From f0e926c3d596f33407af6a518291cea20579055c Mon Sep 17 00:00:00 2001 From: Lorena Vieira Date: Fri, 28 Jun 2024 12:24:48 -0300 Subject: [PATCH] =?UTF-8?q?Adicionando=20resolu=C3=A7=C3=A3o=20da=20ativid?= =?UTF-8?q?ade=20para=20casa?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../para-casa/Student_performance_data _.csv | 2393 +++++++++++++++++ exercicios/para-casa/relatorio_qui.md | 55 + exercicios/para-casa/relatorio_t.md | 54 + exercicios/para-casa/relatorio_z.md | 48 + exercicios/para-casa/testes.ipynb | 1604 +++++++++++ 5 files changed, 4154 insertions(+) create mode 100644 exercicios/para-casa/Student_performance_data _.csv create mode 100644 exercicios/para-casa/relatorio_qui.md create mode 100644 exercicios/para-casa/relatorio_t.md create mode 100644 exercicios/para-casa/relatorio_z.md create mode 100644 exercicios/para-casa/testes.ipynb diff --git a/exercicios/para-casa/Student_performance_data _.csv 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+3389,17,0,0,1,7.583217279598868,4,1,4,0,1,0,0,3.2791497270250276,4.0 +3390,16,1,0,2,6.8054996446806175,20,0,2,0,0,0,1,1.142332879526157,2.0 +3391,16,1,1,0,12.416652655483858,17,0,2,0,1,1,0,1.8032967626292098,1.0 +3392,16,1,0,2,17.819907492885296,13,0,2,0,0,0,1,2.1400138778043982,1.0 diff --git a/exercicios/para-casa/relatorio_qui.md b/exercicios/para-casa/relatorio_qui.md new file mode 100644 index 0000000..e975641 --- /dev/null +++ b/exercicios/para-casa/relatorio_qui.md @@ -0,0 +1,55 @@ +## Relatório de Análise de Dados - Teste de Hipóteses + +**Título:** Analisando a Relação entre Gênero e Participação em Atividades Extracurriculares (Teste Qui-Quadrado) + + +**1. Introdução** + +Neste relatório, analisaremos a relação entre o gênero dos alunos e a participação em atividades extracurriculares, utilizando dados coletados em uma pesquisa. A pergunta de pesquisa que buscamos responder é: "Há uma relação entre o gênero dos alunos e a participação em atividades extracurriculares?" Compreender essa relação pode ser útil para a administração escolar e para o desenvolvimento de programas que incentivem a participação dos alunos. + + +**2. Metodologia** + +* **Conjunto de Dados:** para este estudo, utilizaremos o conjunto de dados "Student_performance_data _" disponível no Kaggle. Esse conjunto de dados contém informações abrangentes sobre 2.392 estudantes do ensino médio, detalhando seus dados demográficos (idade, gênero, etnia, nível de educação dos pais), hábitos de estudo, envolvimento dos pais, atividades extracurriculares e desempenho acadêmico. A variável alvo, GradeClass, classifica as notas dos alunos em categorias distintas, fornecendo um conjunto de dados robusto para pesquisa educacional, modelagem preditiva e análise estatística. + +* **Teste de Hipóteses:** no presente estudo faremos a análise da relação entre as variáveis categóricas "Gênero" e "Participação em Atividades Extracurriculares", e para isso utilizaremos o teste Qui-Quadrado de Independência. Este teste compara as frequências observadas de cada combinação de categorias com as frequências esperadas sob a hipótese de independência entre as variáveis. + +* **Análise:** utilizaremos a biblioteca `scipy.stats` do Python para realizar o teste Qui-Quadrado. Os passos da análise incluem: +1. Importar as bibliotecas necessárias. +2. Carregar o conjunto de dados. +3. Criar a tabela de contingência com as frequências observadas. +4. Realizar o teste Qui-Quadrado. +5. Interpretar os resultados do teste, incluindo o valor p e a estatística do teste. + + +**3. Resultados** + +A tabela de contingência abaixo mostra as frequências observadas de cada combinação de gênero e participação em atividades extracurriculares: + +| Gênero | Participação em Atividades Extracurriculares | Total | +|----------|---------------------------------------------|-------| +| Masculino| 718 | 452 | 1170 | +| Feminino | 757 | 465 | 1222 | +| Total | 1475 | 917 | 2392 | + +O teste Qui-Quadrado retornou os seguintes resultados: + +* **Estatística Qui-Quadrado:** 0.062318177188428615 +* **Valor p:** 0.8028687865937287 +* **Graus de Liberdade:** 1 + +**4. Discussão** + +O valor p obtido (0.8028687865937287) é maior que o nível de significância usual de 0.05. Isso indica que não há evidências suficientes para rejeitar a hipótese nula de independência entre gênero e participação em atividades extracurriculares. Portanto, concluímos que não existe uma relação significativa entre essas duas variáveis. + +Embora a análise sugira que não há uma relação estatisticamente significativa entre o gênero dos alunos e a participação em atividades extracurriculares, é importante considerar as frequências observadas e esperadas. As proporções de participação para ambos os gêneros são bastante semelhantes. + + +**5. Conclusão** + +A análise realizada com o teste Qui-Quadrado sugere que não existe uma relação significativa entre o gênero dos alunos e a participação em atividades extracurriculares. Os resultados indicam que as proporções de participação são semelhantes entre pessoas do gênero masculino e feminino. Esta informação pode ser útil para as escolas na hora de planejar e implementar atividades que promovam a participação de todos os alunos, independentemente do gênero. + + +**6. Limitações** + +É importante reconhecer as limitações da análise. O conjunto de dados utilizado pode não ser representativo de toda a população escolar. Além disso, a análise não considera outros fatores que podem influenciar a participação em atividades extracurriculares, como interesses pessoais, disponibilidade de tempo e o ambiente escolar. diff --git a/exercicios/para-casa/relatorio_t.md b/exercicios/para-casa/relatorio_t.md new file mode 100644 index 0000000..c1bc88d --- /dev/null +++ b/exercicios/para-casa/relatorio_t.md @@ -0,0 +1,54 @@ +## Relatório de Análise de Dados - Teste T de uma Amostra + +**Título:** Investigando a Média do GPA dos Alunos (Teste T) + +**1. Introdução** + +Neste relatório, realizaremos um teste t de uma amostra para investigar se a média do GPA dos alunos em nossa amostra difere significativamente de um valor de referência de 2.5. O GPA (Grade Point Average) é uma medida comum de desempenho acadêmico, e entender se há uma diferença significativa em relação a este valor pode fornecer insights importantes sobre o desempenho dos alunos. + +**2. Metodologia** + +* **Conjunto de Dados:** utilizamos dados do desempenho dos alunos, extraídos de um arquivo CSV denominado 'Student_performance_data_.csv'. Este conjunto de dados contém diversas variáveis, incluindo o GPA dos alunos. + +* **Teste Estatístico - Teste t de uma Amostra:** o teste t de uma amostra compara a média da amostra com uma média populacional conhecida (hipótese nula) para determinar se há uma diferença estatisticamente significativa. + +* **Definição de Hipóteses:** +* Hipótese Nula (H0): a média do GPA dos alunos é igual a 2.5. +* Hipótese Alternativa (H1): a média do GPA dos alunos é diferente de 2.5. + +* **Análise:** utilizaremos a biblioteca `scipy.stats` do Python para realizar o teste T. Os passos da análise incluem: +1. Importar as bibliotecas necessárias. +2. Carregar o conjunto de dados. +3. Separação e tratamento dos dados a serem analisados (GPA). +4. Definição da hipótese nula (H0) e da hipótese alternativa (H1). +5. Cálculo da estatística T e do valor p usando a função `ttest_1samp` da biblioteca `scipy.stats`. +6. Se o valor p resultante for menor que o nível de significância (usualmente 0.05), rejeitamos a hipótese nula, indicando que há uma diferença significativa na média do GPA dos alunos em relação ao valor de referência. + +**3. Resultados** + +A análise estatística resultou nos seguintes valores: + +- **Estatística t:** -31.73 +- **Valor p:** 9.449925924922258e-185 (valor de p muito próximo de zero) + +Como o valor p é significativamente menor que 0.05, rejeitamos a hipótese nula. Isso sugere que a média do GPA dos alunos na amostra é estatisticamente diferente de 2.5. + + +**4. Visualização dos Dados** + +Para melhor visualizar a distribuição dos dados e a posição da média amostral em relação a média populacional, foi criado um histograma e apresentado no corpo do teste T em 'testes.ipynb'. + + +**5. Discussão** + +A diferença significativa encontrada no GPA dos alunos indica que eles apresentam um desempenho médio que diverge do valor esperado de 2.5. A média amostral calculada foi de aproximadamente 1.94, o que está abaixo do valor de referência. Essa diferença pode refletir desafios acadêmicos enfrentados pelos alunos ou a eficácia variável dos métodos de ensino. Essa informação pode ser crucial para ajustar estratégias educacionais ou programas de suporte aos alunos, visando melhorar o desempenho acadêmico e garantir uma aprendizagem mais eficaz de um percentual maior dos alunos. + + +**6. Conclusão** + +Com base nos resultados do teste t, concluímos que a média do GPA dos alunos é estatisticamente diferente de 2.5. Isso sugere que a amostra de alunos analisada apresenta um desempenho acadêmico significativamente distinto do valor de referência. Essa análise pode informar decisões educacionais e estratégias de apoio aos estudantes para melhorar o desempenho acadêmico. + + +**7. Limitações** + +É importante considerar que este estudo se baseia em uma amostra específica de dados. Outros fatores não considerados, podem influenciar o desempenho acadêmico dos alunos. diff --git a/exercicios/para-casa/relatorio_z.md b/exercicios/para-casa/relatorio_z.md new file mode 100644 index 0000000..fc77fe6 --- /dev/null +++ b/exercicios/para-casa/relatorio_z.md @@ -0,0 +1,48 @@ +## Relatório: Teste de Proporções com Teste Z + +**Título:** Analisando a proporção de mulheres inscritas no enem 2019 com idade superior a 20 anos + +**1. Introdução** + +O teste Z de proporções é uma ferramenta estatística utilizada para verificar se uma proporção amostral difere significativamente de uma proporção populacional esperada. Neste caso, estamos interessadas em investigar se a proporção de mulheres com idade superior a 20 anos que realizaram o ENEM 2019 é maior do que 50%. + + +**2. Metodologia** + +* **Conjunto de Dados:** primeiramente, carregamos os dados do arquivo CSV 'enem_2019.csv' e filtramos apenas as informações relevantes, ou seja, as idades das mulheres que realizaram o exame. + +* **Definição de Hipóteses:** +* Hipótese Nula (H0): a proporção de mulheres com idade maior que 20 anos é igual a 50%. +* Hipótese Alternativa (H1): a proporção de mulheres com idade maior que 20 anos é maior que 50%. + +* **Análise:** utilizaremos a biblioteca `statsmodels` do Python para realizar o teste Z. Os passos da análise incluem: +1. Importar as bibliotecas necessárias. +2. Carregar o conjunto de dados. +3. Separação e tratamento dos dados a serem analisados (idade, sexo). +4. Definição da hipótese nula (H0) e da proporção populacional sobre a hipótese nula (p0). +5. Cálculo da estatística Z e o valor p usando a função `proportions_ztest` da biblioteca `statsmodels.stats.proportion`. +6. Se o valor p resultante for menor que o nível de significância (usualmente 0.05), rejeitamos a hipótese nula, indicando que a proporção de mulheres com idade maior que 20 anos é maior que 50%. + + +**3. Resultados** + +Os resultados do teste Z são os seguintes: + +* **Estatística Z:** -423.4594208018838 +* **Valor p:** 1.0 + + +**4. Visualização dos Dados** + +Para uma melhor compreensão, podemos visualizar através do gráfico gerado a distribuição normal com a estatística Z marcada e a área correspondente ao valor p destacada em vermelho (gráfico disponível em: https://colab.research.google.com/drive/1J2sdvMV03euIZY5gx-pe63WBz0eByHqC#scrollTo=S7-RmoGo5IB2). + + +**5. Discussão** + +Com base no valor p obtido e considerando um nível de significância de 5%, não encontramos evidências suficientes para rejeitar a hipótese nula. Portanto, não há suporte estatístico para afirmar que a proporção de mulheres com idade superior a 20 anos que realizaram o ENEM 2019 seja maior que 50%. + + +**6. Conclusão** + +Não rejeitamos a hipótese nula, pois não há evidências suficientes para concluir que a proporção de mulheres com idade maior que 20 anos é maior que 50%. + diff --git a/exercicios/para-casa/testes.ipynb b/exercicios/para-casa/testes.ipynb new file mode 100644 index 0000000..b4f03bd --- /dev/null +++ b/exercicios/para-casa/testes.ipynb @@ -0,0 +1,1604 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.stats as stats\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Age Gender Ethnicity ParentalEducation GPA\n", + "0 17 1 0 2 2.929196\n", + "1 18 0 0 1 3.042915\n", + "2 15 0 2 3 0.112602\n", + "3 17 1 0 3 2.054218\n", + "4 17 1 0 2 1.288061\n", + "... ... ... ... ... ...\n", + "2387 18 1 0 3 3.455509\n", + "2388 17 0 0 1 3.279150\n", + "2389 16 1 0 2 1.142333\n", + "2390 16 1 1 0 1.803297\n", + "2391 16 1 0 2 2.140014\n", + "\n", + "[2392 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_colunas = df1[['Age', 'Gender', 'Ethnicity', 'ParentalEducation','GPA']]\n", + "df_colunas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TESTE DE HIPÓTESES:** \n", + "\n", + "TESTE T: relacionar médias de notas (gpa)\n", + "\n", + "GradeClass: Classification of students' grades based on GPA:\n", + "\n", + "0: 'A' (GPA >= 3.5)\n", + "\n", + "1: 'B' (3.0 <= GPA < 3.5)\n", + "\n", + "2: 'C' (2.5 <= GPA < 3.0)\n", + "\n", + "3: 'D' (2.0 <= GPA < 2.5)\n", + "\n", + "4: 'F' (GPA < 2.0)\n", + "\n", + "H0 = média do GPA dos alunos é 2.5\n", + "\n", + "H1 = média do GPA dos alunos não é 2.5" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Lorena\\AppData\\Local\\Temp\\ipykernel_3732\\2220151043.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_colunas['GPA'] = df_colunas['GPA'].round(2)\n" + ] + }, + { + "data": { + "text/html": [ + "
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..................
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" + ], + "text/plain": [ + " Age Gender Ethnicity ParentalEducation GPA\n", + "0 17 1 0 2 2.93\n", + "1 18 0 0 1 3.04\n", + "2 15 0 2 3 0.11\n", + "3 17 1 0 3 2.05\n", + "4 17 1 0 2 1.29\n", + "... ... ... ... ... ...\n", + "2387 18 1 0 3 3.46\n", + "2388 17 0 0 1 3.28\n", + "2389 16 1 0 2 1.14\n", + "2390 16 1 1 0 1.80\n", + "2391 16 1 0 2 2.14\n", + "\n", + "[2392 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_colunas['GPA'] = df_colunas['GPA'].round(2)\n", + "df_colunas" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Hipótese nula: 2.5\n", + "Hipótese alternativa: two-sided\n", + "\n", + "Estatística t: -31.73482737215511\n", + "Valor p: 9.449925924922258e-185\n" + ] + } + ], + "source": [ + "# 4. Definir a hipótese nula e alternativa\n", + "hipotese_nula = 2.5\n", + "hipotese_alternativa = 'two-sided'\n", + "print(\"\\nHipótese nula:\", hipotese_nula)\n", + "print(\"Hipótese alternativa:\", hipotese_alternativa)\n", + "\n", + "# 5. Realizar o teste t de uma amostra\n", + "t_statistic, p_value = stats.ttest_1samp(a=df_colunas['GPA'], popmean=hipotese_nula) # Realiza o teste t de uma amostra, comparando a média da coluna 'GPA' do DataFrame df_gpa com a média populacional definida na hipótese nula.\n", + "print(\"\\nEstatística t:\", t_statistic) # Imprime a estatística t do teste.\n", + "print(\"Valor p:\", p_value) # Imprime o valor p do teste." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Rejeitamos a hipótese nula. A média do GPA dos alunos é significativamente diferente de 2.5\n" + ] + } + ], + "source": [ + "# 6. Interpretar os resultados\n", + "if p_value < 0.05: # Verifica se o valor p é menor que 0.05, o nível de significância comum.\n", + " print(\"\\nRejeitamos a hipótese nula. A média do GPA dos alunos é significativamente diferente de\", hipotese_nula) # Se o valor p for menor que 0.05, a hipótese nula é rejeitada, indicando que a média do GPA da amostra é significativamente diferente da média populacional definida na hipótese nula (2.5).\n", + "else: # Se o valor p não for menor que 0.05.\n", + " print(f\"\\nNão rejeitamos a hipótese nula. Não há evidências suficientes para concluir que a média do GPA dos alunos é diferente de {hipotese_nula}.\") # Se o valor p for maior ou igual a 0.05, não há evidências suficientes para rejeitar a hipótese nula, indicando que a média do GPA da amostra não é significativamente diferente da média populacional definida na hipótese nula (2.5).\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 7. Criar o histograma\n", + "plt.hist(df_colunas['GPA'], bins=10, edgecolor='black') # Cria um histograma da coluna 'GPA' com 10 barras (bins) e bordas pretas.\n", + "plt.xlabel('GPA') # Define o rótulo do eixo X do histograma como 'GPA'.\n", + "plt.ylabel('Frequência') # Define o rótulo do eixo Y do histograma como 'Frequência'.\n", + "plt.title('Histograma do GPA dos Alunos') # Define o título do histograma como 'Histograma do GPA dos Alunos'.\n", + "\n", + "# Adicionar as linhas verticais para a média populacional e a média amostral\n", + "plt.axvline(hipotese_nula, color='red', linestyle='--', label='Média Populacional (2.5)') # Adiciona uma linha vertical vermelha tracejada para a média populacional (2.5) com a legenda 'Média Populacional (2.5)'.\n", + "plt.axvline(df_colunas['GPA'].mean(), color='blue', linestyle='-', label='Média Amostral') # Adiciona uma linha vertical azul sólida para a média amostral com a legenda 'Média Amostral'.\n", + "\n", + "plt.legend() # Mostra a legenda com as informações sobre as linhas verticais.\n", + "plt.show() # Exibe o histograma com as linhas verticais." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TESTE Z**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inscricaoidadesexoconclusao_ens_medtipo_ens_medtreineirocadeira_canhotogestanteidosocelular_na_residencia...pessoas_na_residenciarenda_mensal_familiarnota_ctnota_chnota_lcnota_mtnota_redacaoestadomunicipiomunicipio_id
0121MConcluído100001...7<= 998.00472.9466.0497.3369.1780.0PASANTARÉM1506807
1216FCursando110001...5<= 998.00358.9495.4482.3416.5600.0PAMÃE DO RIO1504059
2318FCursando300001...3998.00 até 1497.00574.2538.7582.6571.5640.0PAPARAGOMINAS1505502
3423MCursando200001...3998.00 até 1497.00NaNNaNNaNNaNNaNPASÃO SEBASTIÃO DA BOA VISTA1507706
4523MConcluído100001...5998.00 até 1497.00NaNNaNNaNNaNNaNPAJURUTI1503903
..................................................................
5095265509526628FConcluído100001...1998.00 até 1497.00NaNNaNNaNNaNNaNBASANTO AMARO2928604
5095266509526725MConcluído100001...2Nenhuma RendaNaNNaNNaNNaNNaNBASALVADOR2927408
5095267509526818FConcluído100001...5Nenhuma RendaNaNNaNNaNNaNNaNBATREMEDAL2931806
5095268509526916MCursando110001...64990.00 até 5988.00458.8380.6493.1396.7800.0BASALVADOR2927408
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idadesexo
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" + ], + "text/plain": [ + " idade sexo\n", + "0 21 M\n", + "1 16 F\n", + "2 18 F\n", + "3 23 M\n", + "4 23 M\n", + "... ... ...\n", + "5095265 28 F\n", + "5095266 25 M\n", + "5095267 18 F\n", + "5095268 16 M\n", + "5095269 25 F\n", + "\n", + "[5095270 rows x 2 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2_colunas = df2[['idade', 'sexo']]\n", + "df2_colunas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TESTE DE HIPÓTESES** \n", + "\n", + "H0: média de idade das mulheres que fizeram enem é 20 anos\n", + "\n", + "H1: média de idade das mulheres que fizeram enem é maior que 20 anos" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idadesexo
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3031822 rows × 2 columns

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" + ], + "text/plain": [ + " idade sexo\n", + "1 16 F\n", + "2 18 F\n", + "6 30 F\n", + "7 26 F\n", + "9 17 F\n", + "... ... ...\n", + "5095262 22 F\n", + "5095264 30 F\n", + "5095265 28 F\n", + "5095267 18 F\n", + "5095269 25 F\n", + "\n", + "[3031822 rows x 2 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mulheres = df2_colunas[df2_colunas[\"sexo\"] == \"F\"]\n", + "df_mulheres" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "22.08743069552742" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2['idade'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([16, 18, 30, ..., 28, 18, 25], dtype=int64)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idades_mulheres = df_mulheres[\"idade\"].values\n", + "idades_mulheres" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "x = len(df_mulheres[df_mulheres[\"idade\"] > 20])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "media_hipotese_nula = 20\n", + "p0= 0.5\n", + "nivel_significancia = 0.05\n", + "n = len(df_mulheres)\n", + "\n", + "# Calcular o desvio padrão da amostra\n", + "desvio_padrao_amostra = np.std(idades_mulheres)\n", + "\n", + "# Usar o desvio padrão da amostra como uma estimativa do desvio padrão populacional\n", + "desvio_padrao_populacional = desvio_padrao_amostra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "não consegui importar as bibliotecas de jeito nenhum, continuei a análise pelo collab, através do link:\n", + "https://colab.research.google.com/drive/1J2sdvMV03euIZY5gx-pe63WBz0eByHqC#scrollTo=lkVLqS3UzOaJ" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'statsmodels'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[22], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mproportion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m proportions_ztest\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels'" + ] + } + ], + "source": [ + "from statsmodels.stats.proportion import proportions_ztest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'proportions_ztest' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[28], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m stat, p_value \u001b[38;5;241m=\u001b[39m \u001b[43mproportions_ztest\u001b[49m(count\u001b[38;5;241m=\u001b[39mx, nobs\u001b[38;5;241m=\u001b[39mn, value\u001b[38;5;241m=\u001b[39mp0, alternative\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlarger\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m stat, p_value\n", + "\u001b[1;31mNameError\u001b[0m: name 'proportions_ztest' is not defined" + ] + } + ], + "source": [ + "stat, p_value = proportions_ztest(count=x, nobs=n, value=p0, alternative='larger')\n", + "stat, p_value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}