Z_M{;bG?XIy0`d#XK^xWqN_Mvc5#_`-8c z9VQsY;ig7NmUA-YJdRJg>`QpVQqfKaLbNWAO*uD8^(;FTx28uaAQEx zlKn64w{{Y_ZMHXFG23z+_f)c$ONg$8gM;&&k~Qu4r}NA))x&2$wq0R zWLmhxO-F?n_`lC%8-Yr5le((0lJc3K8)}r#X%AKzej$G#6E;`CW{Kg82s0%)yHFd& z3j989yT)Z0w7IoQ@j8m>pwD^noDAVWtV-0O`marcu(RxdWRCLD)--15XnPij6OEr~ z&jj1@*{25Hb6<^ZVh}1BF2Dy2LDDh;W0K!Dw0p%Yi2$hxm!rRxzn;q}iU<2|#d>Wr zZ(}IuW|nOj;E#xgm5Pi;)Vyta3R9Fn z#~7*HMX?X!4jsH#8<^uEC;V1hf?DAkAxa~OphLGru1s0+M8!g8T9Teq=a)<+lX6A0S?!v`I_1SiM34%Wc=xpS{bQ_ZEQ~uADLN*4!bsZV#u;~ zq0FljU4Of@Iga9Nqw=dyB9RcSwF&1*OYD5|HYW#gIvE}JI#cg)R?Uo`yfpg7{G)pf zFjDFZ+`ccVs=MxT=LJtm9>(8=j2+a)N8|&-Y@u=+Q&x`=G#5Bad=&f64?lWlT7A+% z%fM%wStS#mr>} z#M`5*H-EB!)(*LFj!dwhZRmZE2GoxLVjkx8fm&;rkToX2@qLGr> zLb(x2JMSON7m77SsB#7IGml-Y`ZHT8M*S$4{Oej5*m0p?63%wEvrPmkzyu7`V`E&L z<}!P-Sbjf<(r1)S?PnP<`N>)77i-FB{aHW5fPr=naN5+y1>t0YFE zun3{FV@a%`Hny?trc%AX2HSsLOO~*6uE&qk?*0z;o_lu_HXnJzu;Pc&*qmoYR_07Y z?ZeEC0p}`T^lw42+ZNNV)mK4=5t1w`fgDV|`l5h?HFQOrSkqu*6EM3M3;$XcQf`y< zTsHBbqC6_W(9RW+Xk;po)>3IztuX3j$m`+>!d{kr6v382;ExcWRd;jKy~4SiqW#?V z-!~srl&AW_#HFhI!JDFXpC0;(B}OB~Kl50e0mBcH(D!T-wN;iWf|K?+UMZg0PU;Y_ zHKtVcY!FTj_#qF%!SIdR_4=q#&rMwThI|%C(0RxTOyKtQ*Zsbokm3_-X1v7dW=*#K zAL90C)+f3+##O}e|0d7@9CK|NC0m=M~B%b_O^*G1DY5A>b{ z>rGMIXNklSMhXu=$p?JgJKbJxKinpoD`^%0!iMgid>>_Zf5~p+=?W$jUE~k_+h0he zrY%YopC}zai#{0i+o$F7^O?dQnJpDoOuQ^?`viUXTyPBkXCnEY<7fa5Wtqc++5)m% z2z#`%l}&If3qT2USur;KhvTrLR%U$sM89pKFQqR?^E8Jw54-m@>5eTG=#UH(K*ehC zJ11VdYqln6H_~c&T~46m2+@SM34+TZ?vp;zD)84{gMjNuASEo;eJhHg#82|%@^z@= zj?OxhS(~@lv@8LOnc3}V4Qr>rJYJx1oG;5%nW}sMLZ&^asrc6xCx_`Whvl2qiiM>g zoi;(Ej5Pr-h=EmftLQ8p0DdFx*iw(4?AUwKTaC*ztfdHE!(5~`&I>bwoe`hyvC3cU z$nbHqlwT@*-=G$sGk9&4mcwm4z%vGN=sy5fZ~H8##|<2P3KQfqBAo0Am3C$3!`0-s zEA_F5tp^Jwb=&W5$13rfEx|3{KTX6)JAlYf&L$IOa=~y#$XWaeCZ6wvAjLB!X%gCA zujzNSWi&R0&+ZN{PsD)Wu`-O|cw>5Zx2tNbJ+N+@EjD2Ni_dyY-OEb^hsL=J1B?Bk zux&G!h+jC!jmeLmw!S+$FpR(mp@Yoevo~Sq7ky*`v)m=JT!T)BhJ8i@Ql8L8lc5p? zsn-I7@?*f8ncrQXxvXLXDRTRgS7MJ_VMh{jIsg~$(&|(*dv*ErizEhoNzP)Kp^-=93fG4C-0&%3J_^B#|r4bA5L{File6W3}?)$EVHTT zsa-OJobFegahcDza~Wrx=kIyw58CGWBr})j5u`mU`)4Y%&A z2H}G7P05ln-y{cDwIq>y(GEF-*%{=@0(Dw)I(|`XN6rC9E7{gy6S|JR6y%`-@n@u~ zi*#e0)qL8b1qNV26LYgZo*qu~ar*9;%PzAlKV~hJU1?Urny9hDd^Zoq=HE6EMBdtc z{WCP)l81J)vEhlP4?OyE8?*b7POSD$o^}eWYS8keudZ%Ed{SuBt+b7U?J%xR`g=G# zyTuzBs2D4D2v~B-hOlt8;mq0J%#MlC6s_eRymm(f*$m_u3=YB^@H~lNS@ANOYLA8R z&mvwi*`QG6^nF3TDj7(@%S7tt$sfzO4;FxKq6xYW>Ql$W^(J;j!;`ml%EGL z7nc&x^jlk1$kt{32qt!60R$`yrdR^348yg(gX%s3aS1q>_}e>1-)rg^(mYby`5o}v z;h?(EF4Sy(E)p(nDriv=@$mNSdjA4ojiz^ zF7zOx#zr$rw2O6TBz;zzWG+nb6jrvHeu}4E-vZv1vdjiRmV_5Jri<%}t~S(cUc-5; zUoTE_`F_l{6h(ExggdYe4N`gLQtXz&ps`aIOuPGsh-{4%+HqFK3+B$(*)gQ9>Gwjm zjeVQ7yT||!j7ZHwY*mG0{yII_S{GD|GkWu8b|K;p=%cR1kC~^!yf)CkX)dv{H=P_{ zu~dZzqh>N}rC4sZ8mvNT#@$nM{~oFGLsQ2d@8ho!Ssu`2SSy?y&+pcuO2Z3F8hJ-8 zTpAR(mqA_#YCW&0-|lpwdj;A@mX8%RS#9+L&{YIijFzj0wClzx{O*`5-^Ya5a(Gg7 zRnB?C`I*2PeE2M3>4OkF^})Y|dwb1Ud(jzZE)^KqXk|GJ<+=W%B97~1=g;Y1N3wkrkvNeL3qe>>tq z@NTKMbCO2O(x0pj+YwDfHMKt_ybX5n%XQ5x+_X6f_8fS#tjfxb4#4z$wn6z)eskE# z7e2PbZB|E`IX8-X&G`Nh_^99CUo~dV0fFG`%0ZN1`71&{VntFmUW3)7mK(L^j@=GQ z%Wp0zQIX#&d!X=YRuKTxa+Awk6#B1vci@WSjQDt4bZ%}NSlXFnN;_U4@4e`hF7Fuu zi&2tuLDjC8uqARi7M1%+M^-^I&aV!%b4CsYc|88EzUXckkWRB-(tGQ76K3=F9@IsE z`l#%A>0&5csK)djy`@IQ_Ta@@h8TJ2iE1aa-B zXJZD?duMfGzC85T<-me}>sBV?D3H%3BRyfComwz?hV)Q#@0^Tx?xNCg?b?ur+d+pl zcJo-7nL@A#c3R4`)q&QTBbK;i=DLoiZK?5^P}|WVpVHJak)yCd1?Luu@#A!$>tJ&W z{Oq;o0`qQ#aCo+oO$xl)_5y_lOw=Yme$Yp=Yw+W1VDPwCoiF1GP`fqpvfX?zr!b9p zIClI)fNwcIb}*+@(pH}tU!g9|A240j6bojnK5Jm~vW^pt&6BJkHX0d;HkU~FQ_kYz zPxa-0GQLzVuZ+Kl*V+@$+P`sAgu2>WI>c|h?77r%4Bq`$P2)kanr2Y$whoxPSQ@{H z6FtilhfGlC#jtWv_BQcvO*kWbmWH`n>(G0HRn%beho^FUV}Svf&r&^koY{EPme1vy?bRq^cDh*!zzNW#ds{sut zY1ldm@MgU@>4^X=3JOg(t4UFK`Fz_F^zC4G<%y}0|K6=BS%<+KkPzZQB7`ii{){YR zOMQ|xLlKJnftCE-^n-?N7{?AmxOb>B0uRw@0_j8kuTJ#%de$Z!^3(Tt?hm;o%~#|q zW(Mqa`hfkqF-!1d>gv4TnI#s>BB4+VHb`$$L%=)`Ewg7MHppUdAw-Wsjoe*eJP4%h zh*p)XyM&ydng$-4zgnlOs(!i1WZKp^Ex)K4;56WSV7e~$pZ3%?)S z{P}#&yT@RUpomJi|Ahr;ejpBhBl@s>%8h6?T3;;`kjxB=hSz|Q63GeNk`aqtHt{E>Qq zKbjvJvJ0)w%x?r<7N5FQ|J*B|nen$l2l=(9LU|`@1Nf}43t-P0H%`8O9$b*6fHt{i zFw=6N<9nn0gdR3{SrEZwm@Wrx6L}XSSjBVcPHy|0sE#7$NqxfUeATuRZWsUwbtNf! zPxEeg9G$Kcd?2`Ml07knnq>W?k}47D*#K9WS?oWsQs{+^57c#eKhnS=Wx2tL4Umj! zP52Tpz<7+w<~rrqmRr3OlAh>V4|-I}As03pwAzn*Aha)TC@dK`Jjd8-!4N(23n~zK z=W6YwtA;;+3#v(90r<&6_TNl=j+risv)NORJxMUEsN59@#S98MO;ncueKPpd+k#{T zxph^ZVK(YwCDH3(`A9gJ0GA$YLF|7qmwOS?#+Mo3hCgnH^dhoK>t`KFpZ@~QnvR)N zMM)R7MCs%id>y`nn|!s&QH??zuaSsb-7S-Xk8^7Q6*AqgGg$C$Y&x;}eqm;CChYHeyfOJZCgX9lHMMXL`4N{7fbV&(H zcQ;CR=O)hD==*)|cXP%#H|Ly-{|#fn-uz;%x#pT{&iOo#ha4G>+E*vgGgZv+xFTwu*7>zDg@S?&PejggBW-sZN)k z+`;87CT&E(gChc#k z^D5rI8dA{yp=DQ6;Pj%NiH`jwi$Lo(0C(VIWIoBbspWexG)Du~GuwYV+1gn)3HxDF zSf3)n;=zrM1|6(SiQEv`4@YKeV?4ZpO3qc@;woX2P9#f2`L_@I-+%v2Kyao;d6c7~ z!;JD-W%HFg+tYy3i*?R!A(8Hj`jY3ro8RrUMKg1mwGYN6<||E@5ApW_H6cqNYFyen z{Po$tMiZWQ4!cDO?)jPxOkem`XgObH1F|E1W4x?`2G5U+6p2-#!{+t(|M8m~Wr+E| zKokrtHfHdS3i9J_{{kF=FM>R~q#Hn40w^Pvg1jV3Z)Bu9)t_JSXu(}jRgxpG5HEP( zEgK?lf$~iLXRa|3rUeV6`o9|^ z2#k@^qIni-_gXYq`Ekm#YlZcp0E#8u5zC$d(qDTUdoKaR3Y4Ge68P=NyAlOBUvI9` z1)QLL!|ZJNC5?sMnIyz>c{V4OhUQSm(E^$Z@16_v0^;_w5`vpoxuhEbg>|Pa34AuO z67Ms9=*SuEV0EU5iS(O{nZY^reVE)BVbb*BKe^!A=44x_(yl$cZaY}FTl9QlyVa9m zbsa3-2#um401mKz@#GKoF|8Gi4OPQbK+)x2wsu{e7_?(QZF2czN76`V(`p~L2&cUO zfZ5OYz_00VmaedJcwhm&R|F8Lh^hNs4F8yr%2V(cX}q3)T@#x)9@y&L8sh|ozaYJI z@cVo66r#WQEN)=l((X;X@PF`G{*uz*Pq+9)nXWF23w8xlV1@73-&k>6q42O#0^TE$ z4u4+;U^H8i5STH%0lljSzkUleZ9N;@PP)1xg!Fh3SiG#oht?dZFln~?p1bo%=m+7E z{7b~=|7|9FUby5vKey_xEcXG;7ibvzQo@l zH2V*{5$m%O7<;>ihyRSL5g0^t9~a(`|BkC27&U96;?(~-0aP52L!M2{u)3O59Pp^t zM8E_R!_WUS%h+1rx~MahN_7939mM|_%U5t|Se;P9DJJmd^c|^TVyQE2gO;C#RKqV5 z77#^%D#2B=Ik1SV-eJkv4#=yf|HBLDS#n zi!NDeJLW8>-|cptIND$B$p2+E75O8O$^z}e56*z88Z4i(AiVt#rj-bCg6Of@kY;dV zh>G$L4)zDqxZ<`_%CD@64lDW_n3u@QTPXiolAXvU`G2mU^8augpy+RsC6S~ky|gex zpB-tC(pn+}py-Jq4V!QqwvDK6uodW(S{9Wi{yP0v9Qh~u@xr;?lp~OQ?O$FC>D(S* zfjqEEMaB;AVyMU#KqyYool?GVoLdp?DR%$!Y?5iC=;Zn+y@0*NWPQ}BY#MIdWDty?S*Gxq@pS-L3k8#NpeAcA_81)| zM~lb53BZqB!Z@(i0R+mue_shKM3EFfo?#ugAEU+thkf7To9oftTzIqJH#Fl*mZhQqNhKI2%^=gX-%|3ZW8guCT1zP3vy$Y04>qKr zKil*ma4zZ>UHaSw;zzNahW6@4pZ{+nOINVzIW#)WAPmZQn)+>1TWBtoNgB8qr@asx zY$m;9JB8kx?*Z=~pNuql59tG=c(d4d6c?`Ncas`FUOj(WB^qcDBBb%%f65D3Wk3pO zjYrLCN<5!IGOZb{E{=<}-~t+HIDYUvBS6fDu1HffH${f z$&UJu`#|jSylhA#IF?;%07;RZ}lxa1tedeh-|9lPp(La@@>{`89Gg$kTwICJ(q&m4Bn!2k}7kv`v>(G zozM`+rg9pqYyYm!7xD9}ano*NqG@>SlMR5CqI@wM;Iv-U@)JC~fi>?bva#SL$qVac zs8aNpiO+d;pUlZS7Vn}eV{*vL{CwwlqRMe2{^PbqB_0s@F5*Rz41`LIRSg4O#iImZ!|*|^ z?bpy{O6a+x&e^Yk#-fp)txyvVZ^2F9&HUr_;P}hjS+W2ash08UTfaqSQUsiA0CfkK z-`OjeL($?tP+syvN$mZq?e92yMIT+dg_tf7GnbEZHS%|hGI-xabEDu<3G+hT5|?MT zS)q8K%k|vK2Y^gMNTqs!6@9&prn;C~Si)8r(~-9_Ft<^&6(SQR%D6ew&3RnDqWKUkUeW|U~@hR5t#Wv-1QA4uA0sD1wu9| zCOG<0)Rh(hMNjaUVNWPzS3WK*VtyW%lqK%->ow_I)so>`06v>vTJdeT=(FD_vk{hN z3w6niQtr7hJ|4N|JGWumR)Wv?q_eQ<#5_^jE3^C=+m@g1E(Q$#K(Tr&&><^7)(HzaHW2;W0A(3#WOQXA7=6TZ-L z+DX53_L}(4yxPuhe^f4~-$7$_Bz+$r1+CY)hSVO7^BAUQ{&r&(H>Avt>3Odr;*!_>Z@j{hDf3~KJO=r@fXkXNh!~|n`Ofk zHg6Nu*pJ;YyldN1uu)Qvs^Ar>l5fw#<{NN$>mv3EWb-MjfdyXbx`M-bqqW8H4L%KF zesK@4ehF8SUHOW`2hNVg71F%eAK1iqBk}QbQM}>O!%sLi*=foD5DFic%D=5Ew z+&MrYj2#U9;gA_F(@?zx#nuT(8Wc&B8n>t_9o(i#9;t_Jt z?!NE4{81M5ccR@mp9kB7!Bh_tTC6lXB5{*kzDvJ;c z=c)P!nd|rZX{Y3`_sg?0<0%MirPOp*a*0gT@QBnS#G5+L_Act7l$y!QswFmk>(q(; z^)a<-*OljfnqKs`8#kw*8Z)qRc0m_J$8ulB#&Lvs!s3bSea1RkK=$<{pra*JAw5%0 zy5#cJkZYpdTwQ5l9)3*hclLyLhEYEwxm~iqx*J!8VIBLRs6;l)t+S5=M1~_B-tK|G zOgmGmFSV_C4aT-*M=EFP2eR|0ao1!Y1%(k%y;wau)a6H+UxAiVJYsu#&q+B>mFiC1 z({X(+Ywu&~KmUC@ScyRC5_PMCA&Q^Pa-f0APB^dt)STs2_h^Xi68LRs<`kV6MXS@s-m&~M*%-pIh$NReYXke?j8?=R6C|F&3*?b4-~^i1KoX% za|>G|MG2}}--Y%|0JH2xTB3-A@^N(>E(Q0fR1B#b2hQmoM#VsCI#ZOG6r0`X9B$K5 zN1|mn&gGnqB`3oV@J}_au_In}Wa-!nBEdYl#Jd%QExv`4z+|q#b?^m&i zSx4+G-&#7|kT9GQlGV~+-V)%e``NhveDv?aJ zphM$OY4j-rrsA$-0YfC^<&5$fv$%fhxgx-GW^^AIbT>D0MG7|yG6%Q40dZr+X&v22p&@C0kxt2DH_!QBLF;_B+$Bes$0mV_ z@uyKr$>$5CsS~rQcV9y|KvVRGv_*SYvc!xXN^cS(&{DWHTFAw`g(2v~prJF4^;vP| z`e@Yv^@Ki3_^GAM^K=p_C5uL}k^CCG3&1d0qMolO4X7?HujQ88&2W(OW?u6Z^FD}8 zwNQ-W%~2C^$2*{YHlTuuT_kDIf=4afsUyC--E_DWg28oeV-7WTB?&1U(g@NT`kse$ zT^~GbV|uvWrTK@t@3%~v%ThQ0@h1@Lx^Mp^yuDr^paWCv2(W7C1!^f-%BkY+8t@ul ztt)9)0t8HY&pcKj<@3G5iUQjDEg=pa65ACLsLG3Ooy;^r$g;m!|M1y;84N3fs<&7e zENoRJP-gJ?4HpcwyZ0PV_sN!G@3c7amUNYHx|WPtyRI+F@vzoP?L23sU3h<1WE^;Q z)}bhZWz&;V--H)QcDbOLJ{D1v&?b`LCfgDk%VBs2(aD`m%s;HC;RQ9+bk|j$62zED znc>Rx!MUHu?c|9s&M#`W)gG*h@pX0q@oJoaS#a?MraS>+jCtEHEo?=-bq;2G0YC2N+z^xcNkl-+GD(a zSss(C0Su`59Z|<EtRY1!IC~l&zf11}fZmqNcSC z0!lrivw;D->pc2_=a^|#?`DTl($H~8+a?_9e(jF)qVIF_pk(KJmSi7_-GzAhP{qhm z_9q;Iv!MFA-z;kW*%t1m$9<97q1jdyM;j9kYwZ`?z(<=;=2$!;`V1Yc?{T&ot9_Tc zmYBj*g7|Lzln-z1Md*UdA{quq?U6l%IBG7^n-TF?zt>aStIuycd5=yeB$hv4x2hDc z8b9@q)YV%%)UH{c7PPO{l0ATZO27L(elGrL&5y$Dc{3|D4Oft2+3i>092?&>w(D_5 z8h1b?k!+G+1f=eHKg?IZ_UR8aRI1`e$@(pE4i9}(R=54mOH+TJ-8FLriy`kPUa0i# zAmqFiheHiA4NVg8Ic;;;m{hr*#z)26#lyi`HG&c70d zfTVy?RwFka#h^#tjYDe3o%D6?1r`!c9on`)Q+}VFAJFEmPT`2zYi8dsWHG6m5DFZxQr&C%#UQCajjBnef_M{wDJQP&wKOW zqy3Kko;ns4*h*xWl5;Sf^ zdx;)AZ~pd+#Dk+jw@bxDV?(RG#Uh69*zw%PUTg^t*Xf_M!aXm(djidS)6Z}WLx44q zs;=LEU+Y6fzcQJV5%qg>s)pNoAG(dGeN^-5CHkcxf4_gi5$1kHBP;NsQ2?|gsPWpX z&;cV}&+Iyjg}eoC9*y8^=}@P-%SgHxE>&xNMhQ>r%k|%9m4i|@4QkxGs4Lv_6S^0E`Sz0Uk=uO^6tEe_h*iU>g2Lz!VX-tF zi2{W#Wru15+R)z6u~DN$C?1Q1j$C^TULH>_s^X@Oy3!Pt+DHgbqXCFxs926=My&W> zZ%(C*gt;5-ZvUb?B+K?ma7DN?0 z;j#I%>6T;rD~`0N484$F%V;$0cZPBlG_}jTLd;%Vj9IKpF$J}qy~eRKPXv76Z7OQ! zcP+QWOepXo`{V@2AIGa2dTdTQ7;lb>Z!EFXzOwccZPZj+WRp~)W)ajI!q zTHdbS4i#4!C?c_QSww##k3CM>vgBzV3AC7R6Zd7e49_y#B`}OXt1*IwY0T6MbR}HC ze`r^PT=~fARfk?PE}?GX{YsxJtxZYVJJ=s?bA7+KanLQYHHwWHQTzCu$`14}xC^vs zshfsDjfcQ7`g3A0yBtv7YL|<-LnAwJkej*4l)>Pzkr3G`>ft`ZYtw6(8U~L)NNk^u`v4Q=U}MD4dL)--b?6{|$#k{U%H3R@g-39|(R&L6hq2F} zTIMjmTv?oK(QdebnB5{vX@D(g=shV;ivB%l*`0hgedD;u#2zy>-Rpy*d2DPsAle=e zaz0deE1_pL+ZM~08_%cNW-sJ1{gS{N%`e#-QppL(8T(SQb$1-8p?Zr|{cB>4aQThQ zu0)y_-Pw#ouC3XKRj%*Wetv%LF_(8Pzw#G#WLU2rrkR_8e%&nlDPd!Kjb*<}*Fb3d z^!DNdtpKFy_)FqqS;k*ypNV1B zH|O!$6AQA|%lJa1P{QpC%^FHqq;dlp(S2LXdyi+h17>x;OmW4|k3x*uKV{RcSC@lo zj^fBIoIP#~|Iw_N-GB+ND3r*om831D;wQ~h#@Kj$kG2Ag5H)#PMGw1EC1_$EeZ>dE zHah^5Q>Cw9s9W_rsVdd4dB}pd%%t5BePd+4({N%T4F655@-yq!e*40=m)~dRyF(Ix zZMI-cGtG*#bt0ewrtf0I@vB)1G11e72e`4ukMYy^`pQ;lr2}9E140vcoiqu~ULC{v z&b>1;MfV14%=@u&g{(iVNwzVlv5IB1J)Zv}eQ;a&el~ML*d?NH-*b=M>fm@TUE5g? z{rr`vLy5VP?Yj~N1$YAY^Yg$g$KjyYYr~irV$#`X@Ht8dpsOM!_-(@%7U7~wefeZD zlp9A9{H-ekpX{vizN$Kll)D{l%JSvchgaJ?<#Y4se4D2b@w;Aq^w$B`-4+vyKY?00 z-Piu;qF%D?5OO|jjT^khErTE4hsAT}Bz&)unh1zwS%b`kO4~cd_GEs`A*60fAGX?t z`5)m3LM-eI@}<)V&Nr;GUtx#!1BFHS4Au`{&PA>xCJ|%MK4q2C ztcPiWxz6yKxYKT?n0px95WBpFF1RGtOgWNX?yK7`-*?N%y{$i5Rh4U-@a%1APnJ*CK_J>9>6hr+r-3H8azt!U>k(Ys*@Mro@gs zso@o&3U_Gcxn@TV?^!E(`f7a+e7w*BACQi)K3E%$c@X#}t|5IbJ*)X;cB=be$cVOy zj>7OW5%;soPJXiySEIH46&~~1jikvMn-q?O>}#rhcAJWH&bDRR)wc1F?PRGokXW<3 zmawl=HdRI8p0M+lGSTkup;ivn_c$xx@z?i5F=2?9MZFaRMIT3yu8sTkjr2N-A2V+0 z15AfkvUG3t10X=*Qa4i&ZK%Y5haA=iG{e4CexNzh{`_dUz6SuzD6OkZ0?Y~BP0LRf zC*GCo#hK{0LPy?5okq*&<}FUvf_2hoQ(OjV4_uzw8B@^-1D^sn?@DN|Ndx}kt zl4(MWqZrR?_cXpa3sqA|G3VHU(T{0Q>E~h^s-#R;<%cI91{PU@OE|)Uxgx(R9-I+c z6^V5dM~HvLL%LTKSPI+hZHfM()Mfsb+k-0t{GQuMLPSQAKal5DUt->dFYzQ2shbU0w@pV1rVbD0GTT&(=L5N&o3~YXX8NHx zGIuxYk(N z-=8&rLco6X2RSX()S}?eTF}eB4M`t~u3w4y&1%DBo*3@u-RPqzB$fsF-sqo>t{)yT zIa&>H5gg44zO!q?YAJVT!aqgMj2D^k;|x?u%p26v`ZXRMfeiAaxdX)qfT*d2L4((0-#Bm#uJC;sI-x`QC(vc~*-x&WGDh9G9@h59+6t08kua~mk zkxjP8@K}7k`5ff1!Y|_+vX3owtn+1h4ALK%GRPCd;f6j>skz-qLG^RF^jS-L*t6iU zO{XV+UhyH8X?t`VhX_#D{OlH?@3`wnQb*4WU9fJDjhTGCiV6LXuy_k@pR+kndkpQ5 zs-c0*9Nj6R(^D;SZAjA~_QuA9r~F9zVA%49UzTzu+*t}8kU}yN=@QM2bC+&!$ai^wNyO z;F~4SS>QlTW5&qd|qL&z^wiSFDW*BnpIu02AZujD|Z(0~q{m*8N>9GhDfrfS9+ z=LMbchJHoC)vlZLg@-Q~maW5%^Ui_5L2}h`*Q-LQTB|?r^P*TXM>Wg#h~HtFa>L$! zytIvaE+@N;{?Vds_nbbD+5MhmF;*ex`L7am$*jlz{$baX02SjnMqmxA@f;noUUm@n zB;P`GSaK~rrZQS<5f7Xpoq@u|T%N8+z)x03LZkCh;RpMZ_x_=CmMFc(QOS&V%34Ez zEtjoHap0jO)H?=zn?~^Ujh!lNVy;MKooT8lC%-yau}GaLN=zv{42P6Zs-Cb6WUoEs zSEixcdE=BF4rTjX>DWlp)}hHK(wf+X0`Iin#D#I76}+f=*Kl^Pa+xyBm+-Az0IO!< z=#o#B^8)vf;mwx$k3uA{B1XsaNg*sjM-|wO(+(;g5}JrudNX;?*>}UL*%L02Ygr-*?XrgS)_tqIhHL?Nz|(fW8ddF zhC?YeqR&L;5jx6BXUR`IiubYDVCHU8pv=o|O@6Z)u+-O;-=m2I2hG;?Di^!4LNB+q z*5X^N>s)*yDy{Bk$4*4yZfzXHWOl}UGA!+1hg%kRrx<^f^HcfYvK}ujb~UK77LDPD z0x0TL+$)97GGR2sR_t@)EM#%-JOXO`>4f5g?)c($?<};B{YKW(13yRkNVfOgr_$8PXG6(9Lkgc7I5#155b0 z3b3mY@1NxgvwBOd`LBPFWW&-Ea#Fp~nZgP(o$RECo58)>k#X3fCu_qX3!kX4Y5k0$ z=-st622s?9r23NsfQ?u|fygs&Bcsn`e(_vq_l|%V5W{m_pL{l6wsr4xjA%l6AX@AFv}v``E%Pcn zyJ(?t|7ievR5EqSS{_m{&7I$5sQwL_NInj)!D=-r_-$RQI5rIFfB!1?XxPwARI!m_CJDn0Xxt7kkbxUq`>3&r^T;{_`x&b;T34Ps*=Z zG#4fXsU?mPh>jqdkm8crl-1s+7o%VAXx$$G91Lc&X^rJUWIX24KsT*&-7ZI!A(?8I z!O3F!GoNfEJ>}m)Y?&cKsTyC}@?KvnGbo&xTM5{ltTXA!Z3)fQsCG3XyVa%6>$TqE z+!!6oM(f0t|#<&9_DY&M_hW<`&!hDEzMm%yYFg=M=iO4pP>4_3m#r zFK#tq*lT^eWZk~nw_(;gFtpHjv#N{M_ihEE=w`q%K`+}kDh{(`1vxW52K*F3MUcbTJz@uKaZ{$rzUA>6kvr(p%urohY;F z%+Jn(yAtv{$OUkdCbLol|8d!ADgUH4>d*~57jEeint%>FZa`!r>mkE)2`K2F^k*qB zou3{A?_aDVb3(`I4f=aNC!?!;#j6JurmGo4W?hNr^D}Ps!Jd%pW-MJ`&I|R!gOuIo zFAV+L5Ya*afBwCL1r4@ec^{?+Q(w&ytjdF0(w&UQq2U#X1A!G>4~33lJ6j_;YPHQZ zhp5wy;qyF6GH%oGq70dL7`-oaShc>Uo!#HjUAfrpNVf_B3MZa&DWX;J`Oo?>k|~9K zM#mO@JqoJCt#*QJ)3p70yY@ios63c5P%iY~&Wz2_7b}?8n>GNN&h*M3cR%=$n*D}_ zE4!oz1a?enPy%C+q7a{*lKx;w1#2hG*5MYDeyV48cCr0LL7N-comxXJfsC_OKz`y%a!5$#8T@a`Nf`8 zPH-p+>n&R=?-AYm&~-!5_&b`skF;z^2r}C{N?PfLD9D?3k}^-cX~9^3*YB$8OOo_eI55#HtDXJ$_x}6E_XDr_Lzu zKg2YlE4)Q}Ej&?IGuK?{#OzdA^`uWel1-aiH^sW8JNb~5_ewn+T;LAlE)kOG>(bP>L0zn7}pOn9dD#Z)f-xCsry^T!eKLEWy2iEIqvp{$v9I!8prHupGsxkkxG@hkL zo1Y)l3G3#9DwgxcI3IA8AEaD+ndXa*L!qmjtqW}8Mp9&B)QDV&cvM!)l7i_@>IK+w z=3Aq6QtY%Io7c|B`%(9*Ru8gDZ!33B-3%p`feCButc-H?ALR>dTx00^4)o8&Ew;nV z`7dT`QIW?xOZfNq`3moWm+7(u&Q;$+fGTS$8RwzT+s@lh%JZ~0nweVeKneWr`WFf# z-c7gPeG20dO7i>@4mPNvfg{*m&^r~O2f)6j7`At}>JSC-$38-xn^ToeP49nse=9`T)Z#Cgv$VJ|LeavS?@5bbF zN4Y?sBj)tz)=S6-I_8Y1+Ly}S4@aD-)NRat^XfV-@kk{T6n57uHtS*ZL&uQ^8-DUy z?XN(L4oY6L#(j7mC~hm%ee-Ne%34_U(kZpJ!|b7U7<>2zmweeeN>DfZnkV_b3!uZ6 zbLf{|RV-f~E)HAi#zfO2zI?Rm+ikADdtj8R_UTC*Xt07Zi?KTUQH051b&Owu029I` z1CXxQxv%II!m%uA!*2Xs?iv5K=mC1So@G47Yl~tE(|2!)Vy7J4s;Z}+5J~9+F-&f>D%f-Fm!zZBZn>HFFK=@ib8Ge!yj4Q&+Ev>NX>sT#YnYSF5_ zw-_it4(LBF>)E)rIFcmJjCdt*uZ$zhISGBj;)$QaB3?8Js_D&0u;b+sZI^a(;-MN6 z34-`*U0`f6H&Lz(M6FMaz}656GQ4{TwZ{7pCj?40yXz%A<8sE@O8#HXqmfMnR0{Hu z^zW80!!+`=?tl)ZlJmSC2VR6L!03DzWn?ATMhhTf%w3kqzNrYP^zELbD=!oIZc_!6 zony34uSm!&zjV>aGvYZXw$w6={G>qf(f4j~-y1v%+o1#yBuJUMlAt~TS@_6pCR*mb zkAoCILqUZD?YPgN!=)dvV8)Ah6!@I&v>e`YHleEQbL|Pl!2A7CQkJp~S$5i?4`;J_ zZl(7zT+yIz!VLdr;)kqf)KDjT7mEa~8#M=Qj_Q5-*_-cQV?Cs`8D{%s4(g^jA>Fq_ z4Mt+~p|K8D!vfoHn3t9^qP`!RDSpPU<1OI-Ia!NJrGdClHB0_hBB~|Gub1=2#bV-& z@uJj#CU4Z@-oI&rJwqD`5;u?|MNZej<_#XahuqPzbaGd$jZHm^44E zys>SWu=E*&gh@}R02dj>La2C~SY#;bqJ|`iE1#TxJd9+2w0gWnF9voaty{g$^LwOh zrAv^deS2B|ALoVKrU-f!og~h-1KDvT0O7oLg?;iF(78#k51t2Y*er1tayr5WC0Kn| z|L~o()u1jfvnxuMBk{ddXly_I(YDR-s5#5xljcJzNXzso`OFJ>D{Nzj|?u>X)F!hZ= ziG?JVgpu~MY>J|jyWbPA2kkI9o_3P;-8>X=UngL1O6E3`w^FG$ICFc;^QXq>@2`B_ zv@KFv*kjYyP~53ej~+Sx3a|ve{P4ETlD)(&dc$NLerTCVx$H~isOj9NI|NTrFUqCV z+c7J2YwT0K zki%;Ec4oM!*q+3)#b-WA*1*paokj_%G3GqW&EEIQw}92@aNzyVtz}=pn7PdrUy%N|^&uIt{$98kHG)8!(Fbf7?pwGl4sWz?dLvcwcd=ijg6m9s z^el5lJ&j~We*&H_2|jBCW5Ys#^wMsf_v0{lAM&D(*x=zZXRjf@hV}o!OG3V|cboBx zzEjyPpu%JaL#`oktdWal)24TN{v5w{@5R`5lmAPi=}=s5Qi zuj<84x@mWkHmFtnHmm(qb6v`C_WNs`!=qH7!YgxW1a$p4i7Gw!veD4cWC80LJwQDh z40~4xgZVu!Xo%;r(4+@^_lFBL*m_lk*m^bYN*soDY-7b{d(U766D{c7DPpWZAR!t^ zk{nl}!MB28p%&U8yor%|ck|>f>d5*W?39fa0E;3)TP6*Bd%A-OrPX_jN(>$W<{Cp% zwyDt&b~V%3-?YF?bJlsfQe90T8qEJ#Iz`ues971@K9N>4A{VAlg@}%UzsO5nCmmDsHZ*ma3mXoftBq3PLvrz&MzCR{@Rs6 zV7({;Uo!C7TZVtdMrB9!xH1|X6jV+UU`D|X)cRKs4AC;9fz>amhox8TDrY}gNvQd0 zPW0dYU=|ey#xO!XN7YkOc^QUWHIh>3z^5^wqM{N`C2joUVv*~S7WF#4T%;7YS$i}} zL9+*E01E9)?U9}k%!$Jha7GEat*R%b!inzw$K3!gDS3uY4@LEX9~8_%7ko~PC4&FP<_knWxR3wg2T*Z$qqHQq<|dP(nH z{>PgIU0+@=%9M+T=I>Ml+9N+AyxF@$LDljRSq`QB=T-f;uG7m$%SJLOZ;Fs6p%g}= zq@le~tD=V+SD@a*njw^37lF6O+`XE=@NFUfye6@ zOOI)%(L$-kSio`cUB#ZubF|-Tv!FBW(4qmLk$#s@BvAysAQJRDt4tDhW0~&mc}MtG zIlyM5U@R3?z+w7BkG-Qka@GQ5v61PO*Epnit4I^+!tbNOV6gQqPOLd=s1h6PE~n%1 zwy_X&;WO`HijQ{FzX6<$*L(ay%+Khb`|Bpb+iwjKbTacG`xY@1Atw45EUB=nTdrsU zuh`S@p@SfddR~TgMCZ_JcE{h%SVBOOew5-XMgSTc@>2_x2WeQxj4oF8=)A{4kYEkN zb2Au8rPJJ-b23Q+1xr|6n;z&DrI$rbZcfKfGZ80>m@Lv3gklFK=ceD7yUi#7gfjw>UV8L93HU+K6XI*go&fnT;#&MhrR8L z^aW=a{k~U5ggt{?w-s9wnt$%KfZQrh@G=os7FWj(9>ie=Dgav!@>U(lTkTTF9{qU| zA6@VyGj*4Gz)K--1xMa$K_Gnd&yyUNgD0u;y{z*3W3QZ~;8qFJUC2cYCJ+5GapG*q!3$Y+K&48mkVOtn(E~@oEHUNE}pR<(tI~fNUe{ z@kaIT?+&q>yJpUI|M-QgkO=&^4m>Wyb%xokEzXccK94%UuS8>=xXhtd_0B3!=q(w+$0JIYIb+-R18?fjWJOWKcN-q*isQ=~C{y4XP zc8j8+1J6CuxAm8Imexjq0^XT=ScNSIC_=^R@Y%@Tk*L2dA&z;K&i#*H^pTrA9#beb zn~^iDHZ8b?nhJKnIVjsD&m1Hf<|nr5@JD95H$=L9f#}c8%|qM&cwgiwVQJ2G9#Y+O zoHhDhD~SxS>6tOEzaoySFbdFD&vY~%tAOg+bj$UCdK@FD0by5JMc0qy>T2#w|3|+0 z&kpdmt|5A%W?@2lg27?fwIBNCOlWm#Nyr>7=DUyINeCK@P0;HSw##@OhSi;y+82s^ zGfnsfCYz!0e z#_w)2L_VdHkp;29{1>bX?K)?(@6{>=7=MNqLYu_4lzVrwx1>;EKb2`aRt$VQbsL;B zRsD;)oY%oB1FJ?}IPf#oVi>Gfxv$@kXE&$3xsSrm4^3ry^dg;H%4O$3o*eNfPwxDy zJ*0Bwy2S&O06`*II7hNxy`TP@u-jySiB{^L7m<`jrJ~RQ`tSP?zyq*ot!ag z!m)!uQC{1KF=?jSjdk80)TRPn5LCME$AKDwTIyK2{>5!Ub-ynkrM*9 zMcxKYd}k(n>x9aU@-}DZc09g% zfg-x?mXtZ?)$ctFLn7C1>c4Wjo~nx5Q&>b-lY$4*BKBky`*TOGVm zsDUIKq{r$^YLWzn12M{D(TR-%Vm9%D$cGD%U&`eH6LcKlpw3Kca7O};O%bN z+HhNUa*gT8=2Rp|$}xj}aZxa2s@bk9;fW&kLF*&QA|CUFb`z!{T%%Ps@cEupLm-s) z>pJ=)Dj~>O-TdM-mva9I?(dJ%W~S=AcA*rKWBxSlZyS~sOrVo3Y$?V2$;DCqoP5$_25^D7FUfCuXr+X6x9hzvQUy0-#9tM{jT=n%~KBtXLgIUw{Tb zhwFKx?#&yor>k8wW9@}Z4Oi5d;BRhWLt{p1GVkXdYiNLVH@KivdmbAD2HbKs{c6QG zxD;Xq`CB3dH+N@Cz=1qhvrv8| zGHX3fx%J>deW6~Jd}!*iCiT5t|7cE4{$-XugxP+J@^Jp8e!2Y=i%6%SN_P?@)Y)AA zp3%ijx-IU`qpvQn4;IQjw~Jo1scKjF)lxOdN8GKVYGT&^63zSBFl@%9>7|JfGPv|t z#$G<|)pe+4K&I$wFIW~mUUw&pC`?tm*1rYzR6bzOT=XyRDh+#}!33qG4H~^nOT% zA_(auK(eV|)c5q;bO#vu;+^qs2Jh^;!n=2MdNdyu{Jv^_2RQ@Av+)S&fU=%GF|4I8b8b%TxLDdDo6yd7v6R0X1U^fSIk zwcobST%HPjpsG1OO>}@3qI$$h&y#!mTpz}jpJn5&$Mw<3z5D%xDt8?*4hs7%I{zSC zMyt~dd0Wo_!lm78Pttf_rcohQ+T(0KfmwIva_Y!5;Dn0bGVyeP7vwc-6fdphdw+09 zq)I>r)uQKl>k~9u@?7(eiRw5Qo8I8YKL7FljouFrG?4!XFXy8w$rPrPB>0ruqMOYS zIxiEwIG+1RiA{Wu7FiD{G&Aq7z_V#`y;{z>t)&x{}(XwK9_XYz-e6HNL_U!NF670HwkvGes60Tc%n8UyjU z=_t+*vb*0qo-DJ_J>_kjKq=%a_p@v4xgkDjw|xnW@BUZ-cee3k`#8%y$%a6*vomb@ zHI1;2(iNWh4lF|j(DTWl*_{-zXfEwr8xGMe%{W zC5`-Q-ShDH`gTz4i%qHZ>geIghUCay@cyPaeYBCf+~tiDItxax+qN^Jn!nqWbxG|h zF?OWY^$7HQ1o%t~rl{8yG;4&CwC3oMacppO3cjy}pc%Whv1(5$FrYimB71W@zP>!T zjvtroj%ThWPov}&6f3A$W z#LO`rOH;gu2_zu>jTY{!Gv_FwYy`xX!^{0Z^iG^iigpEG%t5@^j=p`<1RrDjI(|}y)&`C!Q7B~=a(#*h?jb%S80Fe*^r!} zFpl9-zD@gkAsxOwL~^Ljw^JR5L@LkaZNlA-eXDM#4=ju-vP>5Gm5vV53Nt`Y%|94@$UXJkF$gR<2*H?6i0EcbQ6JCn%+j*oc{}0~uwhC@SW>?1gD0jEBbH z3dX}NX5;wm5fQ;(7!QU=`(WyPl|{;pH5H_{0q;rMh-29RAeQIIb5XYZ_dL)PA*kY< zKx@`@rZx?k-WcpR29a{i?>ogf!sQE8sA$g(X|v>7RnpZk5!iyu4zK|8j({^G6XOP|GzB zF`CMGT3ele;I}m`rv3H)3;6{jis{SrMk$AVPe|YE+gT(r%RH&AW6RJXp?>z5Z)sj^ zxn?H{d^in%oFvf}9kL(03nHATkB=~3V_)>y(Qs>H8*H5?Ol{aiy_37hQ8Q z)uvRrYaxYrv9D#KGoL@qw$@-zc#8SN4&^=8r94x783P_}fAdcL96FDc&zFW;c{r=QJmsnge?J~D~Ey0CgfKaG|k(7^rh>nx2}1je-k z{s)riN?4I!br|-%*I#k|U+leQSd?8CHmnGuB4Q8<5(XeGEe#?fjdY`QcMULeiKwWQ zbcdw0bcaew4Glv}cQ?b#w+HY0d7k6@_5JyNz3)E{Mdq4o@4fa~=Q`JU)_<|Dqoid$ zY$=4#NwdgQ76lI%fQrVEb)NeCU;bwjDF}^e32*XHN$n9V6qdd}&F~I|HfoGpl7WEx z?WI!`i0VetbiRfXOrtC6mbEq^Jh5nBQg~6I#hc)2Hyl~leB`QjcYZNiuiD4v&PkR9 z-3^e3Pa6V^>#&}XrpFgXL_Un@&(`kCFu6981@f>L)M2qNp|~z)qJkxx{E_=Ztf!*uJ1l5V?WTlM$NNCY5`C`IwU7DLLkH_+xekt(imZb^f z8d2{{X3#HK`!3^4U1YiI1#-Zr>~07S(%B!`*n7}|ljzyP(Osv-8|py*0;Luz*OQ~} z_TlCuX6V#or;~q8J{S=GrA^)UCC*YEQ=gxeTy55Q%&pUZY)y@lnzua$QbHL_xysFn zD?C**^2Q!ee?UfjIL-LIP>c=#mC0T;?iT_2D+hSgBjwIr4YXX|71TnXUU2Xvn@?g+ z-TKbPU)aG%N2t)tjqI&JSdn2jos05N+{Ko=E}T>K-SQ8sP0g-Y!}6pRVEM^xf-{V< zAuWz|LG#DsrJ%c0t;+`HT&}yL@~O{n2mGlp1*0UmYm?;CVsx4d&H>z!JE!@s30^pDIl_@)LQobj{JZ=)R!v(!9@+s z2JP=Vj|$UQWBIj98gJv4PP*X~Ydn*w6wez(u=`_@E362pC!IJAfP3;$v4ZDwg}hu} z)}x&{=h7Jc$e;KP_zIPG=Re%g`x@gnu|BF4L@Q>uQb9t?*Kf*P>5IYae>{>5pS=R3 zWUGu|w5QoR5SzWrdIc&CjG>>;pX&&b+>a65TIfo0FOO+=Wl~9VVXwEn_`~9Fr3ipW zK-7O$+$*xxcq>Js9!wRy`UZ;iBG`_ZCFa;eCW$S0NBOv08X01@Mj#_EsFg`X>n%Oi z7QqtQibli;4v0=xTIBb$;D3%zPJWZaFQ4^&#ip{N+LDCC5-^quztFP;wi!on&(sGb7d*qB1y|w-cO+sBXYC)&ygZHC7&Tp&| zFusaRS2P?0YM%^RyWORJAn;oDtpzz<=SAw0&KN2!27Nh8sWSYk>a8&277UBRlsSV{ zrF94-V&7^k&=$%0;g-sWzqH`Ghu_=7JNqy9M97QV11u)RdkG*aq2Tgw2PO-qYQ7BH z4ngrRMahNkgJ}tV`*BjZUDf09DE1_&=4Jtr(`aoP)_X(91TazOG#ao~Wo?cbVLv`L zo>Km8SY8$m?YPR=W7s4laU+;?(78wJxgz`W?SK%8a^NZ2LBHQ&=w|%R=hb~}h0gC< zG?0~SF+1Pe-{v^b3>`(}axJB}iZ2_h28$YhxcXHwFDr?J`btN0>QcckYgCb0@fj!m zs=aE|3bn5^W&CV*R$s4ayU9#OCrF(w1>nae01z3s^ITY`)~x=16*_}qd^~O(f;Q_K z420kx7D@GKF?aIMi|e|thPoW-n?yXJJ}iDb!Z>~Il1E+xeZiUlwKUzghHF^v-qRT= zE^}q;-%)JvZgs#5^YOU(P5EmLkL6fv5X$>?^UGL1%llv^*kP1P8jKg~=Bt<2joNa= z$taJwaH)4^7(+6i6Mu&o1{agT5JNCcsf1(|yzeCD?c4R7q*OtKQ-24STZ#M~Zb8xK zN2k#-E_|cm{6a>@-e6vu`_Bp(>!5lvpZY`L-y}|d3UEjZ@dfv9jb|7uQ=TjF8dCfX zU!7c}2|B&;k<6#B&`FI}@EZ)_>9M6E00G&O5}hLPn|@(Zt$rpIYWPO_AY^JW?xSxl zo(tq7HoLPqLHNf_t2{X8Trv)7Fb5OE@MU|m;t1BI<3DN7XC`XyJTX=$Ou9e9j^F5g zrK%IcFagh^vV>Fe6562TugUF)0cZFmsCl2z%$o#?pR@IVcZSmYkI$|{Y${Lu%$qzx zqR=o3`Zbj-qa=w8=UPYYAD${H@Zb$X`GHel3X@yqWk-`lTlidmRlvW^4uuyCX~mz3zP@|_cbMOtuGRk|>ly|r?E zimKkIBtyneB%*`{cm4_!yeYW+1hFsJj7V`l4dVPpC4ty~zTmM6+H#33 z4oAa|l}^)XY1|RoASztv2hes?U~x+#J>67FE+5LZ#1xsRuac=qW@cDmSZ(L4gPW=c zEUh=wO`qRa+REo19yDHt&E(bbJzm<1WJ?1{R3&m7icWrGuIpAq)RhCgEGs6R{*HnHk7=PB;F>nm+#a8zg&BFq){7f`z{ zga>A}O9@52_H9a(rk*^XPWzn-XT5fYqfVjlWWpYXst^>z+!&Xr@kAMtX9PLwhavsv zL6mIH^IZ`*MT@KKMURBE?i$~x94V%DrY#Bc5}ubR_aH@A9W zb*?*v&N4ZHMbf*Qr+)+tX2iUfm)RzlWs=tF{eJ4m?v9k^gz_oq$TUtVy2PL+i3oUZ`I^bUh+$rR<;79M zS`~i|e?6AcXWKZ;5^wJh`M4mCau8rL}JW0@^9X~{(HS@+I&A~R8jtJCZWLO74{0q2G)#2VU88;G9G~fD( z6OV>I`7%Dwy{AC@<=K4$hsM}uf=O((eWA??eG_0ikQwixU58K1qS*mG0E>hXRZwpZtm}L52;55Q42A|@!OM& zai4f$qNCp0ro{NT0k<477LD+5Ce@3&Efo0P61eGreXK6MEPmv(t@qaRlp?xQNI0Wu z*Kuvps~L$(7q;)=PLPL4R|0(=PoA1qtS0+?gaG%4asgaRg4@K<=ISGK}9f86crZ4=XZZFYjR$15;`~(u5?(?XjiS zYi@#BISyZ+e%=2B&I$&76c5K*kCr`_YhL-Jgj>b6LuhM;VUy{5xK>PWy1uPZ)Y8Vk zgd=bLWPq!Nau|xDzQ6X3f9z8ypwL|Ntz>1WpxwUyltE|UD{}gMz%40)5V(=LI2M83 zL5iuATB)0x25X6Y^`k*|b;dcl%vqsuIulji>0`Bq+VnN+W_)vRh(NFAeJp>78nA+V z_xJmf)o8-|E*= z19+V`?14+Hd6ip;i5Z>M#sr+px-0(iL66$-h|Bft53t2MVQkJB9s3TM$oR7Kc0Foc z!l{4KDUZ)@BKd*R+HvU;la{@f zje0R<%Nq{9P;Z7TLmmwa^JIOG7J_O&ZP&X-_=i}byZ;j|JWdAX-}w4M%`j2SVc+B6 z9Qt2C;K^Z`D z@)2U>Kh+Ld{lKY zQ0U$UNoZ1bfA}0nEwdk_d(&=H322^bRgF6LI$>BXMXf)CdslJ)`~Cg^UJNPw%b(+~ ze*Bc$m(Vv-{hW%Zt-4XwEm&$T1ITY{WjYsGGe#%iC$pzMzN@d3yi)@22s5*R208H# znG3lFO&F#UX!y8I`uKimJ%Gf}k7BctJz$=iW1~h$e7-Bro}zXyLq>?=m+1@HsB3pu zd+(T}wjfw3z1NSq9f44} zQ)|b1kj-d`Wf)Mopx|?!q_VHMff`4hwG`@`Eo@uLxk+bVvjqkSvOdvd9>q%bGPXs+iBDj? zYd(hXIn$F(h2s5^jp5%NZWAkGz`wabzg!Uq|FBgemUo$ibSEn%K9U5f7;+;pO=hIM z1A$b0R|+BZsiElGVoMWi1SgXjKx9&;`dWg?%3%Flh34baV6n85b`H5R8*Tmldx4kV zxtlP?9-bZ)!CQh}omhYGv#vFuAm;0JYY7+a=FA2#dxbC!(cb|4Vi8YpJ~hS*XsU7p zOiM0};{ebWxI8vr#i^S%yg45-kwgu^5y%anom%w1t{63=*pTJJog^C$NGXvQbjIAgDw3>@MPPqoX8iIqO}iaE=cLwwgCj`qcLD`HgQ%kCUo6sP%V54`+f5V zkIiwreyLUPZx!w^>mDG&f^%ughziQeNN)nIQ8N;L&|U6{s^)Xqq+77z9bbpiDXQD+ zv58*@{K(W^dxl z#-K(3s-GP|Eu?{?yF+r3tehmWMeobw^y`uM8)Y^q6}#Ay?}8UtQ%y=s+t#E__K)F3Rwu73;fzLM#Ij@^%$+oB_XAnTLpzpKq>gKF0~d+(Umi$bgY^W2 z1aQT`$9Tu>`de>oyVYZgtcUDyZFORg-^Ok5Q(IZuClG^%Y3mn><{%pSJKP`i@sx|7 z2Om15-4*ccPsEL*WeC5x7L^(NY3mm8M2}!66^nrLnz{!HMXO!-;KFRVnY*RdA&d#-uqnwe&ledoTXiz55%H$O6Q=g zGNraRn2m}WcJjY`qc2|wN>+z}Xk!{+!S|*}5os41y(e{gx!f1=L*`a^t~TV7;+7-| ze|Hm=AFlO5eC+brp76d(1h~Zin?1%xo z?I)hexzCT)e~xT!hCFaH3p0p-9!y8rB0mpW6TpnXhb~URg+Ntg}j0ql1>!c2*vV4DB%q4)&Siy)g^gPg$`qJ)`8V+ zNpiU>TOTbYkoOiU%Djb)7WN&~@TaAcom;M{v?4jioQJZtRS_|mFf~L;C|h1W$-LX@ zn58dN((efsXDFB)_y1POx6v+dX#-jx38Ztw-z<%X=IUMG@_|V%JTok}>h5w7Y%i2& z0wuC+;<28suQUwRAwkFrYqHLb+L;zOJHZvw?{Q07L*UZ4y&hHtRP6p8aY)Nn%a`Zl zagWYDBO+{G_5s5QGJw4__Yu~t)&t2G0T#Sl#ZWDnEEF0n=|dxWk|i$O5%u<{IQb43 zr;RlY`l`Hho1vyf=!hZtPX&}kh=vA$p_(ARzf)U0h!n0gi|Id^mt*SBz5GLr6Y_d; zAKZdpTEl5R54$wn7NRG7O1F-WBMer%ZIt=)f_`$C8pElpUg?`R{?icDYRE(8Y8gh* zI>db+kfdbyqqW^5I^V=xqXYCJ-t%lB;Xs?5NvGsnfxh6-iWHvCyu<|@jSI0z`3+WPoO;A_kKT&?Zd)_EuPvmL(`kH557?{yHz z`f4Btu$OimZ{iCpoN@KrP5N0Z5F`!*y9*hS7pS-y9{0T}wHinRd{8$dZM!fjWk%zq zCyqbOE`gDaP*$z)4Kx{myubyTOwAwkz7};D)i!t0X!*TU11%sEguDX~^+*+I6 z9fWLe2&!0dlk0th-D2u35=gJT|1i>lZv>=#odG&&FGAiqzZ7+&=hUqJ-7;07w{g9@ z*KB~si`76d?w+J4?3;XyhX6n0nF5129Wx$fJU49H#IpZe$nM6ZArL<{H}uSX$k3Ey zRz^fAl%M_!N2aKTt97-Ao)u%9&;#XK7g5mclex(l+9Ep_F`v<>ndi47_u1qsL1Tch zv;^Is%Zu-**?iIqDy%qp5h>{dIdMgMFD;L&?Idokrf}d?3~|?g1x0sK9B_n=94WPK z$2G=5JHCu#T&@_sB-irguEK>+7fEPXsRdmeSf>HVKP^G06u4{$U3e3kA;SjX;xRDJ zD$^xPMjvu{9@i>5xcqo|0Y^{%hc0psuaLPrOO+9}{hQIWCq)W0pIK2Aj;R)TZV5lq zxhu2PvW%>2SHy$a|E4321r4c9&T?f}v*uhH2C&8Mw69du%`dzW`}~HETutq|as#{D zOCCsL^^(?$fVp7a$YJYM`uz!A)}CmKRkr8GfwlarZ$E$jh{vpzg*njEAAUb>8l~^% zZZ;8S9v!3GC9)tQ8iV4VJXL)Efpy5%=k^!VV^oT}yPTK$m#q)~)mZkax`H$0IZ}8X zEP8Aq3hj1qBO5`JJ8@FDtotcMG}n$GzWd~{;eHO#I&`g#-?@DKTdH`l5Aez%;>c2# zyRyBs8&_JCoOHg&b*3(IKRbDb*yktC7$JHlW8fd5Xde{wmq-@M$FIaO(RmaqpkxDH zFWSkl$Um8rlp@nrgp(ho1Utxd^HC3w`+e{b*ot2H6`WQw^yZ%@RgMsEyI2Bdi57yh zg9;PQ`DEPdu4_-}aV%AzZjUnfbgiA8TIAxj)~KUcXCH#t=NC%fp7Zg%0Um=RfcYP{ zD%>v&un1`VcP0z>y8{`&|GL=Tc+BGRX&_SXiBjdH2b-hme@q5m{ryui)0JGr?4O<@ z+tFzIPl*bJu#snP#6^H5NH*a!^)t2c|0sm}G?OmYh@ZS5dH>&+{P!P~V;bJFd2i@7&bQ$K z@ZbhEC2I1iSsefB(9<&l9s1c1dVc>k2_U%qSq3-~a0X`7Mcq5aqlub2#k+VVJRYDJ zp;S)P4F9+kJYoRO0Zlxy$4}3k1pe={{C|2cz$^o-kq)`4`R;qLC0V-5Y)7?#Z(%CT z+0jqIJ-o!m?Fws2_fwqH4GMPI^_E2u-ctSP?j4+63B%}f;s=j~2n5|$7XfwYa_%|< zTX&V~3Yz;>kifh}?^^#8-0u7Ks2lJWEhg*WO@R|P9^LS%oR4L}VpQ&r4=8ZsabI#- z?*DDxaFv-a{Ihtq+uAnr4G^V?kd4|46DRNi(}qua?W<(}T;VwpV0l04&GugxiV-{& z$MF5b6_LN2%Liv_uz9}U`=8C#i}RounJED5R}9|$tH42n9c-=!Lgk(R*<4p-b;0Jc z46dK}>wIv9v;u4_JX-P2zn?>N{gEx$Sm`1d&ofVoE6P(i?+fp9ynkIau7Vd>LV$lR z;{WI8fWUM3M5d3!YZ=9p?-4L4#F7H(1iMzbD)RpvTo4Rcy8*Qh4ERYsDE~Q!Q-@CG zJ5xLcBYcDNq%FW3>Ob*l3J+Lw`vX0HG7k>f>LYM+z0iA!;=Wg|0@-eyxzxql4F7+n zzAr)7>AEZM{5k48z{pR!Bh{Z}Tx3*c@d1T*6DYi_U4wl8$EM(sjSrvg$g_=!TB7P( z^b^NsV&>_M#4btad@kNR+#j_!MjvhwyYNQ+Phd-Cx>RC2%GWE2`#YbXH(sEi<{)a_ zC&Ys)U>DK;8>5z4aSmIHz^*G{<)y?72q;#%%FcS-Qs{{$1l%N9Ifi36W+{5<<#I9HTq+*VotV0GqMLqykY z0T!JyaN_-cch1F2{rX_%j1v!>9W-LxyY2zej+e5Q=&vQqSi45zJYONVsIgezXGq7({pz*8Z;D%gWN9pOy4>LY5z?1MjcCC(9#Vb%1iRmkMCix zMhnB7{`~=<8b6Jr6#nxA;qZ$%HL20Dssjjk!vAIJ^9ZYhz3N7SUIo1+?uO1)dJOH;%kDUt2~IHgf0O(XI-lqqGn%vi z!at$oCN6aR^etHVcSF4A0PlUXMVaJ3em3}5WN}70mr3gK|M5&Atpm&blU7pXAETq! zU}for539}`m@X?H;^GE$bq}|$z{2PqE@*5{tqA@NUVm`G>;I@H0Pa5T2y`MHCjGNl zZZXJz#6=?YB$xlm?EVI}Zc_Z`fi%=xJIEe@u;A?0R4r6qmiz%b@+08Gil%56YdQE3 zFt3FP6v%4Z{A(BSPrTs1)OwERg3N2%7h!a#7q15J4jwCvxgLnTAAk;HO5A_~)E4+n zQn4l{0&ylRja;v%8xI##UvKRV8B!SSl*atOQlM6RK9r5Wmi@25X<)r#r5tP&O_g!ZqD*+-b$O@Hp1GNn`ZdXOlDkz5o3k>O{c1rL?nOqy?gejB3!!EJr8o z{_K>RdTy3(#wx{GCplh@z`eJ5kALh7ZEw-sT}Na>xu&~E-T^W7u>{SlP(|;SinW$XOF@(F~{gxALDZ83!RL{0ekH)O0eaGYJe3w#}Q(iIR3s!{rP7;Nb8k-#u!g z0CrC;M+mb`NhU%?7VQ_4;?6`$K@oJJf`)ajRJ=T2PT&?ThV; z7>`7c?JVyhVP_;YBX%xr_}j*Z2x`IhQI%15)M+rM%VPJP1Ww^fzwonlr=u*mwM2Okl>tX#>S4&rFoY$Y#aJ!yc#-CROG%Qh=M)Rcm`{_beaPFLK4 zC2$D*r0+KRfaI0n1~7M@CgO-5Xczcv|K8!|!%@Wg5ep^_W||-h!+>2uf(l%Z1t?J5 z!U^iY=3~su%6|}mjojT&unu>7fkrzw?ra3L(lTO;rFQkRbayw3pXHX?WLj|Q&-Z3b z(5^jr%z_6d$b&)8j1NoY)m=8)$R)=i!ZHxPD^P4~mzE~sn)o*YT0HhCxzY?+))Yw5 z6B`KV7T-TPSzOSv`1(EtV6laBk-E9}WD5TLGMARu;AEDbzr7&5y_kA5DheOW1%9EJ=!u*Hnw-2U*}A2@ zANJvsbG&E4%z_IK$cfn5guxeNeu#Rl7E0^E<{})2^xd1N-N$Z3={d32_n>z3^(y=iWbFIP?lZSNf_>u1#6^<7 zbMF$*H}R@Mv$fK}&rI4nm@p*)Hf&F(1eLO%zk1nK7h`ee|{N*2Z1^lKDq&0=Zf84xfviH-LdecSFaafi!}bMMK7*W@;sVAYM3iu>d0e1C#P^mMlmx)T(G_yuP6roZ~peljn*v=s87 z{v!Y`rG{*lzVUo2TVkMEa352G6j3L-_SGT`3W?zyOr<7e44y~Ziu)HPfZr=ko^EMZ z@tAy~uz&%r@RLN(@lT| zmEI{(0bs?dCu$A^kv?-BpR{jv&kXOHi(1QGjVG;f{zI@J)pjNPZH`$f4*?|9#qgsLT$_-qAYR_b3-4K{3`gLT|#XF>T3wu0NUzF=N zw9vvEA!4eicdf&2WRJ(7|s6yQVfL zwMy6h4F8(IVS~-miuty{$CB`YL0#fp)piE6cAL!O3D1=^kCO!V`<();_0X78VXrJp z^Oagpb?t-Q9lE84l9EFz`??ThIcrkqYt~qJK_!2+oQQ4x+P=_sRUKw|rDAD5E_1LN zyjXM8wS~lE#4$x`QT-A)YB3&MafUMOL77J(7!>iVgyM6{BatXh{?Pf1^$xrWF-ktx zm4#unmwv4Ih?9Tk&W0vs&G)4idiUJXgH!&e{RmcP_BImj}TJY^$( zaAqbrLS{JErXbO6BEYPe{tz>A&_56r*64} zTQL}xqt*5x-KGZQmoq7H?441|%tB%cF6q6zCo1k3*h0Tx=K)JW9l5FdDDjBfN|99# zTv6L)IW_Haj(X8lCTV~(_5w-2z_~{RWO-2Cu@$%N$X(|S&b?>>ozmFj5u&rpwtE;) z8d$Hz0|R(%I;Rz!m$ec0^dJ}%^c~%~)NaG~aGqXIz|7Umm@_hP60=_pGouT8!mW$~ z@hgNVw^ncAiD{JUbfURmv;PK^SQMhEEqZ%PA>RGO-w{L2@q8m)u;~pXDq=jNUL{q{ zhhg}3voq-J%@^&6^mEZ(Sh!()T&LvjxP$MVc{!_*uP*#Y_xz}D*w^i8yjR7*C!y_w zdg>C}yL$@{m`@W`5Ht_j4ifOZyM#~TU&z^{I}Lv&Y-xt`w{errQ6Wyv0T`?~TsD%O zqj9Gq6i?r6d?pyyoLU44(j0F1I(C*q{`K)5Pt)i-tW}r2>}H$eFl3pDi0A1s(ZK)h z^RVlJSfd0Xjk4t}>uG-Yx$ZqwpuXk0r4b*6hDM_2anw;t$!v>&d4d2hl@kgRE2=b^ zxP)X8V~oU0Ff-L#7UbZE$JP8znw!`idZZ|tdu-OpD{+H6~$F&@t6d>~w zfK4GkF(%$VS+BNF+kjbi$=p+>@?l3!l3OQus-&4V)Ol>pSt%)KzcCP;iT7CQa8B(T zfu~6Ck6H{V95>&!9o6tkOaM8`BXW>I7e5a5+9)+ot466<&F2*VPLRAN=wQXRQFmYv zuJD4|8=As|ynUL@0!Fs!!BY~4*xJAekZ3UTs`M&iNPD>hS}Y#q)p+phlrmXai* zyKB|N40K!g=u|Vr=eL_L#VI|8$w}RRQL{yuHn zkT-knKv=tbQ`)&UENihlSmsw=H+lsmZwDlymBV|TIr^PlWll!F(*@!Aw_jpyCMhb zmXi3@DDle$3`Zodd8PTETGJ0a?}7B@Q@Rw!$hs z*S@#m*5Zx$gUiBS#^vFgKL{5lGwrI^%7%^n*|KeAstQ>NE~l^d>e^MB#v_iGP@U^? zO(4_ITZ6gXd<1cHXP@d7&Td?s3*Hc=3gI~2dtHVe8nzh-WtQ7-Irs<+6KUCklenvF zi(W?Fv3SMWYaVDZZ@IVHc9FvSwzxun!5Xe<^T`%@)MI zCEB6vOKZ8fQjk=izn)Zbb*TF*?JVjo1~Fbr#_q}BwyEDZ+iUA+;la*$)_O^erexH* zKS!b=jxn;~36U}hm+*~H?Gg1WWjrdeLw9GBUk#2Vuij|FhN^%z@2&qY#w#g2Vh}KHrclID}udap0Yu36dllCXhemXo>94Pt+#}!12O<+7+#=;2jP8W_y z?z3IcP(QN#Ia{&uo@A#5&l!2=ba3$obUL(pr`;u?K3mm>u{({RcU#jEwq}iS(@zu- z=(LtkVly+NesbD@D_$Zr^f(RiggbwPb69Hc)M85vYYs3cN1@YR8%HhM)tPmQ=Ov|S zo%Pbv$4+d4)Xj3qi}$a-`WMYLsS0dyFfO!LxUY@Sx0Na6rG-*h(k%AMv1dcqS;Mc% z!B2m&S4-(st%iaOn_EEB$um7}+1YLuKpE`?jz1k+=687KI1c@G;kL-ynfIV(DMpHL zARY7v(48Ghc5 z+%CA>|Lj$MSAtQK*jj|xb8~(IZ-=4Ut=ExEF5YEn_Ufi8`;+U0+6~H{9WiV~gT@V! zJ;pibLn4V(afpBoUZ;PaB=JDfc#(Jv!F-Zfui#pgz9VFcK!xK~*~sMnA`wY@t{~$c zrSCd7=h$gj@C{TJZca(OLm$;OwFRIPaAqQH6Vn$`i|Bv05n|J1dry8{6L?a<0k`zp z>HNG@+E?a`6cU@PpV_Z3xQ-69@4#5bnbHex9UAY@68l}fL+L*0Y>e>e*C3i8o zBtYjV9V#^QG!mg!2IDtKgsq&2x`-sN<(*P)q$q#=&STRLBk&<@qWRdbMpS=KIWA< z!u*ZQ&gIN_PtWvrWp(<^?q4J=AhC%>lEk|$|K@~mDhb**k!gICBoTO2Fulwg*7=R~ zfs!db!Sr3k@Yh0Y9rSs9oC3THRn!*mc-e0TDkOL>UHYyO>PE$@gn4QPUJ%>?qt^%F zHd_(vlckg$WZb<&#!`Ay@e!{AL%a-JM!8x&nyJp`Xoj%#Mv5+sq0uG%BfCGuETykp z5d$DYYI}-*Gtf4;rKtx~j~L2X3DTyH`Vb$LV@;a?#D8$PyY3W#Q98L9uYr0Br2W-8 z{N}#Id>=t>1u^RC8^@bASmiCeYr`kk`l^}hi0&yI@5B1nY3kkdx2>}*u^7lMlFYO_ z*=m2eH9r9PSC@|ouCLcC)WA9?r~Pc=o`Oz^5$F-~_& zL2z`^$v?{K$yV=;sbawHcJ)5ssF-@IPE&PQpl$U5f(z%i8Jm?oAgD?c+G1w0yr0W@ zo#E6$vo7sTVu`Re^QdXNmzp|xCxdgOpX#Wfcd{&n>Bv@P3DBxvxT`93#GQOr=u*WM)swf?nJ#eU7!x`U&#% z=&QAvTq=h~C)XcclDoH^O@6R4$#p`Yjq%cQ&$99pF~3%QFWeQSrT;vYOjGY(f#mnS!(CqzuGRK4yv?4_Hcm`)U&%^I+o2nN zfaR)-pw=9+b-feyAsXxgZ2aF{5c^gf6|aQ4+nrmoG;AE)=^JWavjsH^hjr)n|GA6T zN5q4~@GbVb`)XlRc8sbQ5xTx!$e~pE%m!M?VlnFQyJVZ@_U--1g;fz9eJy*GjuQtYTeca4w|W>$!g- z$+@+WPJiB$e}=xI60nAFeNHKZD>kz93d1g7y2B@+gm`nwNA-EDt^ngr+4w*eUYoGwEb9(ecQZF)&M7Fv`;71m8udZ3Zt9U&2nkj@i>ALy#1wz$i!y*Xbu&_d2*FkxOF<)N)FhR&do=kN z+BlNaQNC>$S`(vyAZ{DBAuKBj`?PXXQUbw_Mo-XO4{T=T8 zWIOI~bElAV|71(ufLHE5C9+=VK6Dp44J#ZhN-&;0pYyDsDf1MviC`R+NWMW()@m0} zH5UnuZl`B-{Vuv1ZLkm&?$KC5TW+DJ9^vz3D~AhvdJrc36;na9RVes^*f z8_&KxC_BUyrzGp6IB6c^r}9M$9);p$H?@UwgJG3LRl>J$Jz1ZpWH0!GNxCA9^gWl- zjVlf9FD6CQxd5@WL6FaS0B7szD)>S0T(ugux-VDnmjHff1G=4qVy$?Rtp&09I)M84 zwR|g=lDaygt4KHK3^U6$^^fKjI>@ z+W;K0)l1=J@)~U)R7XQ2ld2eX1y}k2O4R?YN1Luob89Euri`EEL1w$W>S&dEw>@;}{hc`rx#oB0XzF?YIfA|Osx^3Kifbr~zal4?0JDzhj)9_c167e~ zEuhgmLwg`h?#y;9XV!4VwQfh@efh-oMMUwYR{4r32?~Gu;=KcnM8wO6Md8cxuy2@vaF8EGC2RxZ&^K5uVG(O^%Ne_ z{0_-dMs9{m+q}m1zC3@uiaynfm)%2CTF==Y>6s>g@o*H+T~(p8LZMq`nRt=%YWi{Q zcm*m988HNc>4nTJzlz@E0SL2f)z(*d$V89Q%OGEZPVcOVdZ}Mxs*``R1DPLulsZtX z&Z%;)8wx9*ywkZ5Q*bTMiP`6v;t^70f5Am}@j-$U&OI!Qy z4Fv<2Z(f5o2K8QQU0iXOXC;42uO-33Xe@yMn>J*cj-GvXv}SrjwJea=W1x{;9mh<~ zeK6*{5IoP_Zf>aYdVg$b=v*>wak!@;1m9B!O^vT(6DVxa>iSPuQT29Pqy$U#vg?om zwJL)WRanv5Ql;z4^3ZiQq5TYK{BCBvn`asr)C7=dehs=8XF`p#L!PZ$at}crhWBI= zAS8MVW#I#yF!X_Iz8nGWAC)V`ui#fPbYlMN~1Lo%~nfb{tW%6I=tziwlU}`;-o<|hH#I`YakM{Gy-#` zrwZEV{jE4Pva&Py3>4r7`BxBee;u)XiP9$RRhZ&Vvg9tbEgybO#0y*LC11GtP4-n= zqYURN27BcklzpWYU^}t70NU}q9Sf4Mhh?LPmSJn8h?(b5-)@x92AXKPt9m_%r?T`Q z5eh(@mVJ~eqMaIvl=Jf*xntfehJnrOX7aV3l=Q1JGgT}=!NO`AL#);9Z0f@3VkD*T zxo@kAKctHL|MW$fMroTJE0lUds5#9>`Y8RxB&hhu@k&#rc@mkaWvaD$$Y1(IgAJ(# zm~S>XDW62>F64xpUa%Sbso5cDoQ=yT1}>%1HeT|I+Qg9J&OhE$G!ZN$ z=$aPyb#g6bB=63sXwxZ25Sk0AWK65I&9iOu6C5kSI|Bm_(L2M~2Hywc9i<&ykX%b0 z7@TLx?`D|?y>oA-wnlEZ)r!TPIM-#?fzlNK#xad5d#X{qQsdKDO3_Com}fg*Sk8Kz zAigwnM{~e$-Y2)8S`p-fX)i@O2KKsD8B0=`k}f!<=Ya+eHGgPxbgsO@wLQ5OYTl|_mW7jnfR$;&P)gKYKvppmS#Acp|u zD~#H?qf|U$Mddv1yLCMJGT%bDzjS9b&#*j>%q$m0j#n&AN3eI-ari524xk$jHKHC4 zr4%~eg@$!e-h$|2t6(`HKXbWnnMI%ko&h;%a7XlSK_LI>+^Gzq>W8~^KV`})5*%V zc|^8=OGb6ud@{L(K#P2^>p7q2H(D`=>qn!u#-AicdD`am!@*8*z2V^HroY>k;I;UJ zFv3f-IO;*sxp2oBMCB;ry+LiZuq6bzBI@gL<3gVMt0Cr$vSIg`0$p^rbF;ZtXM$5z z`9k`vf?^>{Im`SLdlZKu-da`tj7#hQ{3%&ZbEqC3$(I<7-S-*|q0|Q)kp8Y!u3i?a_3B3Ou z;L>wqSH^*y;!$WsskT$9x36k4jz*GzZRMFQn_)qB^O$u+d#vP@;~GVHM(wFEeJ;F$ z+I^j}cQ(L3$~QSM^v*7`prB)KxW{tdAIFVi$(f!Zv7p^L2Gb>W>J)6ONGc#|8vTHc z49=3aTnGIb=(KED*sgAG(0gguxtO(eybA4<`qGqG&*?XPaiyQ;OoIDlmd!!GZR+#D z&poIEf3-*htKV6pM0-+TQ9-kf!*0neA6A^+#lmunp&xly6||UksH#9{7uu+!@@LoC z8}G5lj^roG?dEP$7Z&aF&XzrmjvEISG#*nvFV43!)`~nSC~oszqkHTRuDbO1eqtCCP=1PVq6=G$+X_&U<{IPZ zb~Y-aeSI;6UXAW9%6rFRBBiNAu@Hy><0&YLUrc^}HxyRPA!j<*&QZ_mrTqu?bpI=~ z3u3j#@ZcAFz~8&AOMLf>;dcB^S8#t~Tbns*TVLq(b<{AzFX4V|z53yZM%MZSepwCzx)I}0P2u(uyEPHkCRpi*1NTnD|co;vur>SCtnf!Jm; z$T0R6-pvG&p;bqJI11xV7Y|ecz&qCK^+%od_fO9`fsRz?_Fh5Na<+G6(D@4&d9rBT z%qk@nLAfq2a^)(=FRNSln3ea zEzuul(zHBNCuVUN>ye(DBr)(@n)5i0$u7pOM~=sHOS`G|e7Pq%1Fd^)e%>3@FK?T zmZOq^Twlv#kMT5)q1CNH`+c(RX#ALI(+u)Uz;H}}`Go!z>~5dZ(d_sHG{R8_fTcsH z{-_WY@1YFVNH&q}Y6;(MRTbqfx6zGgjxAoR-9>NRw(On#(Tzq4zq^#>laA(fc?Zim z3wPdRS1GnV4;OByVd^}DiaIWWxAfYkv@`i|sIz=FGr#irhRsshT26L`#%QnzwEoUx z`gi8Gb8n~v(|dQ+UXCYwHBr6>h127|H;cJXYLFmv!4?w2?6AU-FWN=pLBH~d#0vy$ z50P?3R~*tU?}K~}QE8%228O4Hb&XH@)<03vdNI}=)TFJ^7IvE7 znE5VIYy7FJ9-V9$eFf4{n4<8ZkJP%4JdB$N>_BX0W#jsB82c8(NKI_}@66^s?TjfN zMrDmU%<;+d zi3hu-P!{i{9RqlFh+n;c%yXT{r2TFBHt0pofu2l~c{;ROzfpI@wQpHxaUg=Z%`}{u zn({x%(2&9E_f_2Z`-R zU$j1YO2kIGkO()s3-~iIx-N}T=vtd^Xm8cpMlBZ8|GNTKwA)J&?{3!tWNAw6be$Ia zD^>Lz{zx6Y>k&O!OBxD^TsY>FV~pv28X4g_q|eCy@ zC$k}A+-*F)E9|`~S>{w`k<-Ay&@<1D+=n^liQe93eyG0%xmiv_1{7yUiQ_zgZVSg* zADmY=;{jY_FfFZN79iow*snJAr~Ac?FBQ7)O1=^Ax#E5D>z!~lQUq)$4w5_CsR%y% zLDpl|Q6|G_>(mGee}Q5;y7%IoS5^-);nQN+N@%%PUwv;&>135U2@>U6$8JC~9mU8` zJH17({n^MNrWHHw=0Ce4bKfPe1@RWmNv-G{?~l{fGqH5qrwK%<%_mmFg%5T)_z(L_ zF(dJYLzN*4{HQ0rfrL3?gtdFjF`wzE|K}2o=!NpX?h}LG- zh`A_Tc15whaM_&6SH62Q;eCq8;-^tZlWsfp+i}^OebK#%OtW!N*PfchoZqFq)6S(pM&^8-(0YZj_m|UkUWciM#ZmX>hN;;8ncH}f z%qws5DehG<5)(P^aVkSIxl2Q7JnfM?%U~QMrA6aH%F1)9=k0v9R!*_TxGKT{&y1W3 z)~dn~$HHFCqlm=Q=$(C0)xMgQnbcL!WBVO*ycKmz>7zh#_1CW7cn+8?WZX1rA<;UV zJhN=fai!A{{fV(jj-&qOQhxRB3wkJhLD5I{WaA{$YRJ1F|4(O;%!Wu&t^iB^_osR; zTTQ6rt&KT3pFnDUyJbreuicO1?xXZTWKSv(3Xk`6+E0-yqd8U4_S`g$G+-Vyglbn| zOWW18f5SdKn}ehqzsJU^bVa6ds>e5$#+w|Hc8`GnKBu8npn z+C_B;Yy~nxY7g;}w)r4t7Jt?nhj{PaPmb+*<#XEM5Q~tRn;lCytP*9J{7<`s%7=)( zg3iE;P1kG*Wxhi{w*fdZTC7Ba`T5>Qb70hD5-Umr*fePV*p*^3PEx#5vS{JgPp?Ag z-{55>-o_07KkU7ASe9M)HV8Kzih`gtN+Y3^bcl3HcS%Z0N?sr;2ntBugoJc=H!9NI zjnds+vv2fy-uE{%|IRnxF~|I49QReb)?Rz9^IYc~hu(Nj?&Zy~m*kAg8D~D;kCzH! z9(5eLfpGzAle0s4w_7`5#(2;I`XQ92+L?Hhk|NP_*Rpe}-pkuxU4E<9ZrLnje?qLd zt>Sj#rvNm5)_Xzb2AhldO9zhzpr}O8Q%hU`#MD1Z4sz+H&+IpeQjUhL$X)`7HL2I> zew*JO0aG_TJ8u2t1ZpAPFBnFuP&w~im&a}*wt-k3?M;*%EoyJVNmS1BVZW;dmmW7n> zoyigq22v#+4X;lGa$5|SaF(N(MYqGQD6}cJrT7#`qd=c9me-^;^ms}5ig+pBRX<_F zZXr^m`%V7V;h3^$TU;!r=V|MUo%zI&7)1M=%evlldf-m7d+dj}!83UZv|QxWdZ;CX zKOU8=C#{&Z5NQnq3!H^jyZsHv?fvKqM8Lr1|8%neS47@qzu#XSs4y zfgab$VDGOCmswp#>y0R}T{|6AO3$g}w}tmQIbO|wmi!s1p-)Wz#s2B#asV%oCcKBvP!K+SPg0}qF)5Zq^)}r7HpD(2Vg5Eojus=DNh}d$YGAcxX3MX z%CN>Ve!_KUTFUCx3+&FsskFG_&Irgjt7EcNV6QXYT+3Ff!iic3yI&^c>BD!NY#J8j zU-LvHFN5K<# zq`<#XO2S60?)efN+kLC<8d#?{3A0R;eQM^O;OsaY5GLv_{vgAkbobQ~J{6?Yia?~h z?U%-bH(keRdSHy1OTVpoyZvtRF#uRc!-A_>+Pa`=IJYG}Jquyfu+@xB#gy@{^FQP{D;O&WR3X5a3q_T$}a>u?7xU7RIn9^ zop1$+kMR&>dMXdct@ch*H1yqQ`(==_QfGrAZZmXIZd9-3_tkkE!B%3-xW#?ls@W)T za(f?2TvgZFXpzR5?nT58Z-%q&3<1twpYq}kI|+zGMHGTq4nBpa-ai;+RoY&_m^@d= zcX~1sB77Nbejy$H1c#+mDV4thLZnP@9g9<;okXA^u|%@04GDWRm;yAFVfiE+WLExk z3L-dbOe7`yF0He7=zKNs@_JaW&mo|xfWxveQ64CUabOE$=|g4wA3stleSz8Vb>2mIey>~<3J@D^ z2&`+Zh*jI0Sd z!j^Ls@T2vUKqcuDbCJEpP2t$-pB`YwaGLr8ARRwYO`M8|%d|l?ixWBBOjvwO2&};@ zM-sKFq<%xV$e5iLCuT&p$l2jW3MdMkHhz)3>{^moqI^fN59@j$v1u7*iE8o5ob>*o zLNQQ)acky?a{o)N`hhC`IwgQFaHaWNs|zEVSbQO_9@%b!U_rK6q||1hbce}!O@0_Hl7M^gXk$nTcEGp|l1}r9 z$d)jGBn*TBguV+Pcb49%$?rimo~(RzV%`XLMJYeTR);M$+YHb(J;GPDKH9lisH=*G zpk&m^5&SggxjZU1r|c|nhCclI?tfQ3r7TKB5bGle7ts5{fTSlmx7VTE_kx(sm7d3ZQrvT%||bz6lLMsWWcbfGElNUA|P; z4H}$p?a(7u^v>m(*zY+-p$`;OHE_NUm)8k<#EOw=Bzr1)1O5}As<#!!j>NT##CfAfS401jO8tgSn~0E>*nrXPe~ccD3dyesSU7QX*-ERg~9WAb#~Y`U4Hdx?5N3}_p2+EtGv|r{gb5! zAY7u7p-==;isFDI#wL&YHxL2PSecD)?HYn0oslVq1pbx5dk3epu#Igvb@8yal{kR9lcpo* zKlKrLjy-v{-urqf{PD@$x{g6WS=1UC%J4d|m&PWMjrIvg83Tr12Ieg&GrSQXxJb@6 zT}3`#9Q@2f*0CjC1H?T|ml#vy&8mr(?H-{Ze@Zt_+xms!W57fC4d{vi2v(8z(S)dT zwe90CwL21|;_eK8CTQb20bry_2>|ZQV{izP8q+4yw)na{RpZJGplBsq$uC*4d_yQ- zKk{yc{hGOeT0Wh;*lex_6r#qOGar@|FSxkuHxYUSCYThNBd4s7>Gz9?3cEx_+_VqL zq7$|R9n5u3w~y3E{_L;5tEk>Ud$kfAE$0fL3o8H&k&L9Mo^em)dyh_1Gi;pRObjDx=KNlpwq{z%VZCAv7<|2PEKrHOq~AvdbZ zzy`JhEngrv_~Qe_ON)0I<2vm&7t!$L4ykRSH(Cg=@51W3M(;+=a`KF8$Xz=9d=&WS zI+yECUlxHhqZ&{7y_wXH!$%iOm-qIDH2Tt=*G69)O$8Rkoj$vsY_SoIsnSsR@9doX zKv~u*nrZK|4Vj5yK^=ETkU|SYV5EVs+RKYzdS+gUTVkNt=!y; z`ZI$V9DL~$J=x2l8Sk@W=9(Y@uP0KjR)DAB-UrUpQUv^fJYp%Z+e*CyLKQ#3fiNBe zd&*_PrU(Q=h^jtr0_|wsv_0GVp77&MPcx}YO^f>SKIl_|+askOg*`R5U~osH5YA$Y|I$lsoj{rn{Svqrh;#viWvKcfmA4IPI_&8kA z0UmK%8c8Yg>cc2FuYqN~Do)jfg9uoFZ>g)T2i2s+LFtMVP>g8o6Is`uTUrYxB+$cj z0fH91c=*)-%x1h%Hj)ot%Sq)#t?yu$`Q-VlnTh{wLAKzTyTBD(CBTh+$arLOEW>AvnFunNs@dE~>H_uG` zrma={)RdZfe@Hl<;JFke5S|uC#=9G2ZIH7JN(iM=Wgw!-SG!s+)@nT;_J;ng0C&Beqo%2W@_i;w25%|Z4k{&*4^-k{qS<%U%NY1-^#)oyR zP}fu{tpOn3wOL=9RB7_-?3RJE&QN^pmh24aK$nSK-GNK(MK}KmZ-;rz0}XAu4Z*wI{SInU+y&K_C4}JNiy`kZ}DQY<;P-r5DSqH~IxpZ-VF zcZEH77WM%IJCWVU7`NuC;Uwk2bMNQxi_65i8l>>-mnb}!{ZmslP^y%l6IOp-CIdrY zQfYvpk0t{w*~iz{@V0V4d4GRDtbeMobl-K_k7H(iv(vhGef_*=57%+&XVuarE8q93 zEjLA!aN^7&6Q<{rRd_g{CY4VECJ-B8bLAr&$r|wHvK>@1>-<8Sh)2v6GL&ny_&G$k zy%q%nuYFB$IyC+Y5{LK|guNE!YDN#!xcl?0;D##iX{4eX%_#fHchS*hw8GQJRbm88 zvvE&;%eI;KOA4*+zSucG+1q^@SDsF7Q7|C}-Fq%|dUU3VO~|zDQj>q~IP`L*Z|_E& z_C(RZZ(7+6;}O>Qv=fM2CD>+jx&@$$P7f#zm#S}6T>v4XhH!4S(x{Q@IuZwh#6LA8 zUmL<=Pb5K|y5&IwfL|`wJKP5_RsmxUWCWQ3A-O%V18f_~S#RsW<6MN?hHBE&U+ag? zzuKK+YP=gdHOq9kHxDL1XSA&II1SgM%j-Uj44Mz6GYc$?rQMV2|8X9!?!^Eqce#<>&z_PF<}5kS)R>(%-=>Bl*cvG4D(q%lHgo*GmdhO z_)}u+m6`|?XR(-wFm;znu3bkZ70Rus`>QgCR5kXdpLbBd;Y%g( zh70VfD>Cl=TyyXoBI#RcyxDEQP?4{H@Ot@RPqf%^U-L|cJhVmOcGL)Df-Vy|;9*$X zf<1;PXLcNoRF@{?@_p0Q(SR$S)^Vu|?r7G%U+~_GdTZaE|69gnty@s3S6ZDemqDXe zu?hE>X97F9#JkS(Jq?qt@v_}BppUiNl4!Ejt+)IA=t!byMS3@yS&+{`mOt36J7I$h zNy;yxl#}y$ZLq`1t|??WQtx-;vMuy}ooc?ObUU1l-gR^GGmB0|Ao>kU5#@@_f{v|l zI;p_dNvq#PJkt;*GB}Zk6;KMo>&_0_wr^8!U~-YiOCXBY0wJndQ5=NQFbKC;a$LQo zKW^#M?;1V=MjTOj*#y)q-35HQ59r8l9lw`&qFfQ$F7kmi{mG|dht9|Q;xY9vLppWq zByYyo&);4Ob~CK=oVa}^lK^VDvJe<%U_R7fAidh!$^3~9q*_doZ+O0ns5QH#>J+&t z=-z$~p&bD%R)NmQp`Hb0w|=;m`T5%q+$r50EQ#U2X1u5ux*`hi2brB)4s4nZj5um3 z$9(L$AzwZRJK5_{+pBS?Ss96W0{O~)*{=b*3~Jt_9PKu@LQ}eLKo_)>-up!yw-}AS zy$z%ChP9aWF-wiZQ@r?yIm{x)%P7K0Le$~V+K62%K8K;z*O-LEZ_PpdYw@lNaRm(@ zdyhn&_Gx)Z+pk51fdZhe2ueWNm6W-_@<2w&Tf+0X_OAo2Ydi6L&c!M{R%S`5(thR} zj#G)IsJdvsV=NbnHS#SC2+!)x{d^7;;_tV}E1l@=;r6Ojy`D|7COeJvGG)~Hi4LKl z4Hi87(6yz3jVPt70i;m_b6mN@Mk1*_Tr4ki9}=Go^z2Sl?)0C3Xz}}0iLF?ch zzlzyxD;gU5cwRK$P2S2E=O8WfsH$7bR_fr8V|vQ6)iBYbV7gc>X0>NnI`xp;WTV=d zeR;2G*JO{LM)b{<>&vRmj=y5&k7>8xopcY}o&;j4cpQ5nHQWblBakBJ#U9_@1^27p z&)iN|GNv|u0{sh-#uSl!xvVtD@czRx%PnbATu#?1p(^`>_bo5-^&1#fE+=gavg2&D!86sBmBlWvGt`CA1OHzg?|a7T#7NJy$7yhIL}RY}jT3bJPt|$eRADa(C(?(;yR`W^}yL(2HCerB60tAc5b_j!xdLtAT{uNSgV_9*X`lNBXw7Cy!LO zLpB?J6?{4@{M{Ucwua#_s0Ni<+y-a=ii^ru17c|}M)Y7J1M~blz$c|g z@+{;*+(i|1b*cXSxdz$SU+fg2BD-=isTQCvnbS`bOFpaYJ2oQ{VR5-p)9p6lZRd?& z+=?^B?h+fEJlC>;z3Gn5jl6?c8Tpu@$PD?DZ5FjR<4M-Kp14avJx`{K$@vDlBGl#M zII%{EIRhkQ4_*$@fa(GiA$?h8oP?#_FQ8Eqvff4#mY7bsb5*hFhN=*B*tv}60@ACsZS)ir$)W$Wp_a?m^ z?W^BElsi&W4ppJuSZ1+V_m~ItjQ_!^bBUJdNCX#=6fr9BiU*l z-XU!I#m|1SO)@a5q`3^I0n8e&gLHSZ@GN&tqN60yt$-9523|t=+Yk8d^+6xzmI4q-3K(X$P*GTmrj2!CsYdfP28 z;uBbxjVSm$prmSL_vAs@=I&M*G_}cZVF*dbapz5?BMS!SwzJfl*bE|T@X4S3l#eqR zS{>T`G1v9Qng7(3bFU;txTUJk^w&6m3x6sz7M8SY|n(2e6Pt-0qTYG0%9 zD5{@mLJu9ydM@O&I=RUQ(^R6xOP{5&F(xk={}ISS^;DY$1)7VEo3Y!XSmHp%UFv@L z^GbUoMz!pSE}GKWi7gs~Zr#uMK`6H|8AC6BeGA+C`V`;942QSM+Yf7fjLucAF(kv` z!ZUQ8OmMh9EM_U3Pf#k`o$QgVE zuz{0o$noW}vRnkUq>;(g{WNVllH_@wH`;dG5mJkneX{wK2EE7 zBxS2_$2Y5=mhilMB?r&%DBoI|t*cLz`-Q2{9`QI_&>K`$cYmEKlNLy5x>~w|kiY_R zOSvYU_iBYfNzaRJD%yBfA$HngFX_*`cU!Kf7}abLEJGo_xXWe#;@JWTnX_&sxjU5P zqPn`EiJB}n8;q6E@Cf?cij$X;!ngXz5P^ixJ^G_~g2f)VVgi&Od4Aqe!0s~n+HJV= z#uiK29>{?`;}0!eUS`>%uKW!O-ybdh!mTn`9n6wP_hs5``AwUw637elDW$e*db*cp zC6glYHRGyZA=7o{ss>d`kt3=MZPj%kRTmXs)Y*)}-*W z75}$vKZL7*@Dw6^<~%h0G*Yz5Vm1T>qXS=FkcA++TbxQd`Rb~Mis`_f88HpRK~FD~ zW(*)D&Cx4Jxo~W}ujw!E3TjgCN#Gx`K~hpu3Uw52*%DcT0&n4r3#Swu@BHSiu$T#< z?qzt;aPo%r{HnIQ{ack%nv7|99zzHj=U9+-h2@LW4+b2&j~+}gz^S#pVw2h3=C zXDM9=nmzdCfmc&I)W!XG=L++gbM;HvqGuXeT=om3zq=2L*>Ne#;W`9Xp2H4SOJtEe*uXzW_O1{f&Mt?K02d zxq2fTi_s+C8%U>)Lrhi1>BzE1>Bu@|FU#WbnWe*Mlyl6wl+{$#JHK8q zwHz(Gw@XAbe<%WvYzd}>=TB7!xnvTuo`)io zKV|N)(Z#B@-bQlz^-VlWGcCL0IrD?-b-q1|x__qF@%5_sSUio^ACI!!pI%PkbTO01 z0rdZ~29Y{RSeowrBfPGZE9=1k#1JKlBThEJDjd@d_YKm_M=K|O_; zLH?Q5c8@s<2G5zkZiSmp@JoZRit4fV;B}yDb@A=@DS1!5lO1e=g)*J6+@28=N(R>n z!=%|wBT7H5&Lp;H5h8@p0+5mG^MX)$kV|!w-dV;3r61cvl9W8&z7+LxgC5G$KZsm4FJsL@iMK zk;sbS!}kRBT!GlCa?GPbKN{JnNv#?1%QH$%LPq6h`1A_vx;-%1<%4aI7?etW_JJ1o z4+gBf;{|)OZ8*i&YqzzEE&@>X>M8P1Rp=F&BPqJ?BR2Wwz3C1il6;ggEJU-#VQ4rI zQ7C7+ohYPe88y!o`OrVd`E+V7-=g6YQQlT^+uK}Uk=rc3$Zpn1y}#`D6UiPAlvrV5 zVisq4T9)?m6;x!#l4ax|&FWu;Fbo`df+Okk)B zA$5ju=wd}c!{iec@h6T_w0!mv?X{VDxE!sl)O7kM8np$P;-gvL(3)*x(2i|l-&RQMtdq9CHxt`}zWw+E zf$7ERjoqrY;yJ!kW|OIR*n|=CaZftledTWnQUbNxYT;6tmS0P(tk>#G3|o zvLC+shN_%mVr!{^m=&nU~Tde z^BUu2k)nxsF$2l*mYeOngb%0k%*g-v19)!Za5>5!!0$ZFAeedp=IgR6@M@;9mdrbv-<^0A2r@-8e zi7S_ypy7dBOM;pWj)5mBUbLI-0I#x8HO$st!jed!=fc8Pcl@T!8r~{UJ3$SCWA;;b zthM~2N(EFiLsg*yb(Ycc0MF>hj!|91x*fPTgYto#Ed*je;`4l?cdv~TvccWu;k>xI zw;%&1S$m`W&?%lI^&BLI#=G_@-IR^Jo}nt(Ow!~`Z#|rQD99AV4E|I>EE^S zspWd{TXLIMb0+XTr|{l!Gv8Kx*49(GR8CaSxOzO0HhQtf zx!1+Pl2AS|<8dW+V)Ucc(D553pQmO$$>FDS3mC*HKkrNsxt67xttr|c)0hA>h+10` zaSLmPvh?@|TREmezkLjFG$A-$fg6~tmD@B?7``+gvYPT3J$1U8sIcvb;V`$EI@OYe zrfYr8vWu!++?L@6ZL?nuS|f8bHq0-mTT@#ocAMe5OW8x+&iC&I5z@Mkk%t4=jz(FX;#y`XGeoIm9AK3yzDxh}yT)tjG*IAv5!uUk%R3P_{g4UWz2 z(e}RbD&Aic76J8uUu5`>WX&FMd2H&QbeJR$Uq%K*EHBFjwx0ZFDrKr<1TR|wlJYJn zF#tU~c5px1WB~&hfln(V1;p#9Jq0kRl3;i7D8l3oZidfL=CG9{+S1ni42F}k_WYh? zq?L(zy88suT@5~co}gPT(!RgWYAJ6fcO5~zixv%IW}O~7bCTENbLLzjU*Z{f;=w0h zk*_yjDgC*^qK}f>P=Y^=vvRRLGA9{Yi6j@#AhJ4ACT(d^+T!!K)(eL@Odex7^rprS z_h%X{uQZTh)K6qo)E+DI^SbRul)f&HOjS(Or(o69&j}l1qvI6%sjLqNE7E)FeUJ>}| zEIOgn)G65D-la`^GN;J>{W&NWPXnR^#WQklF2K<^vFT*s&l{_l@nZ9u_L!ll2RfA# z`Xklzzd>Sb#f-a{-LMGKv?~Bv1*(pHRdwY^3L3zt^yJxiM1#JZu4CD+GjkDXn zV1S+2r!k*;30mLR=j$3-!MVb#J#w*JQKpMTU)Ys-!O^TyB$BSWXVwh-*#I5bjnxr2 z5Mz}Nc{0Q9%W6Jfv*kabQTuv_#(8znp=sf(BnWyfw9zNr>iRO54+Q+gg(x|drZ=Y~ zIsF_<78S>lRd_p{3~DY_Su` zPSvcEKdClGR!-bq87bQs&B*C(`YS)naSwEXSfIksj~87R7g4MD>_+HG$ zwc-JAHYSHU5Fu2Avyt7%W`^VfJd`eG?1pjQF5&ZSzxdtpiG$Tyi>`AHph^av)jT~* z;w+4LBk~$8KW?TP!>or(#j`6^#f)iuJK}YE1BXYXj7wm_WZ6rZZ>iGX9q*kXjv_*A zjZboWX1+z&AjQv>nTL#g7cdIE!#JPwRV;Nx$8P-2(CM7Qz*GJ=5Dd24>c7=M?}vV~ zaNwO=PA}Iiy=+wHu2c*=g)19m?(O7#)b$9&z(S^$5V4ML&%!B%9Da0L&K&t2i5L9h z7-&<=G8X5b(r9fjC9v+yG_dAdPrbUh$Kx=ciN(anm?jhT=m26FM_$abFdqo*5r&&5 zS{gx(FJ(Gdbjkx2;-l!CNmgdIUc{YhOSYHU>?NDD{}97bj6$T&K%6B&%NI}{TDyPc zj~$`kATi+wq0t!XQ$dR2$w$QJK+)Q~1l-^ZUs#)?B)BcuteT_`1eg3Ru? zpk4ujfj;LWN$5<%i7Ko@d9F{=boa+ja??}3c(~cdSh-itjmy5CSW%1Ll|{rz zG*4opg|F6CfP$ziZO)2pm1VJjm4pFF{7;Qs)|}}z z()`&(Tycu1;<}lz3=P((aq9Z;+D|06&Iu^2fxfvf5B04cploaVrHX`=MjhJ%d$GZF z{SibivO^J?8okF#~1&#@d! zHpo`QVaP0WF55}^Y^so6j~0@@joaoY*UcFed652P0aIa`szikTLfdMTC8d( zn{x-MO!oXVrHMz5zYU%`6$3*nZs6zx(@w*i3ZaLcgZTjWt_6rvuumoC%GQJk6qS=sK3% z)`(6?YWnCQFLCm6DEnk0%@L}@)bv%$QI`e^e4QmAssHb@rB3C!MyLvre!KovdcQZF6h*Mse^tYmn;hY~8giaPIN;nrhQ@o7%4 zxh**n_V@*QmU@}YN4auyAOl|xQ#MKT#B+Bnwanp6 zVY!F_8Pe(>O-lSpxGQ~#oAoU_5D{qcF~G4{{K>S~^h`DAW`7^!-~r)SGWBUrY|_p$ zYY-7z6jwS*wRfF6g&(y4C3a_d!Nq3bs7+-)I_8@m$8)Qwlo=wdQIyWl#=nK}&qjt5N`vzJSFc$cW|}fK4NxsNkTU!{mRZ zNw(VimN86Lq^{a5u;6D{nP=5m>}#sQHZ5PUXnzqYF-=!gFrsAP#7 z+-1|h)1{vrGs)>^035C~upjktXP=4cQ7(xi`C`zAK6Yl$+PA-{RlflR*gUI_kWLe= zs_n0Q>YaY*ZeQ_^1L4FKDH|@d3)|EL$0a2c$~@(`p?QwFvtb)s7lt^2AYOT7kOb5b zk_&+1S3A%gTL62ki?+gzTTDP6@NEh9^c9gKx&c8i5~U=Pk^qOju*X6m-EszyD7?6* z;&C$Gm0Aacpl?ybfS@)e2mK!jRj_M$s)(rYgda{m*&@%ApyI|6c%GI-qZlSd631Ad zP1MDRb>(458aE+#eV9Ns)~5neZilIHohZ$w9U8AYA9-zzbAf70s zu*Wl+-H0d*7+gvmi*k$j#h^DLPB`<~kd|0+)We5WFNY#(KaolmekV{$)`qK+OPkdYAxIedh(OGc#sq!XB9 z1lMQQuG)cFHQkHKOBNN0Eh?UA@uKx8x1P>6B}IJB{XZWSf=MG4rn&m*WK2?9mIJ^* zd@ShY=@*Spa!bW|?ONzDZ7%eMkx zn`rF~Msfd3LhCY14j0Xy<}+OC1tuC7l-;S0}w5CVqp zE&0uF#`A{>iMdNB3e}mkM*wL!nR$=9l0ko^rT=|yfCT87Y4 zbNi?PSr^3fkCS~a8vq{k^mlwpS08QT7JNA@mBQc-xZ~~?iA}Hqay{onZpZ+Awf!Uh`LK2Buy_HYY$-!o(;ObW#OL;C&_{^}Eq5gYu7 zL}gGZTY4Mbr1f#S`}#?$MpmE1H?SSM7#y>QLY4hrxnJ4 z-FO%A4ex56ks_uQBjz^ZJs9`Y;g4~_gJ(g3X{3{HddZ*QuMoe!M!W|FH|P3VtZg*# z5uU$T``|{PHIM)%&3<9R5Lmh*r8s1Q?y7Izyh*OpXL|PU@<8O;ABNJ(614$^qIDZ8 zmg}twlh%HrosidlRwVNv7vY@$Yfvg5hSEsmwEidK|pT>B8SL0dcFEGTDsQAz}r|V=X6`bBE-~Dsp^>sp2p;V$U zN()-~eLCQMz1YmJARm1X@4JD_gjJ+L<-Kl0!Xoyki;#Q^K_6ku25ek+0``Blmp`5I zmKKm77>H)bVz0lE0ZT*^ zT>Xyrhs&S2V_XD=yy$qbm-G7nnBVZh<;?fX{|w`;AOv`(`H6oz+=vdw2;BluUlAj$ zBA_~%EI!*A!%^&h@UqHn&#ZJ%k{0tgwh`=Era%g;e%N|ENTd|=3%_f(H^w#Psk6nZm-ilb`^Ej_YT->bmI&-mpNn|5R^%E7q zJ{Zo9wnB>bUL`NzmAZGKri_3*+|r)yj^}p(l&W4*O+TMROp9&OD~s*$xGvN8_>~?< ztTV`8RW*Mp!T{wh3>6Fic!1V150eLtYzFyM0kM~I-`XZe-uT{EPX@d`1`*+O;c4gm zF*UGwHEu**$op&|K{a!_c2ZzjKMg#dHfKjVnHEfGz8-B19?+Ez%7tJlG2r<40OUW? zyvNZ;qU(;voV^3qhjbGRF}hgw^k6A%O>t<$iwt3mm_90C;nAbv{Fy|@Kw?9r-nGZ# zG2&Vb1u#jz++hy)?P0uFcz;J~gx}~l4WrK2Fixw>)Jc$P??14ofcZecuX=+cwx9ep-7n-{|Y7;Me1i8wqHhQs>7;EsmckIMmB+W;O-5 z!6B+@U6VmIGf6d1S?e%{Dv9nBDX)4<>`_Y`*JsFOE9xm4j?{ExqvilB3BoUf{{5g$ zG<93y2RN5}RG%Ks!~0K4q(IPSa;sGv(JQ`~Sj(zw;Tdv-`qn@}#nSd%B_59^kAVnDARdLPgF!({=1#Kjkck zOFuv(hv<5hRMg(*ZEHf+k44|0?p{>Pyset?8~^G8TxK>zs1D(@PeRD?yGG!Q;sE4s z&KiXPGEX^41o_P4*JWZn2O^1gJG#^p-Z^70LdQBm%gde~K}UB>%FlP#M4CW@_468H zC!q*v1TbR&aRVXOuC0<|heK$cH%8DS*lE32XndSt;_O56#?vg!zioNIVt;mMGiHNs zOUdC&0t%N!GHVq}A*ee{r_3W;7$^hkig?1gBJ?mViD+0JN@jf`HpRuze0o-Q)(7@dB z$NaeKVBb?L-CB1&BD>40!8*wv-6%VA+v0-XZX9EVNvEcJGUE}_V&qT<_VrV@U?p;e zAyMvtdV*n{A`x<1?`}M@$h1zQ`rk#dZSD&|h#eya5DSNSH>Yo_x!?0tg?R1NkpecL z9t0=hdWkym69e@dwY~cu5$XBd=+Wd1zqr8~hBw_mudQC7M=D&qwh5GB*m|fp%VjgR!Za$*xv9KJg#CAPiVAwt903_@pwqNOp}!q zci&=X(#%i{7IIL8I2Mb{Cis}S>lZnsUG(9 zCMp`5G|2dw016rRoFd?%r75S$ggw+?{Xi*PZ*diNNco9U7@Dpp;k2NlmiQP7P`Oj^ zLnX_}JEQ#0?>+0a{zOybp87 zQr-6Gt+#uQzC)plAy2~ZFe<;T&NiCye8~4s^3wApRX?7O8;N01f%PGs!l|C^R6VV! zOk~s>{Y7Z-+@s|tkckdoKKl?(PYZwF&SQ$!{V0VnQAW$B0D}4gRC&xD0fqoH0frTG81HA#($C2 zg7OE_Uex#o4adgEMV+HrVnsP^{fZ4lIE`TCA+a-NCEsyDk-MXm3zG4>4hQXOZvFdD zcmr#^oF^!Q;H_;c^yD>eUU;~eq1X!@{^LtvDkAc~re}u@Hejr+2w9Y$$wV>VZw{JT ztHE;ag&saFU)zs?IEoz>5{oDIIi~77TJ|jo!8#_9xykVfR7&*S#35o06V}MXCw~T~ zkv5*dQ_Zz_o~4sf?iISS@w)u}bueRA5$18U$;F|}847`*xe&h_$Z9T(fwH7s$8*~G z`gKeI&VG9e7))zMo$}ny=*Qm$P?#2~NjorK-;sj8@I}%{$4TU4=xtZ_uUgeO8LYM|ETz4h$FjR`iD zbor7sr#J}%e>EZEM@sny4XkxD-W>q?=F*Lzgm~14I?Km%>(yT_k`nJ+94VJtd=7=; z;uE=0o}UHB+bjh-rsYOFrc>$TRB35MzM##EvxYsq(xvtD$0h8VoMS{<9(kU!09d3^ zIV1VJIm6vFl9ngy9Q_$bS0zZi!)s@wGe4ypE>WxptJmE1Ky~M>)GnO7g)-ZM_md{V zHRX9RG5(zFBTMDR>VL2)_K9fF&1ek4aW}`)eM?JoZygJDO7gmee2el#kkpp}*g9); zDiAh~=(ZZp{R|Ms)%KLWNs}t$^%k)T6r_-l8n$l|%`R(dIf*PwUXH93y4f<1t zOZJ|lOh+!_O+ zOj@;URofe!Ii-i>ftP=R`P|`{)J-KRVQBS zH$7XE+mng*L#PgRq(c4m{c+Eb_{24*f8LW);bxi@9a2VZ8qgmqowC_k{p`ow;r%Lx{;*EOlHJ@8Kn z9)w6oz;6a04MZ&mm93T?Z7oGBkDf?V>^Q<21CRp%(IP&*c@f-3r^>0SZY~J9)*O;s zZ!5mXT}g9jmQkLTBme4Upi^!e*`|^{?q2N->3Jt|-q#(=_tT@H{Nw%yynwOQ?T2~2 zPa#gBmyf-ZkG#a!xDHsIr85hF9*`#h#OZc3hzmC`4TE1i(^cTa`SE06axq_Rpg!NA zmI1>oCPuh(&@kf?NB0PFs&<67Zh7TVK9VZd$TIQsbr|uM!*|DIK2pV7Lpb6F1eh}o zi(VVq=Y@3}T__c8bdkyE)5}t9O7^W<92G??h%BeNWNd=&XuwI-I6^{$9exHNpeGE!_%w_`AhqBN$k1CTbbtYwDST z;LMRnLhBC)GR`+A^Km%LI*Su$po$QIi^1@w@-mR#vOJQ(;4il6UGm$I;^DH_*aXFg z`0uV=`w$?jm+hmllr{hX)|tY4Tw?sMtNX~GgAbXE(=B_0DK{GXcxM<9=78RpX7Q_o zAOVnV+=E|6YFQn%;}YPOZZr}r`i~yy9L5uQ-MH7FolV$blaATJHWakLewAQ<6*p;R zYK@5bc!@m%9 zlL==Fr7Az3pI3o=RS(53!F^Gyrhc6|q_xAw0J~iA$~SR*HZhD=ruD8EhHUyR>XI_W z9I*`6;tn~szZ4WU4F2hZ@5_&0n2wGZ$hhr!43}|2zh7Ab*kp1{Zo}oG4iwkc;#{L3 z*-Qdpy`=~$Xvh9byU=?(Lf-ba;a%;Rm z*1#U^suH&1Q&MJj>tuB($MoARag|_#F3&dP`W=*ZO}^b3g2zw&a-a zZxW*jsk4IBgg~r1Mg~|aG;p74XSqj8FWO$dsh@lI;^&OmuvB~>VS zg99=*+MJkD9hEx-^UENG;ID9M^Rp|h4EAvp z{86OO{~c7Rny>yp$ouQJDA)E497RM_3{*-$LJ>&`=^PXVX{Eb6MLHjlg`fyy&@DC6 z-EDw$cMK^pbPvo7-+OSq@B7*N57>Jj$Np`}Lg$(1uIoOpGvpe0lb#t}r@q5}BXVt#Iq4_|0eG|0)=nKXN5juBI`_#+E-Ek+SD-`6{iG~`z0BgPi5q}Cm*-^-qM z$@3Mq4aSr&WsI1O;u{h_1p?zrHU|r%`lUplkVEhw!rfjnZI1-I`-{FH2Yg|X;;0#jaT)V)5zaX**jqc5|d$rG|1DL zFMQ1IfS*ZG@Xx@54md)!bx$SuDz4A3Ba-Xk{_0?Hh{Jdt^Wdy^Cn*xLBQEsAOLvOR z0E|5@$sb++3Eu#3MfnCNR{n9s%?@_M^2v09( zv`4AQyRYBxqU!!cOx7a*71Hc^{<&-#alO%PF=s7KT;orH>aI0Kcg9LH4K>S6vIlUTp;cjfya=mTR&Q{yAc7%ONn)_ z3~sSBaIh&)U4hgO6RyB#f*YtF-t{%!o?()mCbfq(WgjxS4$Ak{$6g$qFJRApym6 zT%L_a?my$LZqFPBNN>Fkh(;a!<~%ihbO30(y~J4PDsD{;z|Yb}ww7^gPj%U2KaHGw z5cUbN<2jJE(DYy>fKSQ{TE-KB{k%2pzRBr#}u0&@IXApycmyWK#N%a=qE7g3lPWp}a!N;^{Du|iC!M(6qG z``N%b`=#q4>Nt_b>g*Cs5NLf%yVx{1Kf4=ZN@vn@Q00T%0orTPohYJ;=(r)5Jl5p| z&pss`3}5pSzJ3l#dDKyvz|f%w&0#30Ymb~xyi08FqG!$!rk;U>^ z71Wzv^X_}Bepi8ukEX`6*c7i=piQLHB>0Gn2ZMzxBlTgA-z+S z`#OYoy;iwCnaS#Uf}Hg!&ZqUCJOvnPMo5LZ10tn#ino6rqiCcy&Oa^tHYBfgc0Qkh zb%7VZZQ<2Jj0KnNM~fc2TK)qtl!S9!@6uhp#xDv0fCXK$LZ&KcvC2C1s~JprsgH5D zG>s|-D24? zX3-@;`tC~<_u2`ir{BPq2g!zYTQPis(sY9Pc~(-}?vzk?GY-QY{V-P+$n`?3NV`;|`vzJVrE1ke500bkho7-=@%F+i~8hek?FNRQ&n-@u^JB||B z%vxSdNEiGDT9a!8{hZyO*diov`{afAXQe-w*xy^>TPQ+1_#W?3^D(X`PS(2=s;vH$ z%ZmH(LF`Ox0$uv~FfjeWmt;y=q>6e$ci%;~jp(H{&X?IKW;a9g0xYkN@2|gV+x0sA zt=a4hzGc&SDn^3kK;)Zc2PpthE`-w;DkM~JPrNts{aT)6CsEV=diHzpa?WbQ(5`-N zbbsL~0eTyevY4@w(I>JNM)B|-O6i~D?=Z@av}e$$3)DD}tMyHAnYCPK+E?$1`vwt; zlf;dj<8P(8WdKyzLx5cWnbqi@^oZI%avs`6;+N+DxKJLTOnOGloQrP)@bIrsm#y0R zGt;=8p2@Cq^Qu`5!d8tMug43uVQ9W04sPkf@fuZfLX<0ItRm7c&hEU5t?}lam zd8q>!wDN$41})T+N=8!iS&hY@?uxGF>y2=!PTj7i?1~*M*Ymgxw$Sgv44=EfW%%9P zulo+;W!>mmZv84&id-UCclq`Iltco7ig6p^*I|}260+sOtIuOOHPmzGJn@T5o-j~n z;Ca-&uZZ&zs+o82nx~)ON2Qh%n#+T<5NP@Dj;E2aD37nfN-cXLJ`tpTH3GiiY=xC$ z-x_Z}8a?Q$vP(S}vzGbeveYedK5%km&bF+;Hv3AkYW`3^EA%5 z`D2UMKxT6zJgGw1YB00TEgVE9 z>2UA_9F8}8k$R;|o#kB)0B`r*nyxiWz>O^hEw-jWpDdsbt>2jnG50lcPN*{S{n(k5 z9#}qP@H=W)=!xm!@;PSWVT4~J5cOS{cD zs5#G;0)EjP5;7}9m-$JKACe8tEeF}|jOVKIXl;DcY~8*@L{?>y)hKazcwMC4D-5vW zghwGkI0MM7DnsweZ7Z)c>tx?5@!2$l*Or_Wzw3m1p)ChuFZXkCPjPjmBAHR_-v({485 zyF!pZjn;2&rB03}bKF<_*6gfd{KJSgNiBM$P)n$@BhRQX8EltVMWJ0P&d5J1;`D!b zKaI@-6M_EPo_gc^UG0;p{#c$|HtnL+<^8o$uR`vYfpZ9zlxV&`l^M|?BoupPdKtrZ z2kl{0++${8R2+YbBT#^xyA%2Yuk0r{*LbtHc?Qq$1yRof*9mgL#XFV9c2C+`@~=Q@WtJ>k-XvshyLfE zPh*&|z!&JQ$2{I{WJZl}+b^e_{~++nLT8+st;9jBLu-*!!!cp53GZn-V>#tegl&#~*uI&-2V_5up zuqNPHX5Kh}^cy*0eeJZq4!k6$^4!iu?4g(Lh0pw$?8#iO zN$y9q{aFf2;)Z{POwtR|DvCdz{8A9BAWyh!6(? z%bG>2{Sd6V@1m^5ODKfvwQ+YHPZ0DuVWs|kg&mWUl~Pm|-vfod3WvmISTp<5dHmZ> zCp0J6>7zYlOqg09;sSEXUwA|XTvz=F zhm4u%sIy>hWKEp$kMl#{I)~yFz^lMyoK!wLa{U`&z%_$opxQp-O0HDeB66oto zH((F+s4W_n@C}U~@mD1Of47oc6(q9U(LpV3=5;ixV+Q&$ZAS5uEG|A z+Y1c%1|#k(-cZRQZ(~zpr~F96{90k|>E-YL5~!V@NFhv9#Gr=yXh2t-U=#lCVzR$F z{Ard+rk#fDXPTd2oMOsz*Jx7Mu{I8ERhjYp(-XXz2Njq8p0?i`qHX3W=&=xBU42~Y zYI>NImbWR3y}#`HvCQ{?nie9@pulS;C27T^gE_{{BO zIA-_gCT6dLJ8hti>Y?h-u~wJ8<(DfXP^v?zrAUn**p4m&WIwxp`o_@qP5kBpD7@TT zb79==GtEI3pagtm-OK0&gg*CNyMUW@G{C63l;HsHKfDC`Z{3%KiMZm}%ny}P*nAMm zF<~d9!kRvQD%Spoi`i-&<|j7y#@M5Udaky?XDL2}gSjw3cnbI5TIopL=4DGwV)BD| z8cD7mzRNQ#)wCVTW{Fd@tpk#=-~KV&6RozxA2+59!`rgtUhgvF6vGe}d$GkO79E-= zJA(mhVxk>i!fU?^RrQ>G+ZW}2G83E<|aft zgK8?lxoyB9Hwp-YS|y442-1!)=SB4$p|yVkU?h`OULcfQ;9Sb@bOF0I$pInt zd>N&Q%{{{uk4cTz{|F1p0uHEbkSaqt8jB!0CK?b-flSxnyIU9Ft9)@^hsMVw5k?R; z%6R-$cJUYKnU+ii!CE*mHc`K_xp*8^ON=(pRe!uA;Be1tb@=l?e>OSrGSDqN-wQL# zg$%J9OpP;rApEDE^rXGQVZxy`)I9Iv8UOJTVjC6E# zG50LmjZdZo=nNlIy@BkbQ_Nc76O8oNX!@@wd2Y|O;dKGrjX2#Fl~`-BR{qfE3e!ze zUI;Djgx9*I7pHjQ-U1P%MWNW}20FXgA$siwTNP{3U5ZwNwDIeX)LMsb-pI-~e*RiB2TE0p^ z;=v@o_6^IM0v%*W3I!E6ocLEI3VW#nUa#5VaO!J^lZ%`6cE>3j7WM}(lp-}_IP^3d zpcBj3J%*#~JK~-%z!YNP&@3oB--*YP=m-#4eGq(LO`L#zkgHi3(T^6seYUy*-oR>5 zQwF+@Etc?&@$y_w-mw7UtG5}^yGtHEHmwnKUk;~zRAeIV*aSiR=iLDBX=own&P+E4-C4vur}r**el0(=VjW1SAaBV z(d9A&R*tTqn{;x?Vjhlh{5GE>1xQuIqe?ATI{`C5L4@zMUQjUWrK5zVETx3CvZ`Pu znR5{5kE~N>WG6Iw`%fl9G5Yyx^ksfE-^G|m7fK{QHWQf$ex|#}nM$ny*!Ez)MtT{a zDt~yDE&}EZeP{sA65qA{Vj`MJ7myh!#H+EksXrhK!Z1zb97@b=WvoYR=FM{GidV!% ze>nlX`My?xesQ)=*@eK{H+wnFeUvDRY=?4w{YDEzmkEoNjKS)3m(mVs>s-pt6MS48 zFL4}-#tn8#0Z2R=B(!(>J6SUp;ylV+S5$TCJd1&|nc5@s-+)UPdtEA>@rGd_Ow{9x z^e)xgYM<9+_fsmJrNw?}iIP2~k4oziJw-1uoOtM-I;!ETM^Gb-cYaGI6vKA?pel@k zu1Ym{j+p8s?9ZmUe^^F6rs|q@0d8k2lR(}8>LfFuI_xi4SJ-K2GcE%db)T?s7r_GS zzw~}F$(_F^FhJG#?MHhf>W2IDL`ChA<32w&x*JAJWD~#SI8`&Y2HmaP#Vep+U9y&f zBM4IexjZKqbx%a87g9OrRrZui?sF)VCOYYf_XEKwNikg2%vW6u)US}7yv)FqmWXKXeN(eE6rxy$t{0hugX#yY<~l>&x%=d#bQIe+nbBrr94>bg*9StM$cW!}4~fSt}$&;E?}AN7Tfn~@i| zZU!#j_#MSo9@!qFBe)^4m{(v8&Jq2)jr^Q3D*c)F_+rW4{^gC}PinlXU#dx#$W;`O zl6({j4eQN8dS_Z(5O`L)S4n`In($DU@BF7{zhoV)IqfaGS2dWOsb|1UokF6{u8GiLqO1);f$tVhx_0Cj13#5TmkO=njKgVKaxH^>gs(GP>aTkri9cl8AP1!Hf`kR7QtId9n`M3=N;RBwK&8h%zA!0mXs2Un>F!z>`0a3I zKoUr=&B&Zc4W=>_ju4%zbbf}+or~fe$HYvm-$0oUe9k2ASE97NHDIL(F9sn(TcI~o ze)hs)-Ct|m4LFoVR(|$B8+eU(SpWL~yN`_zyJ~+L>J@OgjQSS2U;5ww!v9XMHVCH0 z4L%P%OWM6Os0bdQD{;`4kuc;y^!~VcT)+nQ1P07Kjo=_Cu%VyN$ZUToeF-Gj(l?Gv zP#QV-Zp)*zpX0yk-;b2SUgDh0lf$n4HvG>c{QE3{pKpRV`bROk(wA?1V*!J(Ph=zX zi>YY-`h=|a2RbpXhU4Shqk{w|vq|9kn%ZMp-1(^EDTB@ZzFmZsd0})Dv4;pq>wBn{C~j*#3Y>B;4`lb zBS8SeT`VDe%nX*0`0^k=TR7>+Uh!}EhH*2Dt9TJCHGk`9yOkjwc%lQIURcWzPySutTmIcjsw4X0?&(E= z(WexI;qr`r#2F_k&nR^=UY0AET^f993`mK*K*+mA{etYF8<>1A#0kkBD2C!!QoN9p zp*YK;yyB%!mNTR2g|;tdeb1flIPzXT=9m;8=r;m{+(X}RA7F&a0vXL2}?qr z1ZGEC?S19Y|4pGTFdK#@<(1C<7RfUfFkAjV^*j)Hl2mD$7!%9W0I75eyrDj5hLDC= z_|eGxZ%=4iBLueF=A6YpZ}&uo4S_u*C%RSs?{NJ8{yzYLnfKqX zKo~AF156acKn2d7rJOWwJ)Uuw$d-u*A5*qg+GAV#u7-#vya}+*cX|SVmRcxY(B@L$ z_@%3~f}EgY*GW(sWbU;ldY~Bl5h7Mh6fW*7lat0TMiRmx;%o1L&3+Aed`>u#CD~c} z!s(+466ngM$j~Nm*Ea(w2y?$~*ZmhSXC|B)yI|+REYNfg_8^jfIs;bd2kwTWw5Pfg z!U=2y-<%D#^6!Bav!a09YuyExiT4|mi8lh#cR#+F>8CO#2~;JIePkhzqvipwAgcBa zf@Gn1>+VA&r1C~ej)YwDylZmw!s3WEp)?UqQIinTmkfqGGCS`WsK&x`NXYg|WZ#UP zNM3{Sf)svAXO8=>b2$fsO$mLKbvq?c)u~~#ga!v9Vp88i*UJCRZu$qG!?N{j!2h;}3zumc{{Q}AptCnK_?sICHh5aED^!ZRRb00{ zWSP&3vHxxY*9@dmo=7*o>Ba{{|M#cBQ5~P=HCM;A|6219hh-?<&Pw=I&^XQn7J@GK z3Fm%NhH?#^$27Cx*qYV0)akj*?yvthe&_}x1kQtpL^Li&T_Bx^7Wm|IK2T27zVc}< zM-=3DgLoKEML@HwQtll4w>6P)TA#_N;4 zk2mpQ++8RPu1sy-D#6Ln0t<){{MTVS-+ZyWvJ4br_LT;>;}t6?xl4>w60?aW#wbPG(pc=&(6H+UH1xsfWbB zK&G?5&Y!*trG!bxO^~brDzh|uFEkY-zPsj$4G&zTimEqs?R#dn&tn-rW zuPA6Rx=Jm|U$Ew*arMrT%m+FlE~FngjZMonw8H%kra2C#tTwItf8K6CcCUo}zdZOi z2&|}mC*4JQEo~FDCj=r+O^>#2@^YbG0OS33F{uZD;0BjGNMX(R$A6=GDfb#aWgrkO zIS^Rux}oom0cp7+__*G@(hL_@VCZLGP36K}bi8U{8kXWf&tR3_?7mSw0CXJD|C=W? zaZL-^19RN)2C@l{%^SI#T4mdAL#gR@pp)hWE zv)l8q{;(Z{qI)2t>I({~!ZQmW>>DKcZFu!TO+iupzPMjheTS>CXOHUHXo)IYR&#qa zH#=UYv+L*TaMZ~{4|+$kZ+nv1UD%Tu^9%55+3n*W*^pw7p!uqq zv@o-#Aktq-Li$FVtp%^7oc`9~fy694N8LWAaRMWui$dCk%}KRn55TerY{6HGl7e&|)ziLtbL=br4?$)Fj(|pcF z!yX1&cip?*2g~FWZgva`WwynP%B{R0i{#^#j5RK{05fH3-$q(7K}Yni!>)cl@7~Y{ z2AJe^k+m&WqUF6&*Ds?st#oEdZo?6oF}mzB5g2}Y@1xlx=mfSAR#?hbeqDI^W7V!x zrPgvOJ83=ezxl;0+tq1rpe;EYa(TNiM0zg25*ZOWv;u>}=r_&%Y_trWb@D1VYJzRu zy#3ib^j%-3DBPPuW!b|3fZ>`q;I^)vv3QS{|D?Ja_cmc}XvDCC&(-Mln1yq**V6Ec zry%}ao`VQ889yGw`>pI&Ve;7L;pK{$gIZ5?-4!z#O8z=ecZNRak>oz-nB*~(294d9 za{Q%_h$a;5I%vs1LtW)uBFW zp+(Pa(0UZwm;b$&f+5ts;v1(l+goS^H7+9?p*nDr-3I67!pU>$t3G?nH*42Q6xP6w zH1>R%*{I9kY0;3-ge6 zQu7i{q-^AF--$=7=I^31kYv(wj4Gtkb1Z&#$9b^NixDlNQ;|Ax`dTm3h={9xuz$!> zM>+7RA6GTMYD=n`vV;UZ&6X6Weph*3+rAvNe|O_d8sL08upuDR3!=m*2mu!KM zaR--j>@7#Z3>%~I6hVE95p_=`Yz6QTqOsG=>S`()~20QgbNBKI(n?mg2Qu55a~OCCOD){BY6*UC@7WFcxEtR?mb>(#Tq*mx);# zMeFrZmVC2_Y*Fu~~f7K)N_ZwI1zsy!`Y=S5@R7Ha*RTY|28zBr|nuV)IC^ zLDYgz^y{HB#T%&IsnSAlf4uo3eJnSPn4|9p>Rl!Dkbr;v5`E+Q+``p_5e6Uxaz?^7 z3%4sYG!)~i8^L7r-50S+(YbqsfQppbbzd#q456B~qz;{a=(7$q&ER=h-2%xur$E93 zNEcpChev=l#fguDJEY5$%urAEI~Wt*{z1!te0j%(-4f(z_m}i)^5Z%Qd=}6r#)|7ezyz}*$Yf0pVSFizsQTf zpyW{0d|dp)eL|x=tYneRr+;2WoEl^S>GmMw^tf<9iiP-?tbiuWjoT95u4@$&j@zct0eTYLneib|$%`!EqwEyy}YFdNv4a7?2VfRwyBy# zaU509gD_QzGp?AhOc2KI!8&y>3}Nc+GkZONJDdFh)@sdh#ZB9E*dxDS2Po^qdhB66c}4cB$OXs7tvLlR zqqdVx(g@}f|A_LD4H{Ytrhz3$@JpA(Ax^@{N%YT4SxiNabj-s|zscgU7=OB57su!e zW?c?rd97JqjoR@fYsa)s?Fd&r^H0zIxO?a{_7HFHeSx=rQIVjd&1>|JI$7yQLw;r; z+~H-^e%@a=E%W}z1}%9VKhUnYp;X#XMvqcG2v<0;?WuHC~n=$_K~1Ka3l+!iqn z+by*7;KmfelI(d8q-Un|?)9tb_@fu+w7wpfb?6q5O#|XuR9u?IN`&x#1Dz5Pxw&eMC#G zq37-eN;=UGs`rulaA zREkp4mUI+CY^An%`!Up5yW2XKn;)-qv%-b)>L4Gbk;Ni=W$~M{)2|bcoK#08bb#!R ziEs8DLP&2PFxsaLnAZ&rAG;7f(dSsB?&=iv0y~R)UFn7m_}c(ZaO&w$mTzRZuBZTQ zUo@dfKjz@HawvK%6tQ2DngO3L&m&u0Tx(eb!C`ICpZZgN$2#12$;;DVGmz|9x?MZn zYQ^M>kU`lS?&U+jH~8b(Kz2ZLgoj(CD60dQJXIj==t1Y*w02odKyxk$MdW7I@n0=^Dr8U z73XFH?q8>RPD9Ia7tT9?LT{{0B5Q`NnBi_0Gmi?9roMCVDAee;JCia26219HZQ?AV z*~B!Fxu=mAM_s!f5jyR51Z|d*@&~i=tuxR*pC*m{>-GI@{gYeU4(83Aw98odNO^3 zdm4|#p<3O8#UTtA-J>PwNwz(Yu8D!4B*MSsi4C&BU^dh0tXOzaLWoH5_ol~`PJ5#k zslUeP+GdM@jNruqS7UG@nu!BFo--(K7vlo|RGHk=(q>Kgm9FH@5KEtsyxV%TP~5s} zoZ3^m*=lKC3aM+fz>h$sxEhZs_Kl7zo-dF{pSnKrEDW+|Z2Y^xR;2-k z&%2q321mipO0{ZM!p>sjX%aO-*^0_z7B(`a_O&m-SbQw7V!}P4WR=a6MJXw&J*!ml zR6{fSJr${CoO}NPsCeHuBK@p5D@INE>~OFDsF7@m>?XRTkKDc31+HlK>bM*BO2m&? zAi6^k1RB00Cg#kazpB;KMN^$3~_IVNB6ZZigWpT*IACA$qI_e~(NgMLV$OyIBv1@h1EKi4*tf zZ@x&UeOF9%mB5F!w=A)rECbBF*TX5JAO)~ z>v@xJ1{wIh_c~X&G!4CQaBUT=Z1ySpHCBbV%q3ZLE6mEWO*!tXWj*EY{87YT$ost< z<2BCB384hHdkJn|q3FlOn8PP7qCG&5-vYyJ!|P6$yCJd759x48QNdg@!A21}7yo>x zXeEVllcl7cR=CcM!`AaS3h7d6_6BPHs&Td3*yE<>@R*)j9!)fK8e>JQ>y`U3O|E*g zHfJg@>J0Bly>(8So#S!bNEk^ok5j|GX0UXo*OXdTN^pWe#~VwW(CJYN%;H?U8%z?55 zS2(Yz>4nTZSmU@uWz`nOWw-N5umM87>paqvNtI;r3b(LR#Y~F2Q$1%w(>s>EZlP*s zT7ek-9Cn1&z^f=yoZNo@$roi|Nu;?qr457ym6pb+_yx+$#ccKHx1txG8MwG1#f#X= zogF(S6DKC=JKX<%F)l(7E4mw&eucY^M^#G2L4l^tt(^_=699+vpB1>W*Z-`Nfn3`Q zqWNX6iB+dqv zC;kaG6xl7VOwxp2`0~kRRBtF39Kx>bs4rvIKdzb8+OK|4SG8Qx>G%N`zqMI+;#k(+ zU|!2p+seU!QTuiRm39gB1byj0=_t*fM*#3#TVs9q+spL!ED5~|eacK*p`4|tuv7kk z^#Ob1sXDH<;?sbOyFW4*vo#b4cnjOs&j8W)`l%`@l4uWAM3TF1=0Ho&%AOi!%N4FF z#zM{Iu=oMNYYu)LoW|9tyBAx!v72EA^cxIz4AUIC77#UCF2?|i%DbCz980qJ&S;fY zY5rWJRSJVqy~v55q1#DO#91jo4!WKc_y5fAwoF+nqXg_nw{DBM>yLf9a!wMJU+I`( zz5E1}w4F1_F-CrwMXVq^3bE3t*(PI9?l6$`y+)*7=NM7;VI?-0F-@nE4c6m@c2f~Y zn?tw*0FP3#rN6Xb?%MR8uC{9B65G@_DVu1d$9Wh^gg>#1QIgQq7;}g**pd3i|4_SK zMqNiPP=TY|L2oQ)x%aD|j`0AC&OydbX7526r_!N@jvUio|8ha~Ll+;%zc+FGCRR;> zPw3s*>tI98NS>_mRo1>sj}QfAaz85YB0cMsvLsZ}*hhju8MfYxwkO3a@f^Q`L+mMf z3>zT*NxR(9I+T3h{ga8WHIGaOtB%x81B<&>4qKeKyjXF#wVB(8KQdMYi}A0j^m2wx z)3~MICE!Bv&aR-ID?t#0<{a3uLnCp)kDr!0sJFf0e_@TvhhDbD$3bDD=7D{|KtR3q zy+apPSbV&y;rQh^Too;T1cBFZfM86E{OWCft#suBjYLh( zRM+96k!ADq#g0}6(lCArJq`G{<<(nU;*$4aRSH*&2KtiqC-#PqEq97Sa#pJT)e~wZxw{<1w=P06!`VZ zG|H8d_ohZ~Pi__*jU=D6_~|(@I2pS5eP9rQ9x-V8m}-J-)BvfVo8SCLErM)heu(xt zP6@J$%|<$UcRciuZd74QQ(Gxs$wIr#rYMyd$EicAO}&7s@NPIirVH7)-I9QIG4aD? zy*x(tWk>UC2y`<&0VHi2gw!Vw ztH2?xut4Rr!;Ofa61taj8V@B4jhws0c_T1eN7jbqqtp08`GwaQQ`mA^7=K3=RWkjT zQrTm_mj={FecztLcaQ6DH;zN5`@()HO6gvu)d_}(nt5&60I7L0z)-J;JnR!+kFW5~ zG>6O$X<6Zvr~N7o(gBjYKRiSEkdkdeOXGY&JG=UBTFqlQ4s$GBwqje!6L`%< zFli)(C;F}?gRbKx{J*s0<8dfnvz?yJ4z6`Fj{Gv!V62z)-@Ay?OuoHL*j`e54}{zG zB`Ku-E=oP3w4zmP_MAanQwQ&2-f9YgSj9O*DbAH)W*cJc3#>N3?L8 zN%EagVa)>wDjTyO7{#RRnijJY|I#Wa#b4p1Qun}a^Dw}!VU<1i=j)D#R7QUL8qo#l ze0+_&!x^P>KA`{35}lZ9=vM~lPGQ}F?b^qwJDF^GBx<1=(YL&nyT)DMM!(d0#L0Rj zPxQqPQd)Oq+eHTf1UPCPbj@^t^R{)z!-{Vo>minPK02Xr9tGyEUEPGw6?C7biZVWr z^CL?1QHwhSsqm0%32H;~BM90yb<55j<|oHHi}-@gFoZrDS!C#rj`~E;7S>N=mzS)B z7?$wIL3%?~X5adQdQ40-mIOn(^3Gsp!hJ5DHAK%?cT(=@=Wh=#DxZMnIPwGT9+Xv) zb6>$&@L4RVLIaO)#0vV!OrCVkXjkvg_1V?N#qq3~H6A<$lY(;8Ib?mFyAnwsL|mT& z3TrH5E4`n3K5ddr6JqUmVZ(v<2YRf=bR!?2RQSRMvPSGh^K}`@CW(V3gMD9COTz1r ziwMr&l{j=o)owv;hl9rU;s=PD4B0m-`#RPz^b!@z{{LnH05z>!<`cY zMMLWiE7Nbg@Ay;ce7EPI`CqU^iit^gm-C`_2zmTGy7C0j(R$y!=!qC}NDrSwj@7NP z*@{MyUyT&i?;m`L$}4qm$iu+cfN2~-=1p5R0_)!&zkZUrpV?o3(#iI{eI#W7#s7J2 zJh$-uNoizwC9;oubM%Ne!I8u5O=c?{cH={V!N_}s6McOXa_#sZlHoiF37-#OXaqM- z^@g%ihsT(bG2|Bm+9t&6KroUFK6^DsYn0L68vVzMs%?!Rxq=mwgeA;*xujTrrJNPd=h>=@76iOYpRU< z&k}v-E<3*}{IRnOTW&hPb56J1rW1AkVoMt$k+M`7daQAm6(J~=*IKjEz@_9IEx&r0 z%@y`Slc}G)D7Xe4CW0vi`-kP>hp<-A!k-^dvATXe;m3phn&aAHGbSaYgm!k0LVXjY znE4o|P$zMjhK8nLrYW|4H8XD_1bYyA1p4s1*$!&Msq2*!6(u?U*7TprSc$}_j{Tuk z!WYXy@%mhh)m$eWQ4X!wlWLoHGUw8h&H!6AK%XK!mKzmLAm+S4P>=1d_0mjwa*zVr z83_+j%A%lYHFp1a%1etp>oP6A(GiE}loDvmQq$p6TNmRO49Cazo-^*Y^k=td+%-!Pi&fxEcS`>^+stK(9u0Y8euS}U12{aSpI_7B!QXH zy6iAGNc$K;`vP8m)MmQPZnWEU3>|)Vc|h2xeF~|K4rVk!qTqWz*NLp0d>xk8GhZ*f zFcV~Z;WP0yI-kjHuN&?0b_F+O8NAFvr%CkF27=_Alv!&;7(HwvP4gtbuss`Tv62}R z;Z&O43wd<*6`%T_4389a91MVP055`GE0{$HmmV*JA^9V%Ea`mlj!6A{J?;dhLqliX zqVdFjL<;0jQ-A%$;X`M#o<-c(V?!@5jQwtxUFj9KGptxDosxBrf#TSIwSV*pgL>|R zESG}t`J6_KmqSi2@W%uo0y}61o-;Ud)FF7mj%QDOl@75Cl!#I})ex>Hza1&#c#T$2 z{E9L)8OQ*)Y_N4KIVC^P!HINHHR`~XcA?gDjcB(?-;VrGgvP2bdM`5g-nPpxx769! zC<#a`){z|p4q}ECd0E`!RxX`@Nc&0PHe0l)k*W=Fro)LB;^YbpYFxV+Y<_CDV@joa zlFT||o#q;#X>I~*!{!NY{CJQDOMzeB-SwPD%*@p!s+%x~VNFZ5Uyo6=yF&ti*^3D) z86G1~4d!lb*YtjzN<;)^z{HwWqg+0s_u;wt*hdc&Og%dfw^y|0=TXjle4mFhrtI=- z?C+Fzye%H8$sNw9!4Km%##eLO_48dq$oJ3m%5#c*m7VCJxi(C47T_m2gG|e3s|sut zpNYjX2~t)3<4N)wdblBrd}Zp_)7~fR;t8*5SOpQUMFGB^yN#PU%$*WcdUvoN%j=aSpt-3EvS1A3@;oL@LhNnJ8Z8`BPTBHN zwGs!iXQe<6*{C?}SmK5pJ%Ds?Cs+_AP5^D6`&hak5#QbG#nD+FJSBVmrHMNA@6Sgq-8Sg zkF?Ry$So5(aaU9k@>jOZU9m*?cR! z2SHK0(t_GW={<7``#b(^BpGA3`jKUub-T*GdwsHf)hxfRV4P+@pbn0gqS18 zyFgR3X&(so_JH9A1L={LB4)*>P-M2elen*Zvs&79(qc)wBDdim5icFUSvp5}N@l&& z7-QI_63D;lTN!R?dG5GpUS%Q9PgCC{81Ntrq7b04PFO(EdoRzjX6R0P&WGpb8BM+S z*}(d&9iqNhYzs(}T4Lp$P zPm%`ZO1HjWu@VZEPCEr?W`#||T7O63?-aGxUQInm9gF&EolkGV{Qa69ThPfyt(zUI zd>YBkt5dbeeaLjy3JI5C3^zu)^hhvStW@vyD=gI*EG7FmHmv)ftP72t6x{{%X+6le zH?(km&Id;egP{wA=T=+iX0;@f&9+of%(^XsjrwA^?N)>IQ;}NP!u4Sa%s>^SSQa7m z@(O3^-Xk>HZ+~Cw6QMObM~}qm6N|8=iISk};*9-RgZAY~s40LQ?t`Y#YH@5F=W{&i zFv`tS8+%WVDI16o`;&{?8JU>>1;ya>bjp3lfEVTb_pp!Zu zK+^Ac;e6}ixLu(P9fof%_XtRh&)ywRp&`ao~FKe!kGp z{h=uQv6~W)TCqxQ z%gvmL&5=pes7x6==h9{oBTLdhW_4=RdSNMvjMl#BF@F!Ci>jQOlR@8M4#uthQm;+) zOXrm;G5baXam*$pJ6DEG`2;^67YKWztmdtlBr4FO>IwoT&2EWxi*&;wkS={`F8^)f@?WTCF8*$;guz2b$8m(Bah5qo$l>BsMq7YwTo8EXnV3hgDaq_4RdEep(+aA=B z@mT%Tf1&Tl&Ru0ll(sLqga*!V>LonA7K+TD2) zty+OHcgBVbE&8djF|Mq^a1d|LTdlET)l>C$PlE=?BuL)_xR6~?VROKwbR43wc4Fu;_4<>MTWbgR7`r^<_XN54f;7gYiZhyqg z^l3HZHOJks^`@jLb%(`g9H4p2oL{=_3hU`1anzddA?8Y&LG4fp*6(Kamj_XiD@!V4 zUHYqc*o-1XcS4Wuh56$S)8R5H3fy5F1#7gq7E6($z9ezss!P3cEO!_Z%+-PiJw#4A zVSk$C+1&OVAgmqE8d!JdMM_L}_iJTcy#!_3$6k20-iU12_wzjYyKw;mc?lNK0iHQy zwp&}dYS(VTB@VQM38q(-kK#Ca@OG0ux%v=xzxZ3k`_-yErWdyWZ(TV8&+j0$RP(Lh zX{Hib-~F(WTiJy;I$nIcZrb}$KYnUsFvkZV_DH9n48BJuc-;)z zY&MVE>J#U47K=fMEs`H=hveXbcL}0iYV%L*e*7wJw&`aztlEJ5 z)ifocAoem{F%y=%bY@4{`6ZUUbo{!|x#w{do3kKmylYa`N?l)>VO2h<$Kk0#0;oK^ zuB6=3+&ifk==)loJ6URz;Q1KEg5{_WeTVbgS1l}-bFB3!^amn#y9AC)-~k)M`Zit5 z=T)Yr=ljE~Y%D%1y^}FU?oX)eU8VRb*8#}S1Qf%^Cyb-ZQ@ln9{60O4jad4%cK+N< za%PREV^G|ZSbC01q6za27E$#li5kqTYOC0a{_^`Gc9;h8sED63_>Cp;TuW=XF%OSx(0x)_Cv=54(Vl}9j7zrgxj*$jd1n$DBFS>4DY(M-P> zMrE@0BAS9*0Ipw~d0%lfdAj=~0Ek49vs!3a=Oyf4OUE=;Xdv|juH3YgZM>f)C%4a$ z>USqzXS&3Rfrn*uB6?7g&>=b!EYU#b32NgMW&UERDr;`kq-(#j7p>|EoWU%5$|4Cd zOI~**a{*O^UGdJmlw-DW1prK}`u-ZZRhN?nnv0%qPDRB@2{v=imBXAEVdzlBk$12E0gKQCRdrRXD#wBqp)pZ8yWx2+Pnt zsqqlontJRs0X^frCYPDLr1JdiES*-_@98ZQP^4LL))KqlL2<=s$*=J=s$LF5p`}nE zR3}(IgKqX1~bH+VomdT20Q*MI*S52cG9xU{-_II-R}8G%v~| zwuwB@p;-sH=^M7FJV(A(Kqgw8oYXaBO{H9X>KFhL`D=Q{Z45E*_lFF>OY%bZ%$%m2 zgS^I3;YHWGga#3>>z%^?c+!{k&+zW}9sNGuGs?7}xB>P+(8L(J>s@5oT8LjBcc+4Z zPcKmWZr2tlH|qlZ2kqnI%OK)V&4;**YeohSD z#kzd>SL~nG9l*b0F9u4q59|WLf`wE=btaW|&a;N6;c4TNIs@P8(WWvkD zo3uSM*l~PzH^&_&?wyD}%37Z&HlpZ7?DKnNBpKO{aF7{FleKc+0sB_abK^grj<%;B zM?HG{DzztVH(BCIsQO|u0Jn*Bvm{;#&b&w;fS`ff#qw!0NYK)aULANSwBE-fWF9Ir zo;y-skl)XQ9^JeJEsm@&@JCC^|J?L*3k>a8B2>k^sA-xu@oreM!K!SB9ua{8*FAg0 zt6!zCAHd(5%z|BQ*@kt5|NHx>#Y)6+?0hy2X`_kCa|nhyw1N52c&-iUPMPk`*g-XM}?2{-=S5g)@p4zOLm zRz$`9`2{r2$$pwf1jG^5i}ty4{mCp5^p6jQn*= z4@mu$D?LJuP<7KVhkVdg^uq&)ofBdJ+>;gAp4Zqf=JC1ruiHWUB!3);qvy1IntjW~ z5jd?*Dt`cXTyBXKC^`5uy!q$m{>L2OMofab4o$V)B>;{!TURz26Q8Z5=P{6T8cz|~ z&DO|l1}SX&uRO5vxi>A40=Gk=+HwyD1n`1yrNt=pXMo}7?&8PVE=u`i+8Lf?+TRtL zIE-dVm{30ip3y_`6*2jHYAi5tVG!u-#qbGCVx{}sOJ9TmAc$iZYt9oeaK5Q4vUZ1~ z*)L)5yUhtznOYp54VfP~DX&P(D~Z(xC8&k0oi2#&X7;oe`c`P#pq@rNpG_R%%5n=E z)^>bYV^*0mNn(ih zIJ8)^E-3grrhFum9}*~m^?i@){QyjHHrl}6s#AH0A4ZDJQ9koZa4wjqJ^C62_kAI- zW8z?n0QQl_kk;&5EZ6hLQ=OuR6Fi%sh0r`TFFPv6EJ(RvHu&HlW}`YWh&e1e`ojOk zY{aKb2Z2Y{OONlvzoh46O%Qvc+M|Y4{`oywW)OLv!nHsC%ffy@3W86%b9(7tsx~1= zR-U4hmB<*a)c@vM%76fr{mg3PFP#^YrRqbyZv48ZkmkxaN_d1`#(*UZPGD9XbpAp4L{=n1aXOYD`Jcmih&FVvj#m7C8(J0^y6u_u zHHJTQcVOsXkZw@UX$>;McGZk`BAFlh9&Mp5nim>gMuDbE0=?{^@v)cn)nxMG<10(M zfWDA8{O+s!<;qb~@Ex$Ur!%Vz8HN2ptA0tk5Kgf)cmsQamG4cCX62G@%rt6YaJ7== zP+@_u1`N$5_F;ZY+9*@I1~N49cnS*TVOZdL0%wBF6&t5Ji%ncH5nHiv!AB;wL!>I(hu3YmL@1q$CVk?r* zopj4v29@~P!8a+>-vQ+LMUSRG-leiYx~TvHWbtuXwh2%Ro2z!|4%vr5zTUop&x#%? zm8UpN>*nt}JI(D6FSc;x?ka(H*a{;UkD>yrP!&vQdK+LuN2Bb~(?N7oOhst>jjr9G zR*q;R0cY)P@@KA2rEd7FI=X%VI*`?SyaU#!HUXAPT$0qT z^#zpPz5-p&KW#bV5Ia397M$H`FkM0lYmIytz-A$l7w2h^BP_5TwCzw?o)j*-XzI2w zfa}hr<+t@8aH*vW6s4SL|Vp35@e`GbWHL<93;(FRE`>< zRDEz#kI%%7Mn>rAi#;H~U-fA+>jkTG@ZI|20Y$uN>U5o)9x!y#pk9D8QZAwm=$;Fy z+Yg$)4`1}VNti;nT##DQ{StYnXAAxgu%UEPsZw6GKfv>&ql1aTK zjI}eedT|U8wWR2&L;`5FA{E~{2@Z0m-n9ZjJ$apNH)hZ-~tgf+*6!haMkKL6}M zuHKf_LT?L@cDtJ8$zvu&MV%ZGIu>Zg=9;;Y${#)g zz<^dy@l!*v9`|r^WXB-~VK_XE3P>Do@ta^JBz#(mZ z&zzn(-g2Ae7Mi8k#aBd*)FxaT_6vbGx}~k->3#|KZ-~EgUyVk*D!W3AiLyt=<7-8I zIon8a1_HIv=GR$U7gsvaf6h1%&Swfhfg&p%J5LQgXHpT{FBvs= zJl^Z?rW5V+fDb<#6yP7vm4l*A_a8lFP=2j{4g75B!xs)l8+2BVK|U}ShEIKD)M#J; zXmwFU`_-xsB}8s4fxXS`udLu7#LdEnnEQ^;2sl2VSzlw{9duR-`)KZE=T4<7-f`{0 zj2dEQdsbqW^RuI938XSDe8YxGp=wog1>c*#=H!MKC@z(tF6!D|e}W_SOGiBC?9Ij_ zp)k4%hsuQ^OK7yn?3r&bPA}l^?&Q=ks-;W(wm4j-4}Bk{qgT=HZPoO4%|UI&JBcK! zNOwd68;uIrwMZ<@5gE7Y&uFpAycF|m6(fk&GmlkRBfX&M+OUuq%l1iEuwu`BT~BW0 zM^STKSOj(7E^3lyuL6Y6+DwnU16`WB>3+;LQ}InG%Gzy3lTjl14bIsw_TNE9FlA_U zC37xCEN0&5LwapIn8^qYtyH?R^*M*#jx)ol{Fion%Eei+UQ|9)?ZT?%bO~n;3b-bb zEavBqP~m+^uyHHrmaf@C)Le;%YQ8fgsppwUQla5)p+D?zz36@yFO852J`T{LDc#OLZ?*tV~q~w`or$@J63W5PB zYrf!V>&dO{6F?qVz$9j}(0IsTKWc&;>_*kzx}IK?bqv^TrL&Wqx(QJ^JOj)DetCb9s`Uw; zDFmwK_EwKn6kr|$wF@Wfg9X>hY+>M$SZs|pB=gw3{COX99%tk_VpLD+a1!2r1grH%_!}hwf{U135x!|8{S#`5U=}q3%g|grefnq zy-Y}@#ddXaEc)c=92ufVbLv!}Ii8a#1{z;1GamNY@Z;v`&Ame#$^<@;fcdJ;PI_Fh z_m!*!03hAaag6R&3q7Qf*Zv?HO1`~zei+0ztedKqzCL>HLlHoJP3fNp;#<(BD=RKr zZR70rd-L`Dv2$(|_}tCJJG@^ipO z_XywtmNeX3^M7S&qy&PR-XAiBe$W+CHK+v=dr!pv}q z8!a6B(kvU0(-2=Kj1{Qil?>x6gK_Fsg6R9*_ZEW4l3*nX31}eC&~J1m>DRCaH~VeI zzJ`XeQlr*Nx3w;P!g4@X-3m42L#Pa7dT{RUH!O5Wb<=OSo~I!lZPE_7FeS2UE_Hpd zot2FbH(fK=zL;hBZcs977xj~~dn=d7+>9@YPlrT(4n7yWqk;bnxsDjSV67Xk;Nexd z>UVkW(f>8jz0S3N+Pn3MipTT&*spGR?uETG8`Dr#NP>%+lI5^tTJ>F3K0Vmj?X-!% z&K@7V_(-%WOgNG38*kKuu>CvCVWTZJ^N&)FV}$ZdyO6uDte)8HwT20Ocq1_ZJ0OB1 zPS11irNljwnK*-1ch7s{6B??IWTLApM$YKq2K1rP ztL9=2y(?=&7S-56ogL>sX}HrGGWDoJMbR*b#V2lGNz=HwQ_p+|UnR$p90}E0JoM|P zNB;IK;^vmbyrwS~ab5wOB1|lw{@bxR}d|kB^nUbZ@!MKI+2UEA-MQ z-LfcHReEM>du~2_h`eKODdPzr6YhiFTaEjMF?Tf|O|UXlGQ|>MV&g|(3{U(l`A)D@ zW}VZqF<$zyVtdm<@Ey7M1P|vS3dt6%go#;)e&X@hUugGgfF1(}U$^6CJQp&GPh+qM~htobqnhi$D1IU2}zao7WX zvdOC7cUa1l2s2A zSoL!U$V~PIJoPqLl-LX`bAysuw7$NPwb5`3Rq`6ZzHT*a)N4QVn1}171P8wF`MyBL zo@vM}s=t~Pv_++Mi!UX*5H+`OAAzC~pY z-lnh>hye?bd1+(+e6-)zf(`SOP;PhNt5*N9xXtW^XbzsKy42qXo$@rcw38(nGx>xt z)4VHAkI6iwS9_-v(g_4vYCE4!X=q$G$}0_LyiRmuI%P9pv`)|K?VR>=!RUIejj^`< z#QdNrhJ337N~15n2i>pRV>m6^OD1#1a7MWIs!kIN5;regRYMO{Mvz3C$Ln6E-SomA zEd;gc%DYp3kjgy4rB(Zc?QV9EhiD|0^E}Agm>Sy2$N16;DM+P05wM@Q&#LWfVn49% zJMOXmX=B_&zQTDyx{A5Sb!$ele}CAa?ws))wayBOZVJXHs$%avGX{VMl|xRNxCNw} zd2v?9^yQo%&&C6$PqTpF5Kd8yj0qKo-^&@!b%)pA1q+}C`TT{ty2ZwegJqd`dD*|^ zJ}cGtbE|~9T&~uvc%`<y}jXf3P{&7ae@@B>&evtFBm zwiZ_H)~TA)H27qt^K9V9G`a8jDV&itH^DjM&*8tSpCDffbeH$AUEc zw6ps+2KGg>oXd6ERpqX(f#pKSTHwh1CV6rp9XS4(&E~UWLA!;6y$oeh@~-nLCIWg5 ziND7N6OPqVlAnn&@(X(uJNW>Vx?U;b^B<$nkaGGQ-Hs_M++upL6EQc?Dl^|+V10po z*r-oVRb4EM@X+O74de=mynKwI){c72kKsQ-{OxIc89{-rA~RTgY*|#MFslLy94vEi zg57@_G(JkJLY$GR!uz*+i%L)FHg>YmTSP>PZPHI^67Cy8yB~Rl=l0YF)&Le4sH^Vp zEa(^sJ)MB$0TM@v_GyaYQ2Fr3=h!KD%T5B%XT>`?ua>kmQcqUObg+~$MA69vwNBgD z%4k3G7vZ=16hPfyP+cis0#)=8H@r2+%gTY*#69zVVx#wM%Zx(QJz>JDWm*Oc#oH>^ zefK=7L2F|?mX6QSzH?+dgQCUBRQ8>I!N_3)(a!NwQpaMKfl>_3M@ppeV)5D!CRiA7 zcZ1P6WVL4>2=9nAK8vucC5quBKR=wUj(O3U6*fy+FDv@I^oUI!26;Ph_v6hEq!}g; zU91_I+9O3{ZsuW?IBrIn+~j$P2a`0M{u5;G-@#@Bg3jtH;NaRfxU~h1{ReG^K3hD{ zUvJ9exLE^jDNNM)WOH$lVxLi04UlAii_vS5#)cb~JQ(nH2CQTgvfqayeLub4l0`z8r=@~sN9w6y$RCf^p;k#m5*9P0!Py!48<`e$D zfnPbwJRx^Q6*g-vB3)O83_m>T;<{LsPP4Y2F3WTMWjm!}pzmdWLih;uR1;&mQk7o5 zp89l+l3R01zi5EWLM)z7Sc)-(#zLP6KfazE8+OyvaR-trI{Fyw_u66Ex8{z=v%D;Z z@eHY}gp_nZGda9g1*4mpDQSK}6V-Uu!PX6Y6>{psX@OqsYgTG~$v>o&S*0c`U5Pikwfapmp}8LoZ{xSio?WA@ z^f}@paeV=`YzMwHsK5A8)$Aqz6@hK!(>2H_Q7pSpxwA2pSM*{<-KEny$KGRN)M&9g z-bO#sIYtshSmW|!T!2aBcDcTM;9ALX@S47hMEWl9n+oAO5fXn~ON8Y zqPcI!QKdvF#J*P^;T~|#(zJ&#J5}v)2$dCZq+nsL5nONd& znwfmrb5y*zqs!Z)C$rklD!Yq)P`$)4cvBOjGn?D!iIlG|^kNpQA#~q9fQ+gaQ<3jq zH7$#feYWi!gXxbylemg~I)tcTgLU@i_HUh&kv=Um&0+K&9n&Ao0`PK*{>=M=k1N$}RF}jMf8QFv3qdtVlpBijG6XE zW8s=xO{hhG4f<2%LPQ|1d*7TFSTeQFO3k0%EvVsNq+z89Ao2=*#dZ z$n$fXx7;sr-4lav z2l9$-XhtMm-oSl4Lz>yv6P79)pQ2(rAWt2)_tNSkll~~>I?uxIt>pKu)nI>7_`TOx z6f%DqN*io@ohEt|VPmX_jO7`2kg<2xwQ>&$v*U?!Sru7H+Wagp{9;a#sg!74be+;M zrlfNDG0(F+eLg5Nw_*MOj(E{N0JZnWvg61^w;lducYLxolyYgpun}j!S?#rRaZ1x# zWQK|}(y(6BH_3#bik^e?p&U8F+eV$sOE{ zf=S*14)hqoS?m{xtCaMpzf-%?b;Z`;C(*2ka{+y~;{+uklScC6gL9b+FzUa!>Tj3fSuCYAJ~Hmnk@J zapg(fg#eG_K5xe%w$56>F!sm&5XO-|lZ9~I-b8H=$? zZxS63^eke3EYKis>D)+43{}OZ!vS)oCbCw;AE9`DhwJf5qQuQVVqmmfrrNT_y<>(e zgB#;GWio!N7>0P8QLu5+TemX*dbKwD(h8k+mG>(kp}LJ-zDV}Arjx%C#~q~-LsjV> zqbhbjaOP+;`(`^sOW6Y{{jLI1)eJcfXTPxqJe&N%)bnFWg7vW)1Ndq8TEQjmavV3jjintxqW_*^*$m#?Jy)p<6jwX7*Q-;rUDBe65I62^DeA zX1b|mR(tj!8MAvA9_OH)r0Q5DBq@|bL^v=8hA#N?=v$M;F#P6&!|_8 zfG*sgpcB-#$M*_2GELu&OoBTank34NMm6vxtiSn;G=Pz|TkI`|Q_ErW;X! zcYr99R}+!oj0a&Tga|9j&`uNZiu``lBa{!XVdP!s`{u3O?0VkhTT|&)Q{)*!#cliM zY-=ub@d|rUApDwrk7ncGWH7sQyv;y~2P%o3Xw=G=?h^^-wId21`)ZMO>`}|gcB}2@ zJFki418*`ot1CV8I$UoPgqM2tct%`=Zv-+Wan~8&7I%jLucq@?RSS|ncIow92k{{D zknhLqo?^tUffC+}PC2^+?*)N9o#~!TopEe|{AnIAr}%Tc4BuV%FR-U`dzp`YsNj#n z#}AhJW6y;~eKU|ct#DoYud48{EZ^WeJl~~K_i_ji#-fv-AhJ6UVPYV8T^)LRdPvL1 z%*u(A^2eYRT2@=5yhU)iuA@` z=&rD4?lUWoMOuP#rJ+b&^^MaOLjcYmrI6{+t~mjizH%}p`KnJCL>K_OgK3u&>I(N2 zw!gJSiXHW#Lhp;)U^{QXQ*=Ezu@>MEFMhtxvocW{Z>cyxm}qgI2N}e6$*S9AqsVVxX*El@w zOK&smN9^W=g0>`=C#*acNS_~PG5r)&zaYSrV6Gz=#lUt}P2PyoiTgRNSEeL(v?bMz zQ&a0%n5$nprc@jiY#}?`svOVX)_A%p;7dHdZU5Yt}uXocM{2tkVww`r7LDH;Ru2W_|$+%Q%dk?fG+*G+6z$TK1>{wP9pX{KA z9p*w{ZJd>%;_FxzYM0=t)z)CY)-XeCs#EMAyx=cezKvrv;k#a-X*gv%@oYu0Y7&2y24ZT08R;P||DebRfrJ$uQ!9PJl^6E)Yzc8=*oRrpdv z#B2SRh`?i*uHd=)uA@UgBEks>e=9m)asz`OJGl|$Gr&)#3gjAOzM9p7w7GYV)SlEH5_d^`F;?T8`iIt7|&wo!z79b?ZRd~5+V32alDswSQK>>XP~Z?vwO02%nk@|KM)dMph&nfJW$AT`7o^7? zoXiUR+!;YFA?NOv5J$&y2B=6>m9y$>J}914RgE{#pIZ~K#Vx&`o~LEi;b2&G$c3!FGBEAHUnXi1>7ybm@_kqls> zt#vXzs^jkaoxVdi7w@VM zbmrqpCd%bQbX~OFj&w3dND@p$H>KBQ%5-U|l=9ezy5T*9_}PuJ&kfgwl`7pTegyX; zbvDb3la|jTYE=ihOSEVaEma_OjmY#F=6Jq4&N)D$O}8&j55CdC*Znz|J@@@$?k3>y<#*6=`~+Z%f8o7m&{u--{XGPT%^L8A8jORY$Vrsag+>|LJJ-w~_>h;zFmHK$)F`Di9t0;lI zgAvFHo;23t7+Mbxe0P ziwSLE1(-L~f6~{{#p1EnCv$SR7N0r@5mjFA32-ThOf{5P#Pui8FO3HIid^u=^Zj!D z(igway!5zn+z>=x?^L~-VAaDD1J8heE=UuKe3@|r<#Z&0)kbMqJ>`MMgw$1Gn61>q!yaQ^%R-J!^AdCYR3`tTk4&s`(3i=Fiu=)Vq9(vdGbQL zasJqok-3b)$ks(tR-Lc+YkVd-bV}Nm-|@Ax1jyP9zW_As=n{uH70#B^g?8RkEiJO8dT6Ay%%b^abVM$jHwRN z>2FqWqb#Os&D$~fu(^ayvZQ#ihBM6S<%NKhbHgQG9KEZK1(6zv9XWh#3XC+ftAEbM zkZChY)Bg=vVPyzxE4&CyAz<-|D!kZD{QQ2+>89#3qRK! z)cfX-6p7HB@|Wwa4=H%&QzUvYB`UubFICfm1-8-GsA~E1E|<$eEiGT;?IvSzQIY0$ zke8&Pnx${e1ls#PmxTwo^A+_OUGa(9a{LN+Gdl{b2c0j%*}_PN7T0Ufb`&#`(+yvcAa=VA}-B3hH$@=TLv;5EQ0%cD9jHA_FK?9C( z_^`8I8%>TIdtHa=#Vdy7kCOJ&g$*LulXaI&z0tG)|7L%|CI3PZWV)c2|BCigu8_n= zxeg1ioZ7|o+dpMj1j)nADCZOzILK?|(Rpp()C+@GWHT3{mQUL|Sp8{34OauJ1;%XH znCkGby{H#l$35RhXgu6uqDyW$qRbR71b@0h14&5CO$LSg>cMKN_Zy@4V!^X^!eI>1 zv6YxCSD(#r26ne6#5fUPG852#vIX1h!fUSTibXsPXT}x7#>Lq2Z^`}hQ~dh|06X^r zg&7?h^H2xIj7tQr^__XhioO)Djf@uK!Kly6g_wtgn9O)HK#+x&#As of October 17th, the forecast gives **Donald Trump a 93% chance of beating Kamala Harris** in Nebraska CD-1.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n

\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Nebraska CD-1. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-1.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 93% chance of beating Kamala Harris** in Nebraska CD-1.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Nebraska CD-1. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-1.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Nebraska CD-2/execute-results/html.json b/_freeze/2024-potus/Nebraska CD-2/execute-results/html.json index fd7f591c..a2ee648c 100644 --- a/_freeze/2024-potus/Nebraska CD-2/execute-results/html.json +++ b/_freeze/2024-potus/Nebraska CD-2/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 89% chance of beating Donald Trump** in Nebraska CD-2.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-2.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 86% chance of beating Donald Trump** in Nebraska CD-2.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-2.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Nebraska CD-3/execute-results/html.json b/_freeze/2024-potus/Nebraska CD-3/execute-results/html.json index 3b7b3894..cdc64174 100644 --- a/_freeze/2024-potus/Nebraska CD-3/execute-results/html.json +++ b/_freeze/2024-potus/Nebraska CD-3/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Nebraska CD-3.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Nebraska CD-3. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-3.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Nebraska CD-3.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Nebraska CD-3. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska CD-3.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Nebraska/execute-results/html.json b/_freeze/2024-potus/Nebraska/execute-results/html.json index d90700d1..3727f9d4 100644 --- a/_freeze/2024-potus/Nebraska/execute-results/html.json +++ b/_freeze/2024-potus/Nebraska/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Nebraska.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Nebraska.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nebraska.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Nevada/execute-results/html.json b/_freeze/2024-potus/Nevada/execute-results/html.json index 76a1687b..b3671e50 100644 --- a/_freeze/2024-potus/Nevada/execute-results/html.json +++ b/_freeze/2024-potus/Nevada/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 51% chance of beating Kamala Harris** in Nevada.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nevada.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 53% chance of beating Kamala Harris** in Nevada.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Nevada.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/New Hampshire/execute-results/html.json b/_freeze/2024-potus/New Hampshire/execute-results/html.json index f61b51f2..ba4b27d5 100644 --- a/_freeze/2024-potus/New Hampshire/execute-results/html.json +++ b/_freeze/2024-potus/New Hampshire/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 86% chance of beating Donald Trump** in New Hampshire.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Hampshire.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 84% chance of beating Donald Trump** in New Hampshire.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Hampshire.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/New Jersey/execute-results/html.json b/_freeze/2024-potus/New Jersey/execute-results/html.json index 4fd79fde..e3080c44 100644 --- a/_freeze/2024-potus/New Jersey/execute-results/html.json +++ b/_freeze/2024-potus/New Jersey/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 96% chance of beating Donald Trump** in New Jersey.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in New Jersey. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Jersey.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 96% chance of beating Donald Trump** in New Jersey.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in New Jersey. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Jersey.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/New Mexico/execute-results/html.json b/_freeze/2024-potus/New Mexico/execute-results/html.json index ffd6ead9..632e11fa 100644 --- a/_freeze/2024-potus/New Mexico/execute-results/html.json +++ b/_freeze/2024-potus/New Mexico/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 90% chance of beating Donald Trump** in New Mexico.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Mexico.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 88% chance of beating Donald Trump** in New Mexico.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New Mexico.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/New York/execute-results/html.json b/_freeze/2024-potus/New York/execute-results/html.json index bd3c1afd..d0e311a8 100644 --- a/_freeze/2024-potus/New York/execute-results/html.json +++ b/_freeze/2024-potus/New York/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in New York.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New York.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in New York.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/New York.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/North Carolina/execute-results/html.json b/_freeze/2024-potus/North Carolina/execute-results/html.json index 19907d8c..f582af6e 100644 --- a/_freeze/2024-potus/North Carolina/execute-results/html.json +++ b/_freeze/2024-potus/North Carolina/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 66% chance of beating Kamala Harris** in North Carolina.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/North Carolina.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 68% chance of beating Kamala Harris** in North Carolina.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/North Carolina.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/North Dakota/execute-results/html.json b/_freeze/2024-potus/North Dakota/execute-results/html.json index 5257192d..b21d1c3c 100644 --- a/_freeze/2024-potus/North Dakota/execute-results/html.json +++ b/_freeze/2024-potus/North Dakota/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in North Dakota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/North Dakota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in North Dakota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/North Dakota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Ohio/execute-results/html.json b/_freeze/2024-potus/Ohio/execute-results/html.json index 51d3a54d..9c22ecc8 100644 --- a/_freeze/2024-potus/Ohio/execute-results/html.json +++ b/_freeze/2024-potus/Ohio/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 97% chance of beating Kamala Harris** in Ohio.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Ohio.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 98% chance of beating Kamala Harris** in Ohio.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Ohio.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Oklahoma/execute-results/html.json b/_freeze/2024-potus/Oklahoma/execute-results/html.json index e9dc6f7a..9a32d1dd 100644 --- a/_freeze/2024-potus/Oklahoma/execute-results/html.json +++ b/_freeze/2024-potus/Oklahoma/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Oklahoma.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Oklahoma.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Oklahoma.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Oklahoma.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Oregon/execute-results/html.json b/_freeze/2024-potus/Oregon/execute-results/html.json index 27be216d..4de785ec 100644 --- a/_freeze/2024-potus/Oregon/execute-results/html.json +++ b/_freeze/2024-potus/Oregon/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 94% chance of beating Donald Trump** in Oregon.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Oregon.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 96% chance of beating Donald Trump** in Oregon.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Oregon.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Pennsylvania/execute-results/html.json b/_freeze/2024-potus/Pennsylvania/execute-results/html.json index bbce6a94..15da671f 100644 --- a/_freeze/2024-potus/Pennsylvania/execute-results/html.json +++ b/_freeze/2024-potus/Pennsylvania/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 52% chance of beating Kamala Harris** in Pennsylvania.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Pennsylvania.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 54% chance of beating Kamala Harris** in Pennsylvania.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Pennsylvania.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Rhode Island/execute-results/html.json b/_freeze/2024-potus/Rhode Island/execute-results/html.json index 4b4b8eee..e7b54524 100644 --- a/_freeze/2024-potus/Rhode Island/execute-results/html.json +++ b/_freeze/2024-potus/Rhode Island/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Rhode Island.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Rhode Island.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Rhode Island.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Rhode Island.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/South Carolina/execute-results/html.json b/_freeze/2024-potus/South Carolina/execute-results/html.json index 0ccabfd9..6707b818 100644 --- a/_freeze/2024-potus/South Carolina/execute-results/html.json +++ b/_freeze/2024-potus/South Carolina/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 98% chance of beating Kamala Harris** in South Carolina.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/South Carolina.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 98% chance of beating Kamala Harris** in South Carolina.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/South Carolina.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/South Dakota/execute-results/html.json b/_freeze/2024-potus/South Dakota/execute-results/html.json index ece8a3db..b7deb92f 100644 --- a/_freeze/2024-potus/South Dakota/execute-results/html.json +++ b/_freeze/2024-potus/South Dakota/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 98% chance of beating Kamala Harris** in South Dakota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in South Dakota. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/South Dakota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 98% chance of beating Kamala Harris** in South Dakota.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in South Dakota. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/South Dakota.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Tennessee/execute-results/html.json b/_freeze/2024-potus/Tennessee/execute-results/html.json index 6c2ac6e1..ad51397d 100644 --- a/_freeze/2024-potus/Tennessee/execute-results/html.json +++ b/_freeze/2024-potus/Tennessee/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Tennessee.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Tennessee.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Tennessee.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Tennessee.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Texas/execute-results/html.json b/_freeze/2024-potus/Texas/execute-results/html.json index 3b42254a..20d8c8c4 100644 --- a/_freeze/2024-potus/Texas/execute-results/html.json +++ b/_freeze/2024-potus/Texas/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a 93% chance of beating Kamala Harris** in Texas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Texas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a 94% chance of beating Kamala Harris** in Texas.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Texas.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Utah/execute-results/html.json b/_freeze/2024-potus/Utah/execute-results/html.json index f15e6f7b..d1824deb 100644 --- a/_freeze/2024-potus/Utah/execute-results/html.json +++ b/_freeze/2024-potus/Utah/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Utah.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Utah.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Utah.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Utah.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Vermont/execute-results/html.json b/_freeze/2024-potus/Vermont/execute-results/html.json index eb15d35d..9ee9dd03 100644 --- a/_freeze/2024-potus/Vermont/execute-results/html.json +++ b/_freeze/2024-potus/Vermont/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Vermont.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Vermont.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Vermont.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Vermont.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Virginia/execute-results/html.json b/_freeze/2024-potus/Virginia/execute-results/html.json index 0103e3d6..d3c3c59b 100644 --- a/_freeze/2024-potus/Virginia/execute-results/html.json +++ b/_freeze/2024-potus/Virginia/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 90% chance of beating Donald Trump** in Virginia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Virginia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 89% chance of beating Donald Trump** in Virginia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Virginia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Washington/execute-results/html.json b/_freeze/2024-potus/Washington/execute-results/html.json index 9016529d..90f86102 100644 --- a/_freeze/2024-potus/Washington/execute-results/html.json +++ b/_freeze/2024-potus/Washington/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Washington.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Washington.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a >99% chance of beating Donald Trump** in Washington.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Washington.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/West Virginia/execute-results/html.json b/_freeze/2024-potus/West Virginia/execute-results/html.json index e8b230b4..83470678 100644 --- a/_freeze/2024-potus/West Virginia/execute-results/html.json +++ b/_freeze/2024-potus/West Virginia/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in West Virginia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/West Virginia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in West Virginia.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/West Virginia.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Wisconsin/execute-results/html.json b/_freeze/2024-potus/Wisconsin/execute-results/html.json index c363530b..c72541ec 100644 --- a/_freeze/2024-potus/Wisconsin/execute-results/html.json +++ b/_freeze/2024-potus/Wisconsin/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Kamala Harris a 56% chance of beating Donald Trump** in Wisconsin.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Wisconsin.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Kamala Harris a 52% chance of beating Donald Trump** in Wisconsin.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n
\n\n
\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Wisconsin.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/2024-potus/Wyoming/execute-results/html.json b/_freeze/2024-potus/Wyoming/execute-results/html.json index a0e97043..fd2bb28a 100644 --- a/_freeze/2024-potus/Wyoming/execute-results/html.json +++ b/_freeze/2024-potus/Wyoming/execute-results/html.json @@ -2,7 +2,7 @@ "hash": "33ef22863f8e8a8325bc405c8322e46f", "result": { "engine": "knitr", - "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 17th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Wyoming.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Wyoming. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Wyoming.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Michigan](Michigan.qmd)
[Wisconsin](Wisconsin.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", + "markdown": "---\nformat: \n html:\n code-fold: true\n page-layout: custom\n fig-align: center\n fig-width: 12\n fig-height: 4\nexecute: \n message: false\n warning: false\n echo: false\nparams:\n state: \"Oklahoma\"\n branch: \"dev\"\n---\n\n::: {.cell}\n\n:::\n\n\n\n::::: {.column-body-custom}\n\n:::: {.columns}\n::: {.column width=\"80%\"}\n\n\nAs of October 18th, the forecast gives **Donald Trump a >99% chance of beating Kamala Harris** in Wyoming.\n\n\n:::\n::: {.column width=\"20%\"}\n:::\n::::\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Presidential probabilities\nEach day, the model simulates thousands of plausible election results, from landslide victories to tightly contested races.\nEach candidate’s probability of winning is the proportion of simulations that they’ve won.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### Forecasted election-day voteshare\nThe model first constructs a polling average, pooling data across similar states when polls are sparse.\nIt then projects forward to election day, initially relying on non-polling indicators like economic growth and partisanship, but aligning more closely with the polling average as election day approaches.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n
*No polls have been conducted in Wyoming. The projected voteshare is estimated using economic and approval indicators, as well as polling information from similar states.*
\n\n::: {.cell}\n\n:::\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"60%\"}\n\n\n### State similarities\nThe model uses state characteristics, like demographic composition, population density, and education, to estimate how similar states are to one another.\nSimilar states are more likely to share polling biases and see similar shifts in polling trendlines.\n\n\n:::\n::: {.column width=\"40%\"}\n:::\n::::\n\n\n\n![](https://raw.githubusercontent.com/markjrieke/2024-potus/main/img/Wyoming.png){height=700 fig-align='center'}\n\n\n\n---\n\n:::: {.columns}\n::: {.column width=\"30%\"}\n\n\n\nSources: Ballotpedia; Cook Political Report; The Economist; Federal Reserve Bank of St. Louis; FiveThirtyEight; Urban Stats; 270towin.com\n
\n
\n[{{< fa brands github >}} View the source code](https://github.com/markjrieke/2024-potus/tree/main)\n
\n[{{< fa solid database >}} Explore the output](https://github.com/markjrieke/2024-potus/tree/main/out)\n\n\n\n:::\n::::\n\n---\n\n:::::\n\n::::: {.column-margin-custom}\n\n\n\n**[National Forecast](National.qmd)**
[How this works](../posts/2024-07-04-forecast-methodology/index.qmd)\n\n
**Competitive states**
[Wisconsin](Wisconsin.qmd)
[Michigan](Michigan.qmd)
[Nevada](Nevada.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Arizona](Arizona.qmd)
[North Carolina](North Carolina.qmd)
[Georgia](Georgia.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Minnesota](Minnesota.qmd)
[New Hampshire](New Hampshire.qmd)\n\n
**All states**
[Alabama](Alabama.qmd)
[Alaska](Alaska.qmd)
[Arizona](Arizona.qmd)
[Arkansas](Arkansas.qmd)
[California](California.qmd)
[Colorado](Colorado.qmd)
[Connecticut](Connecticut.qmd)
[Delaware](Delaware.qmd)
[District of Columbia](District of Columbia.qmd)
[Florida](Florida.qmd)
[Georgia](Georgia.qmd)
[Hawaii](Hawaii.qmd)
[Idaho](Idaho.qmd)
[Illinois](Illinois.qmd)
[Indiana](Indiana.qmd)
[Iowa](Iowa.qmd)
[Kansas](Kansas.qmd)
[Kentucky](Kentucky.qmd)
[Louisiana](Louisiana.qmd)
[Maine CD-1](Maine CD-1.qmd)
[Maine CD-2](Maine CD-2.qmd)
[Maine](Maine.qmd)
[Maryland](Maryland.qmd)
[Massachusetts](Massachusetts.qmd)
[Michigan](Michigan.qmd)
[Minnesota](Minnesota.qmd)
[Mississippi](Mississippi.qmd)
[Missouri](Missouri.qmd)
[Montana](Montana.qmd)
[Nebraska CD-1](Nebraska CD-1.qmd)
[Nebraska CD-2](Nebraska CD-2.qmd)
[Nebraska CD-3](Nebraska CD-3.qmd)
[Nebraska](Nebraska.qmd)
[Nevada](Nevada.qmd)
[New Hampshire](New Hampshire.qmd)
[New Jersey](New Jersey.qmd)
[New Mexico](New Mexico.qmd)
[New York](New York.qmd)
[North Carolina](North Carolina.qmd)
[North Dakota](North Dakota.qmd)
[Ohio](Ohio.qmd)
[Oklahoma](Oklahoma.qmd)
[Oregon](Oregon.qmd)
[Pennsylvania](Pennsylvania.qmd)
[Rhode Island](Rhode Island.qmd)
[South Carolina](South Carolina.qmd)
[South Dakota](South Dakota.qmd)
[Tennessee](Tennessee.qmd)
[Texas](Texas.qmd)
[Utah](Utah.qmd)
[Vermont](Vermont.qmd)
[Virginia](Virginia.qmd)
[Washington](Washington.qmd)
[West Virginia](West Virginia.qmd)
[Wisconsin](Wisconsin.qmd)
[Wyoming](Wyoming.qmd)\n\n\n\n:::::\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua"