{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# **Cabeçalho**\n","\n","Aluno 1 - NUSP - Graduação/Pós - Período\n","\n","Aluno 2 - NUSP - Graduação/Pós - Período"],"metadata":{"id":"94b7gcfPWOpL"}},{"cell_type":"markdown","source":["# Aula 6 - 18/09 - Introdução ao Aprendizado de Máquina e Aprendizado Estatístico\n"],"metadata":{"id":"_ns7mAL7Sqmm"}},{"cell_type":"markdown","source":["## Revisão de Regressão Linear Simples\n","\n","\n","Regressão Linear: Uma Visão Geral\n","\n","A regressão linear é uma técnica estatística que se destina a entender e modelar a relação entre duas ou mais variáveis, em que uma delas é considerada a variável dependente e as demais são variáveis explicativas (ou preditoras).\n","\n","### Componentes Principais da Regressão Linear\n","\n","1. **Variável Dependente (Y):** É a variável que desejamos prever ou explicar. É representada como \\(Y\\) e é o resultado que estamos interessados em analisar.\n","\n","2. **Variáveis Explicativas (X):** São as variáveis que usamos para prever ou explicar a variável dependente. Podemos ter uma ou várias variáveis explicativas, representadas como \\($X_1$, $X_2$, ..., $X_n$\\).\n","\n","3. **Modelo de Regressão Linear:** O objetivo é encontrar um modelo matemático que relaciona as variáveis explicativas $X$ com a variável dependente $Y$. No caso da regressão linear simples, temos o modelo mais simples representado como $Y$ $=$ $\\beta_0$ + $\\beta_1X$ + $\\varepsilon$, onde:\n","\n"," - $\\beta_0$ é o intercepto (valor de $Y$ quando $X$ é zero).\n"," - $\\beta_1$ é a inclinação. Ou seja, a mudança em $Y$ para uma unidade de mudança em $X$.\n"," - $\\varepsilon$ é o erro, ou seja, o termo estocástico.\n","\n","4. **Estimação de Coeficientes (Inclinação e Interceptação):** A regressão linear envolve estimar os coeficientes $\\beta_0$ e $\\beta_1$ que melhor se ajustam aos dados. Isso é feito para minimizar a soma dos quadrados dos erros (ou seja, minimizar a diferença entre os valores observados e os valores previstos).\n","\n","5. **Pressupostos da Regressão Linear:** A regressão linear depende de vários pressupostos, incluindo a linearidade da relação entre as variáveis, independência dos erros, homocedasticidade (variância constante dos erros), normalidade dos erros e grau moderado de multicolinearidade (correlação alta entre variáveis explicativas).\n"],"metadata":{"id":"6jDxBfu_e2xK"}},{"cell_type":"markdown","source":["### Regressão Linear em Python"],"metadata":{"id":"70yEmHLyt3c0"}},{"cell_type":"markdown","source":["A statsmodels é uma biblioteca estatística que fornece ferramentas poderosas para análise de dados, incluindo regressão linear.\n","\n","Passo 1: Importar as bibliotecas\n","\n","Primeiro, você deve importar as bibliotecas necessárias, incluindo numpy e statsmodels."],"metadata":{"id":"9GoPdPOgt51B"}},{"cell_type":"code","source":["import numpy as np\n","import statsmodels.api as sm"],"metadata":{"id":"3l7W0PfduJ1B"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Passo 2: Preparar os dados\n","\n","Prepare seus dados definindo as variáveis explicativas (X) e a variável dependente (Y)."],"metadata":{"id":"hSLcIXgxuK1_"}},{"cell_type":"code","source":["# Dados de exemplo\n","x = np.array([1, 2, 3, 4, 5])\n","y = np.array([2, 4, 5, 4, 5])"],"metadata":{"id":"zFXOZzUFuMlv"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Passo 3: Adicionar uma constante\n","\n","Para realizar uma regressão linear simples utilizando o stats model do python, é importante adicionar uma coluna de uns à matriz X para representar o coeficiente $\\beta$ no modelo. Ele não representa o valor final ainda"],"metadata":{"id":"u8y4Te8WuOpg"}},{"cell_type":"code","source":["X = sm.add_constant(x)"],"metadata":{"id":"OwqXprr7uiyj"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Passo 4: Criar o modelo de regressão\n","\n","Agora, crie o modelo de regressão linear usando sm.OLS(Y, X), onde Y é a variável dependente e X é a matriz de variáveis independentes."],"metadata":{"id":"YuRQzbz4usox"}},{"cell_type":"code","source":["modelo_linear = sm.OLS(y, X).fit()"],"metadata":{"id":"lQcbs4aJutDc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Passo 5: Obter os resultados da regressão\n","\n","Após ajustar o modelo aos dados, você pode obter informações sobre os resultados da regressão usando model.summary()."],"metadata":{"id":"lywvuQMmuwkv"}},{"cell_type":"code","source":["resultado = modelo_linear.summary()"],"metadata":{"id":"aliUXrzXuxCA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Passo 6: Analisar os resultados\n","\n","A tabela de resultados (results) fornecerá informações detalhadas sobre a regressão linear, incluindo coeficientes, estatísticas de ajuste, valores p e muito mais. Você pode examinar os coeficientes para entender a relação entre as variáveis explicativas e a variável dependente."],"metadata":{"id":"_Y0zvNOruyJf"}},{"cell_type":"code","source":["print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q_bNDkQeu169","executionInfo":{"status":"ok","timestamp":1695059300873,"user_tz":180,"elapsed":282,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"17bee242-563d-49f5-b952-526989bc53a1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: y R-squared: 0.600\n","Model: OLS Adj. R-squared: 0.467\n","Method: Least Squares F-statistic: 4.500\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 0.124\n","Time: 17:48:14 Log-Likelihood: -5.2598\n","No. Observations: 5 AIC: 14.52\n","Df Residuals: 3 BIC: 13.74\n","Df Model: 1 \n","Covariance Type: nonrobust \n","==============================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","------------------------------------------------------------------------------\n","const 2.2000 0.938 2.345 0.101 -0.785 5.185\n","x1 0.6000 0.283 2.121 0.124 -0.300 1.500\n","==============================================================================\n","Omnibus: nan Durbin-Watson: 2.017\n","Prob(Omnibus): nan Jarque-Bera (JB): 0.570\n","Skew: 0.289 Prob(JB): 0.752\n","Kurtosis: 1.450 Cond. No. 8.37\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"]}]},{"cell_type":"markdown","source":["#### P-valor\n","\n","Nos resultados da regressão, temos o p-valor, representado pela coluna (P>|t|). O valor p, ou p-value, é um conceito fundamental na estatística inferencial e desempenha um papel crucial na interpretação de resultados de testes de hipóteses estatísticas. Ele é usado para avaliar a evidência estatística contra uma hipótese nula e ajuda a determinar se os resultados de um estudo são estatisticamente significativos.\n","\n","Aqui estão alguns pontos importantes sobre o valor p:\n","\n","* Definição: O valor p é a probabilidade de observar resultados tão extremos ou mais extremos do que os obtidos em um estudo, assumindo que a hipótese nula seja verdadeira. Em outras palavras, ele quantifica a probabilidade de que os resultados sejam devidos ao acaso.\n","\n","* Hipótese nula e alternativa: Em um teste de hipóteses, você tem duas hipóteses: a hipótese nula (geralmente denotada como H0) e a hipótese alternativa (geralmente denotada como Ha). A hipótese nula representa uma afirmação de que não há efeito ou diferença real, enquanto a hipótese alternativa sugere que há um efeito ou diferença real.\n","\n","* Interpretação: Após conduzir um teste estatístico, você obtém um valor p. Se o valor p for baixo (geralmente abaixo de um limiar pré-determinado, chamado de nível de significância), isso sugere que os resultados são estatisticamente significativos e que você pode rejeitar a hipótese nula em favor da hipótese alternativa. Por outro lado, se o valor p for alto, você não tem evidência estatística suficiente para rejeitar a hipótese nula.\n","\n","* Nível de significância: O nível de significância é o limite ou ponto de corte escolhido para determinar quando rejeitar a hipótese nula. Um valor p menor do que o nível de significância escolhido (geralmente definido em 0,05 ou 0,01) leva à rejeição da hipótese nula.\n","\n","* Limitações: É importante lembrar que o valor p não quantifica a força ou a magnitude do efeito observado; ele apenas indica a probabilidade de obter tais resultados sob a hipótese nula. Além disso, o valor p não pode provar que a hipótese nula seja verdadeira; ele só pode indicar se os resultados são consistentes ou inconsistentes com a hipótese nula.\n","\n","* Contexto: A interpretação do valor p deve sempre ser feita em conjunto com outras informações relevantes, como o tamanho da amostra, a relevância do estudo e a qualidade dos dados. Não deve ser usado como a única métrica para se tomar decisões importantes."],"metadata":{"id":"TOgoHIqFEiGB"}},{"cell_type":"markdown","source":["No caso da regressão acima, o coeficiente de X não parece ser estatisticamente significante. Uma informação complementar para observar isso pode ser o de analisar as últimas duas colunas da regressão, que mostram o intervalo de confiança de 95\\% dos coeficientes. Nos resultados, podemos ver que X está estimado entre -0.300 e 1.500, passando por zero. Portanto, podemos concluir que ele não estatisticamente significante."],"metadata":{"id":"clL_K6YZFCMP"}},{"cell_type":"markdown","source":["Vamos refazer os mesmos passos com dados mais interessantes:"],"metadata":{"id":"hLXB3QqnvqxO"}},{"cell_type":"code","source":["# Parâmetros da distribuição normal\n","media = 50 # Média\n","desvio_padrao = 2 # Desvio padrão\n","\n","# Gerar dados com distribuição normal\n","tamanho_amostra = 100 # Tamanho da amostra desejada\n","X = np.random.normal(media, desvio_padrao, tamanho_amostra)"],"metadata":{"id":"Eu_NF4a0vzcZ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Exercício 1 - Visualize X utilizando o seaborn"],"metadata":{"id":"6aF3LKZ7wii5"}},{"cell_type":"code","source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","import matplotlib as mpl\n","\n","\n","# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(X, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('X')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição de X')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"ir3aLdI-wloO","executionInfo":{"status":"ok","timestamp":1695059332110,"user_tz":180,"elapsed":602,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"b71461fb-f75a-480f-9e58-9e134f78c39f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 2 - Com base na simulação de X, a distribuição observada no exercício 1 faz sentido? Comente sobre."],"metadata":{"id":"L5q118UkxvST"}},{"cell_type":"markdown","source":["*Escreva sua resposta aqui:*"],"metadata":{"id":"fpUQ6o_dyrgK"}},{"cell_type":"markdown","source":["RESPOSTA:\n","\n","Criamos X como uma distribuição normal de média 50 e de desvio padrão igual a 2. Isso significa que a maioria dos valores de X deve estar próxima de 50, com um desvio padrão de 2 que controla a dispersão dos valores, fazendo com que a maior parte das observações se encontre entre 46 e 54"],"metadata":{"id":"WGBAlf491YZW"}},{"cell_type":"code","source":["import pandas as pd\n","\n","\n","pd.Series(X).describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q8JLJ3fB3RSj","executionInfo":{"status":"ok","timestamp":1695059365794,"user_tz":180,"elapsed":409,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"3c336865-2687-4c0e-b9f6-c04874141de9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 100.000000\n","mean 50.164478\n","std 1.941466\n","min 45.512693\n","25% 48.820568\n","50% 50.224089\n","75% 51.307918\n","max 56.242544\n","dtype: float64"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["Geramos o X, agora, vamos gerar Y como uma função de X. Para isso precisamos gerar os dados tanto de X como do erro."],"metadata":{"id":"CLNcY3TFv0_d"}},{"cell_type":"code","source":["# Gerar Y usando a função linear\n","erro = np.random.normal(0, 1, tamanho_amostra) # Erro aleatório\n","\n","\n","# Processo gerador de Y\n","Y = 25 + 2 * X + erro\n","\n","\n","len(Y)"],"metadata":{"id":"X-gtwj8Qv5xP","executionInfo":{"status":"ok","timestamp":1695059389785,"user_tz":180,"elapsed":348,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9cd7d6f8-4e16-4e9e-9ca0-90fb43d7ad7c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["100"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["Exercício 3 - Qual é o valor esperado de Y quando X é 0 (Desconsidere o erro)?"],"metadata":{"id":"9sjYQ47zdbGw"}},{"cell_type":"markdown","source":["*Escreva sua resposta aqui*:"],"metadata":{"id":"p4nrbMJghPHb"}},{"cell_type":"markdown","source":["**Resposta:** O valor esperado é de 25 (+ o erro)"],"metadata":{"id":"D8JqsWFKdkn3"}},{"cell_type":"markdown","source":["Exercício 4 - Transforme Y em uma series e utilize o comando describe. O que as informações nos dizem sobre essa variável?"],"metadata":{"id":"JcuO-7Ljegz0"}},{"cell_type":"code","source":["pd.Series(Y).describe()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wJul_l3ge3YQ","executionInfo":{"status":"ok","timestamp":1695059414648,"user_tz":180,"elapsed":256,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"958f4fb7-e7cf-4ef6-d4fa-19b0898e4fb9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 100.000000\n","mean 125.404909\n","std 4.053841\n","min 116.039692\n","25% 122.603129\n","50% 125.401384\n","75% 128.059393\n","max 138.067240\n","dtype: float64"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","source":["Exercício 5 - Pense no processo gerador de Y (como ele foi gerado), os valores encontrados no describe fazem sentido?"],"metadata":{"id":"g5os_-pUfRyS"}},{"cell_type":"markdown","source":["*Escreva sua resposta aqui*:"],"metadata":{"id":"jRFD9xnGhXi4"}},{"cell_type":"markdown","source":["Resposta: Sim, Geramos o Y com o intercepto de 25, mais $2*X$ (X este que tem média de 50), e obtemos um Y com média de 125.2, bem próximo do esperado de $25 + 2*50 + erro$. Além disso, como X e o erro foram gerados com base em uma distribuição normal, Y também parece ter o formato de uma distribuição normal."],"metadata":{"id":"6EXeeaIdflWa"}},{"cell_type":"markdown","source":["Exercício 6 - Visualize a distribuição de Y, ela faz sentido?"],"metadata":{"id":"0lvvBTrmf_xA"}},{"cell_type":"code","source":["# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(Y, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Y')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição de Y')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"aoa9Ck-SgFEf","executionInfo":{"status":"ok","timestamp":1695036530288,"user_tz":180,"elapsed":973,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"db17974c-9fa6-4ff3-95bf-36869d8e0d3b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Resposta: Como dito no item anterior, tanto X quanto o erro, variáveis componentes do processo gerador de Y, foram geradas com base em uma distribuição normal. Portanto, Y também terá uma distribuição normal, que é o que visualizamos aqui: Sua média se encontra 125 e a distribuição dos valores parece ser a de uma distribuição normal."],"metadata":{"id":"BDeHFUGphd_P"}},{"cell_type":"markdown","source":["Agora, vamos rodar uma regressão utilizando somente X e Y:"],"metadata":{"id":"T1N1n2Alh39o"}},{"cell_type":"code","source":["# Salvando o valor de X em outro objeto para adicionar a constante\n","\n","Xreg = X\n","\n","\n","# Adicionando a constante de X\n","Xreg = sm.add_constant(Xreg)\n","\n","# Rodando a regressão linear\n","modelo_linear = sm.OLS(Y, Xreg).fit()\n","\n","\n","# Salvando o resumo da regressão\n","\n","resultado = modelo_linear.summary()\n","\n","\n","# Imprimindo os resultados da regressão\n","\n","print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GswdnUrgh7ZB","executionInfo":{"status":"ok","timestamp":1695059460217,"user_tz":180,"elapsed":260,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"9d971d01-3a40-4bd3-82e6-6a1c437d27c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: y R-squared: 0.927\n","Model: OLS Adj. R-squared: 0.926\n","Method: Least Squares F-statistic: 1240.\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 2.00e-57\n","Time: 17:50:59 Log-Likelihood: -150.68\n","No. Observations: 100 AIC: 305.4\n","Df Residuals: 98 BIC: 310.6\n","Df Model: 1 \n","Covariance Type: nonrobust \n","==============================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","------------------------------------------------------------------------------\n","const 24.5700 2.866 8.573 0.000 18.882 30.258\n","x1 2.0101 0.057 35.208 0.000 1.897 2.123\n","==============================================================================\n","Omnibus: 0.342 Durbin-Watson: 2.057\n","Prob(Omnibus): 0.843 Jarque-Bera (JB): 0.380\n","Skew: -0.135 Prob(JB): 0.827\n","Kurtosis: 2.864 Cond. No. 1.31e+03\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n","[2] The condition number is large, 1.31e+03. This might indicate that there are\n","strong multicollinearity or other numerical problems.\n"]}]},{"cell_type":"markdown","source":["Exercício 7 - Os valores dos coeficientes (Tanto o intercepto quanto o de X)fazem sentido com base no que vimos anteriormente do processo gerador de Y? Interprete o significado do coeficiente de X."],"metadata":{"id":"NoX5Z1Bdik14"}},{"cell_type":"markdown","source":["*Escreva sua resposta aqui*:"],"metadata":{"id":"Akqm3LVki46x"}},{"cell_type":"markdown","source":["Resposta: Sim, os valores são muito parecidos com o que simulamos no processo gerador de Y: o intercepto é de, aproximadamente, 25,35 (bem próximo ao valor real de 25) e X tem um $\\beta$ de aproximadamente 2, como foi estabelecido na simulação. O coeficiente de X indica que a cada aumento de uma unidade de X, Y aumentará em 2."],"metadata":{"id":"ff5N-8pdi7X0"}},{"cell_type":"markdown","source":["Exercício 8 - Tente pensar um pouco sobre por que os valores não são exatamente iguais aos que definimos no processo gerador de Y"],"metadata":{"id":"0E4pSvIhja1q"}},{"cell_type":"markdown","source":["*Escreva sua resposta aqui*:"],"metadata":{"id":"cJGWueI9jjU5"}},{"cell_type":"markdown","source":["Resposta: O erro faz com que os valores não sejam exatamente iguais aos que esperávamos. Em uma situação real de pesquisa, esperamos que as variações nos valores de Y sejam também determinados por aleatoriedade."],"metadata":{"id":"dseeK6m-j6zl"}},{"cell_type":"markdown","source":["Exercício 9 - Faça um scatterplot de X e Y e interprete o gráfico."],"metadata":{"id":"T_1GrA4XjUSp"}},{"cell_type":"code","source":["sns.scatterplot(x=X, y=Y)\n","plt.ylabel('Y')\n","plt.xlabel(\"X\")\n","plt.title(\"Relação de X e Y\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"YgtVZCmlkqNu","executionInfo":{"status":"ok","timestamp":1695036547406,"user_tz":180,"elapsed":1093,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"595fe865-0834-4f71-c583-7804c3ffbe48"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Relação de X e Y')"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 10 - Refaça o plot utilizando o regplot do seaborn. O que a linha indica?"],"metadata":{"id":"u-O94gpamSAF"}},{"cell_type":"code","source":["sns.regplot(x=X, y=Y)\n","plt.ylabel('Y')\n","plt.xlabel(\"X\")\n","plt.title(\"Relação de X e Y\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"GgLegp5OmWVb","executionInfo":{"status":"ok","timestamp":1695036551759,"user_tz":180,"elapsed":1682,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"43b198ad-aa7f-4cae-b9c6-8ca212718c3c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Relação de X e Y')"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 11 - Por fim, interprete a significância estatística de X com base no p-valor e no intervalo de confiança do seu coeficiente."],"metadata":{"id":"2KX3TDdKGFpP"}},{"cell_type":"markdown","source":["Resposta: Ambos indicam que X é estatisticamente significante, como era de esperar com base no processo gerador de Y."],"metadata":{"id":"yh_iYszuGNOu"}},{"cell_type":"markdown","source":["### Regressão com uma variável dummy (ou binária)"],"metadata":{"id":"GXsqCI6nmdvX"}},{"cell_type":"markdown","source":["Uma variável dummy, também conhecida como variável indicadora ou variável binária, é uma variável categórica que é usada em análises estatísticas e regressões para representar categorias ou grupos distintos. Essa variável é frequentemente codificada como 0 ou 1, onde 0 indica a ausência da categoria e 1 indica a presença da categoria. As regressões com uma variável dummy são úteis quando você deseja incorporar variáveis categóricas em modelos de regressão, como regressão linear ou regressão logística, que normalmente funcionam com variáveis numéricas."],"metadata":{"id":"KhcrUTeonjUJ"}},{"cell_type":"markdown","source":["Vamos começar criando uma variável dummy para representar o gênero de determinadas pessoas"],"metadata":{"id":"vgjp_IwtoGxv"}},{"cell_type":"code","source":["import random\n","\n","# Garantir estabilidade nos resultados\n","random.seed(42)\n","\n","# Crie uma lista vazia para armazenar os valores da variável dummy\n","genero_dummy = []\n","\n","# Gere 1000 valores aleatórios (0 ou 1) para a variável dummy\n","for _ in range(1000):\n"," valor = random.choice([0, 1])\n"," genero_dummy.append(valor)\n","\n","# Exiba a lista da variável dummy\n","print(genero_dummy)\n","\n","# Transformando esta lista em numpy.array\n","\n","genero_dummy = np.array(genero_dummy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0v7CYax5oIOT","executionInfo":{"status":"ok","timestamp":1695059791739,"user_tz":180,"elapsed":407,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"10360051-016d-4ba0-dca0-a0c6e3a33fe8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1]\n"]}]},{"cell_type":"markdown","source":["Vamos visualizar a nova variável criada:"],"metadata":{"id":"qSuRfE4Eo4jg"}},{"cell_type":"code","source":["# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(genero_dummy, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Gênero')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição dos Gêneros no Banco')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"W60awSgco7mk","executionInfo":{"status":"ok","timestamp":1695060007999,"user_tz":180,"elapsed":569,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"8c9f5c5e-a16e-46dc-d46b-c09dc8c9be60"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 1 - Qual a principal diferença da distribuição desta variável para as variáveis criadas anteriormente?"],"metadata":{"id":"e0Dn7F5vpECc"}},{"cell_type":"markdown","source":["Resposta: Primeiramente, não temos mais uma distribuição normal. A nova variável tem o formato de uma distribuição bimodal, com um pouco de assimetria entre as classes (bem pouca)."],"metadata":{"id":"0ZxYdHJDpLZE"}},{"cell_type":"markdown","source":["Agora, vamos gerar um Y em função deste novo X (genero_dummy)"],"metadata":{"id":"IyIkTjQRqfu8"}},{"cell_type":"code","source":["# Precisamos de um novo erro com 1000 observações\n","tamanho_amostra = 1000\n","\n","erro = np.random.normal(0, 1, tamanho_amostra) # Erro aleatório\n","\n","# Gerando Y2\n","\n","Y2 = 1200 + 1500*genero_dummy + erro"],"metadata":{"id":"wp8ny3nAqktL"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Vamos agora visualizar a distribuição de Y2"],"metadata":{"id":"bIotJHLhrgjh"}},{"cell_type":"code","source":["# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(Y2, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Y2')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição de Y2')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"WMtq0Lmxqkx4","executionInfo":{"status":"ok","timestamp":1695060013213,"user_tz":180,"elapsed":729,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"22236e26-724d-4ed5-bba1-b6fd1e469e51"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 2 - Esta distribuição faz sentido (Novamente, pense no processo gerador)?"],"metadata":{"id":"hvMdEC4Mr2uM"}},{"cell_type":"markdown","source":["*Insira sua resposta aqui*"],"metadata":{"id":"mgfd9sI-sRSL"}},{"cell_type":"markdown","source":["Resposta: A distribuição de Y2 será influenciada principalmente pelo termo 1500*genero_dummy. Aqui, podemos esperar que a distribuição de Y2 seja bimodal, com dois picos separados representando as duas categorias da variável dummy (0 e 1).\n","\n","Portanto, neste cenário, a distribuição de Y2 será afetada principalmente pela variável dummy, resultando em uma distribuição bimodal em que você terá duas concentrações distintas de valores, uma para cada categoria da variável dummy. Isso é bastante diferente de uma distribuição normal, e a presença da variável dummy com um coeficiente tão grande é uma influência dominante na distribuição de Y2."],"metadata":{"id":"-UpKO7VPsS2c"}},{"cell_type":"markdown","source":["Exercício 3 - O que representa o valor de 1200 no intercepto?"],"metadata":{"id":"Gzbv-Y7vsdSc"}},{"cell_type":"markdown","source":["Resposta: Representa o valor esperado de Y2 quando a variável de gênero for igual a 0"],"metadata":{"id":"gXufDdAPshsv"}},{"cell_type":"markdown","source":["Vamos rodar uma nova regressão com esses dados"],"metadata":{"id":"8XFuJ75dslvT"}},{"cell_type":"code","source":["# Criando um novo array com o coeficiente de 1\n","\n","coef_genero = sm.add_constant(genero_dummy)\n","\n","# Encaixando o modelo linear nas variáveis\n","\n","modelo_linear = sm.OLS(Y2, coef_genero).fit()\n","\n","# Pegando a tabela de resultados\n","resultado = modelo_linear.summary()\n","\n","# Imprimindo a regressão\n","\n","print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rTRfLOBUs4if","executionInfo":{"status":"ok","timestamp":1695060016060,"user_tz":180,"elapsed":396,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"0782f9d6-c0f4-477e-9c76-e62e42610b7c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: y R-squared: 1.000\n","Model: OLS Adj. R-squared: 1.000\n","Method: Least Squares F-statistic: 5.910e+08\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 0.00\n","Time: 18:00:15 Log-Likelihood: -1392.9\n","No. Observations: 1000 AIC: 2790.\n","Df Residuals: 998 BIC: 2800.\n","Df Model: 1 \n","Covariance Type: nonrobust \n","==============================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","------------------------------------------------------------------------------\n","const 1199.9478 0.044 2.71e+04 0.000 1199.861 1200.035\n","x1 1500.0733 0.062 2.43e+04 0.000 1499.952 1500.194\n","==============================================================================\n","Omnibus: 4.262 Durbin-Watson: 1.843\n","Prob(Omnibus): 0.119 Jarque-Bera (JB): 3.928\n","Skew: 0.100 Prob(JB): 0.140\n","Kurtosis: 2.766 Cond. No. 2.65\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"]}]},{"cell_type":"markdown","source":["Exercício 4 - Imagine que a variável de gênero seja 1 quando é alguém do gênero masculino e 0 quando é alguém do gênero feminino. Além disso, imagine que Y2 representa o salário do indivíduo. O que representa o coeficiente da variável de gênero?"],"metadata":{"id":"Xs8251lJt55c"}},{"cell_type":"markdown","source":["*Insira sua resposta aqui*"],"metadata":{"id":"V16hs4VmuKPR"}},{"cell_type":"markdown","source":["Resposta: O coeficiente indica que há uma diferença de salário de R\\$ 1500,00 entre as pessoas do gênero masculino e as do gênero feminino. A média de salários quando gênero é igual a 0 é de R\\$1200 (aproximadamente), e quando o gênero é igual a 1, a média dos salários é igual a 1200 + 1500 = R\\$2700,00."],"metadata":{"id":"OSCeYsZGuMaS"}},{"cell_type":"markdown","source":["Exercício 5 - Esse resultado faz sentido com o que simulamos no processo gerador da variável Y2?"],"metadata":{"id":"nnXnG6m2um3p"}},{"cell_type":"markdown","source":["*Insira sua resposta aqui*: O resultado está de acordo com o que simulamos no processo gerador de Y2. Foi estabelecido um intercepto de 1200, e simulamos que o impacto da variável de gênero era igual a 1500*gênero. Por fim, adicionamos um termo de erro de média 0, ou seja, quase aleatório e de pouca influência nos resultados. Portanto, o resultado encontrado na regressão é extremamente similar ao simulado."],"metadata":{"id":"X-yli3kAusLC"}},{"cell_type":"markdown","source":["Exercício 6 - Faça um gráfico de barras da distribuição de salários por gênero. Não esqueça de concatenar as duas variáveis em um dataframe."],"metadata":{"id":"IDo1MkGYvQMB"}},{"cell_type":"code","source":["y2serie = pd.Series(Y2)\n","genserie = pd.Series(genero_dummy)\n","\n","\n","# Criando o banco de dados\n","dados = pd.concat([y2serie, genserie], # Concatenando as séries\n"," axis = 1, # Axis = colunas\n"," keys = ['Salário', 'Genero_dummy']) # Nome das colunas\n","\n","# Dica - Criando uma nova coluna com base na variável dummy\n","dados['Genero'] = dados['Genero_dummy'].apply(lambda x: 'Masculino' if x == 1 else 'Feminino')\n","\n","\n","# Gráfico\n","sns.barplot(data=dados, y=\"Salário\", x=\"Genero\", palette = 'Set2')\n","plt.xlabel(\"Gênero\")\n","plt.title(\"Diferença de Salário entre os Gêneros\")\n","\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"trHr_2-2vYa4","executionInfo":{"status":"ok","timestamp":1695060184394,"user_tz":180,"elapsed":463,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"44c7fd46-3e04-4905-ecb1-98f5c8a88d5b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Diferença de Salário entre os Gêneros')"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 7 - Por fim, interprete o P-valor de X e o intervalo de confiança de seu coeficiente."],"metadata":{"id":"7LIFRNSDLdKM"}},{"cell_type":"markdown","source":["Resposta: Ambos indicam que o coeficiente de X é estatisticamente significante e diferente de zero."],"metadata":{"id":"hO2mS3dDLjbE"}},{"cell_type":"markdown","source":["### Regressão multivariada\n","\n","A regressão multivariada é uma extensão da regressão linear simples que lida com múltiplas variáveis independentes (também conhecidas como preditoras ou explicativas) em relação a uma única variável dependente (também chamada de variável de resposta). Essa técnica é usada para modelar a relação entre as variáveis explicativas e a variável dependente, com o objetivo de entender como as variáveis independentes afetam a variável de resposta e fazer previsões ou inferências com base nessa relação.\n","\n","## Conceitos-Chave\n","\n","- **Variáveis Dependentes e Explicativas**: Na regressão multivariada, você tem uma única variável dependente (Y) e duas ou mais variáveis explicativas (X1, X2, X3, ...). O objetivo é encontrar uma equação que descreva como as variáveis explicativas estão relacionadas à variável dependente.\n","\n","- **Modelo de Regressão**: O modelo de regressão multivariada assume uma forma geral semelhante à da regressão linear simples.\n","\n","\n","\n","Onde:\n"," - Y é a variável dependente.\n"," - X1, X2, X3, ... são as variáveis explicativas.\n"," - β0, β1, β2, β3, ... são os coeficientes a serem estimados.\n"," - ε é o erro aleatório, que representa a variação não explicada pelo modelo.\n","\n","- **Estimação de Parâmetros**: O objetivo é estimar os coeficientes (β0, β1, β2, β3, ...) que minimizam a soma dos quadrados dos resíduos, ou seja, a diferença entre os valores observados e os valores previstos pelo modelo.\n","\n","- **Interpretação dos Coeficientes**: Cada coeficiente estimado (β) representa a mudança esperada na variável dependente Y para uma mudança unitária na variável independente correspondente, mantendo todas as outras variáveis constantes.\n","\n","- **Pressupostos da Regressão Multivariada**: A regressão multivariada faz várias suposições, incluindo a linearidade da relação entre as variáveis, a independência dos erros, a homocedasticidade (variância constante dos erros), entre outras.\n","\n","- **Análise de Resíduos**: A análise de resíduos é fundamental para verificar se as suposições do modelo são atendidas e para identificar possíveis problemas, como multicolinearidade (alta correlação entre variáveis independentes) ou heterocedasticidade (variância variável dos erros).\n","\n","- **Tipos Específicos de Regressão Multivariada**: Existem várias técnicas específicas de regressão multivariada, incluindo a regressão linear múltipla, regressão logística (para variáveis de resposta binárias), regressão de Poisson (para dados de contagem), regressão de séries temporais (para dados temporais), entre outras.\n","\n"],"metadata":{"id":"giuw-Z9FyZfT"}},{"cell_type":"markdown","source":["Vamos voltar às variáveis de Gênero e Salário. No entanto, agora vamos adicionar outras variáveis que suspeitamos ter correlação com o salário, e refazer o processo gerador de Y."],"metadata":{"id":"Vo3Myn-v7Lvo"}},{"cell_type":"markdown","source":["#### Anos de estudo"],"metadata":{"id":"_cedybB-9-NM"}},{"cell_type":"code","source":["## Anos de estudo\n","\n","# Defina os parâmetros da distribuição\n","media_anos_estudo = 12 # Média de anos de estudo\n","desvio_padrao = 4 # Desvio padrão dos anos de estudo\n","tamanho_amostra = 1000 # Tamanho da amostra\n","\n","# Gere a variável de anos de estudo\n","anos_estudo = np.random.normal(loc=media_anos_estudo, scale=desvio_padrao, size=tamanho_amostra)\n","\n","# Arredonde os valores para garantir que sejam inteiros\n","anos_estudo = np.round(anos_estudo).astype(int)\n","\n","\n","# Visualizando a distribuição\n","\n","# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(anos_estudo, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Anos de Estudo')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição dos Anos de Estudo')\n","plt.show()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"TOSg3rmq9agG","executionInfo":{"status":"ok","timestamp":1695060271831,"user_tz":180,"elapsed":550,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"11f9049d-9446-4de6-884f-a8f68855b4fc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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3LgxwsPDceTIEc1w60c++ugjbNmyBSEhIZgyZQrkcjl++ukn3LlzB7/++isMDJ78t1Tz5s0xdOhQrFq1CllZWejQoQOOHj2K2NjYMud+8cUXmvldJkyYAENDQ/zwww8oKirC4sWLNec1btwYXbt2RatWrSCXy3HhwgXs3LkTkyZNemIdZ8+eRWxs7BPPqVevHlq2bIlNmzZh9uzZT3tpq7VuAJg1axY2btyI3r17Y+rUqZph6Z6enlp9gKysrLB69Wq89dZbaNmyJYYMGQIHBwcoFAr88ccf6NixI77//vtnPp+//vqr3HmJAgMDNUPRX+TzUtH39bhx4/D9999j5MiRuHjxIlxcXLBx40bNdAmPGBkZYdGiRRgzZgxeeuklDB06VDMs3cvLC9OnT3/mcyY9JOoYMaIq8GhY+qPN2NhYcHZ2Fl5++WXh22+/1Rr6/cjjw9KPHj0q9O/fX3B1dRWMjY0FV1dXYejQocLNmze17rd3716hcePGgqGhodaQ25deeklo0qRJufU9aVj6li1bhDlz5giOjo6Cqamp0Ldv3zJDyAVBEJYsWSLUq1dPkMlkQseOHYULFy5UaFi6IAjCtWvXhNdee02wsrISAAiNGjUSPvvsM83xhw8fCmPGjBHs7e0FCwsLITg4WLhx40a5w4bj4uKE119/XbCxsRFMTEyEtm3bCvv27Sv3OT+uoKBAmDJlimBnZyeYm5sL/fr1E+7du1dmWLogCMKlS5eE4OBgwcLCQjAzMxO6desm/P3331rnfPHFF0Lbtm0FGxsbwdTUVPDz8xO+/PJLobi4+Ik1TJ48WQCgNXT8cfPnzxcACFeuXBEEoXRY+sSJE8ucV97rU1V1PxIZGSm89NJLgomJiVCvXj3h888/F9auXas1LP2RsLAwITg4WLC2thZMTEwEHx8fYfTo0cKFCxee+hjPGpb+6P/Vi35eBKFi72tBEIT4+Hjh1VdfFczMzAR7e3th6tSpwsGDB7WGpT+ybds2oUWLFoJMJhPkcrkwfPhw4f79+898bUk/SQShEj3tiKjW69mzJ2bNmoVevXqJXQoRUZVjHx6iOqpfv35ay2sQEekz9uEhqmO2bNmCvLw87NixA46OjmKXQ0RULXiFh6iOiYqKwqRJk/DgwQN8+OGHYpdDRFQt2IeHiIiI9B6v8BAREZHeY+AhIiIivcdOyyhdbyYhIQGWlpY6Xy6AiIiIqoYgCMjJyYGrq+tTJzwFGHgAAAkJCVpr2xAREVHtce/ePbi5uT31HAYeAJaWlgBKX7BHa9wQERFRzZadnQ13d3fNv+NPw8ADaL7GsrKyYuAhIiKqZSrSHYWdlomIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSeww8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Q7ELICKqDIVCgbS0NJ23a29vDw8PD523S0TiYuAholpHoVDAz98fBfn5Om/b1MwMN6KjGXqI9IyogSc0NBS7du3CjRs3YGpqig4dOmDRokVo1KiR5pzCwkJ88MEH2Lp1K4qKihAcHIxVq1bByclJc45CocD777+PsLAwWFhYYNSoUQgNDYWhIfMckT5KS0tDQX4+hs/+Bk4ePjprN1kRh02LZiItLY2Bh0jPiJoITpw4gYkTJ6JNmzYoKSnBxx9/jF69euH69eswNzcHAEyfPh1//PEHduzYAWtra0yaNAkDBw7E6dOnAQAqlQp9+/aFs7Mz/v77byQmJmLkyJEwMjLCV199JebTI6Iq5uThA7cGTcQug4hqAVEDz8GDB7Vub9iwAY6Ojrh48SK6dOmCrKwsrF27Fps3b0b37t0BAOvXr4e/vz/OnDmDdu3a4dChQ7h+/TqOHDkCJycnNG/eHJ9//jlmz56N+fPnw9jYWIynRkRERDVIjRqllZWVBQCQy+UAgIsXL0KpVKJnz56ac/z8/ODh4YHw8HAAQHh4OAICArS+4goODkZ2djaioqLKfZyioiJkZ2drbURERKS/akzgUavVmDZtGjp27IimTZsCAJKSkmBsbAwbGxutc52cnJCUlKQ5599h59HxR8fKExoaCmtra83m7u6u42dDRERENUmNCTwTJ07EtWvXsHXr1ip/rDlz5iArK0uz3bt3r8ofk4iIiMRTI4YxTZo0Cfv27cPJkyfh5uam2e/s7Izi4mJkZmZqXeVJTk6Gs7Oz5pxz585ptZecnKw5Vh6ZTAaZTKbjZ0FEREQ1lahXeARBwKRJk7B7924cO3YM3t7eWsdbtWoFIyMjHD16VLMvJiYGCoUC7du3BwC0b98eV69eRUpKiuacw4cPw8rKCo0bN66eJ0JEREQ1mqhXeCZOnIjNmzdj7969sLS01PS5sba2hqmpKaytrTF27FjMmDEDcrkcVlZWmDx5Mtq3b4927doBAHr16oXGjRvjrbfewuLFi5GUlIRPP/0UEydO5FUcIiIiAiBy4Fm9ejUAoGvXrlr7169fj9GjRwMAli1bBgMDAwwaNEhr4sFHpFIp9u3bh/fffx/t27eHubk5Ro0ahYULF1bX0yAiIqIaTtTAIwjCM88xMTHBypUrsXLlyiee4+npif379+uyNCIiItIjNWaUFhEREVFVYeAhIiIivcfAQ0RERHqPgYeIiIj0HgMPERER6T0GHiIiItJ7DDxERESk9xh4iIiISO8x8BAREZHeY+AhIiIivcfAQ0RERHqPgYeIiIj0HgMPERER6T0GHiIiItJ7DDxERESk9xh4iIiISO8x8BAREZHeY+AhIiIivcfAQ0RERHqPgYeIiIj0HgMPERER6T0GHiIiItJ7DDxERESk9xh4iIiISO8x8BAREZHeY+AhIiIivcfAQ0RERHqPgYeIiIj0HgMPERER6T0GHiIiItJ7DDxERESk9xh4iIiISO+JHnhOnjyJfv36wdXVFRKJBHv27NE6LpFIyt2++eYbzTleXl5ljn/99dfV/EyIiIiophI98OTl5aFZs2ZYuXJluccTExO1tnXr1kEikWDQoEFa5y1cuFDrvMmTJ1dH+URERFQLGIpdQEhICEJCQp543NnZWev23r170a1bN9SvX19rv6WlZZlziYiIiIAacIXneSQnJ+OPP/7A2LFjyxz7+uuvYWdnhxYtWuCbb75BSUnJE9spKipCdna21kZERET6S/QrPM/jp59+gqWlJQYOHKi1f8qUKWjZsiXkcjn+/vtvzJkzB4mJiVi6dGm57YSGhmLBggXVUTIRERHVALUq8Kxbtw7Dhw+HiYmJ1v4ZM2Zofg4MDISxsTHeffddhIaGQiaTlWlnzpw5WvfJzs6Gu7t71RVOREREoqo1geevv/5CTEwMtm3b9sxzg4KCUFJSgrt376JRo0ZljstksnKDEBEREemnWtOHZ+3atWjVqhWaNWv2zHMjIiJgYGAAR0fHaqiMiIiIajrRr/Dk5uYiNjZWc/vOnTuIiIiAXC6Hh4cHgNKvnHbs2IElS5aUuX94eDjOnj2Lbt26wdLSEuHh4Zg+fTpGjBgBW1vbanseREREVHOJHnguXLiAbt26aW4/6lszatQobNiwAQCwdetWCIKAoUOHlrm/TCbD1q1bMX/+fBQVFcHb2xvTp0/X6qNDREREdZvogadr164QBOGp54wfPx7jx48v91jLli1x5syZqiiNiIiI9ESt6cNDREREVFkMPERERKT3GHiIiIhI7zHwEBERkd5j4CEiIiK9x8BDREREeo+Bh4iIiPQeAw8RERHpPQYeIiIi0nsMPERERKT3GHiIiIhI7zHwEBERkd5j4CEiIiK9x8BDREREeo+Bh4iIiPQeAw8RERHpPQYeIiIi0nsMPERERKT3GHiIiIhI7zHwEBERkd5j4CEiIiK9x8BDREREeo+Bh4iIiPQeAw8RERHpPQYeIiIi0nsMPERERKT3GHiIiIhI7zHwEBERkd5j4CEiIiK9x8BDREREeo+Bh4iIiPQeAw8RERHpPdEDz8mTJ9GvXz+4urpCIpFgz549WsdHjx4NiUSitfXu3VvrnIyMDAwfPhxWVlawsbHB2LFjkZubW43PgoiIiGoy0QNPXl4emjVrhpUrVz7xnN69eyMxMVGzbdmyRev48OHDERUVhcOHD2Pfvn04efIkxo8fX9WlExERUS1hKHYBISEhCAkJeeo5MpkMzs7O5R6Ljo7GwYMHcf78ebRu3RoA8N1336FPnz74z3/+A1dXV53XTERERLWL6Fd4KuL48eNwdHREo0aN8P777yM9PV1zLDw8HDY2NpqwAwA9e/aEgYEBzp49K0a5REREVMOIfoXnWXr37o2BAwfC29sbcXFx+PjjjxESEoLw8HBIpVIkJSXB0dFR6z6GhoaQy+VISkoqt82ioiIUFRVpbmdnZ1fpcyAiIiJx1fjAM2TIEM3PAQEBCAwMhI+PD44fP44ePXpUqs3Q0FAsWLBAVyUSERFRDVcrvtL6t/r168Pe3h6xsbEAAGdnZ6SkpGidU1JSgoyMjCf2+5kzZw6ysrI0271796q8biIiIhJPrQs89+/fR3p6OlxcXAAA7du3R2ZmJi5evKg559ixY1Cr1QgKCiq3DZlMBisrK62NiIiI9JfoX2nl5uZqrtYAwJ07dxAREQG5XA65XI4FCxZg0KBBcHZ2RlxcHGbNmgVfX18EBwcDAPz9/dG7d2+MGzcOa9asgVKpxKRJkzBkyBCO0CIiIiIANeAKz4ULF9CiRQu0aNECADBjxgy0aNECc+fOhVQqRWRkJF599VU0bNgQY8eORatWrfDXX39BJpNp2ti0aRP8/PzQo0cP9OnTB506dcKPP/4o1lMiIiKiGkb0Kzxdu3aFIAhPPP7nn38+sw25XI7NmzfrsiwiIiLSI6Jf4SEiIiKqagw8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSeww8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSeww8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSeww8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSe6IHnpMnT6Jfv35wdXWFRCLBnj17NMeUSiVmz56NgIAAmJubw9XVFSNHjkRCQoJWG15eXpBIJFrb119/Xc3PhIiIiGoq0QNPXl4emjVrhpUrV5Y5lp+fj0uXLuGzzz7DpUuXsGvXLsTExODVV18tc+7ChQuRmJio2SZPnlwd5RMREVEtYPgid75w4QK2b98OhUKB4uJirWO7du2qUBshISEICQkp95i1tTUOHz6ste/7779H27ZtoVAo4OHhodlvaWkJZ2fn53wGREREVBdU+grP1q1b0aFDB0RHR2P37t1QKpWIiorCsWPHYG1trcsatWRlZUEikcDGxkZr/9dffw07Ozu0aNEC33zzDUpKSp7YRlFREbKzs7U2IiIi0l+VvsLz1VdfYdmyZZg4cSIsLS3x7bffwtvbG++++y5cXFx0WaNGYWEhZs+ejaFDh8LKykqzf8qUKWjZsiXkcjn+/vtvzJkzB4mJiVi6dGm57YSGhmLBggVVUiMRERHVPJW+whMXF4e+ffsCAIyNjZGXlweJRILp06fjxx9/1FmBjyiVSrzxxhsQBAGrV6/WOjZjxgx07doVgYGBeO+997BkyRJ89913KCoqKretOXPmICsrS7Pdu3dP5/USERFRzVHpwGNra4ucnBwAQL169XDt2jUAQGZmJvLz83VT3T8ehZ34+HgcPnxY6+pOeYKCglBSUoK7d++We1wmk8HKykprIyIiIv1V6a+0unTpgsOHDyMgIACDBw/G1KlTcezYMRw+fBg9evTQWYGPws6tW7cQFhYGOzu7Z94nIiICBgYGcHR01FkdREREVHtVOvB8//33KCwsBAB88sknMDIywt9//41Bgwbh008/rXA7ubm5iI2N1dy+c+cOIiIiIJfL4eLigtdffx2XLl3Cvn37oFKpkJSUBACQy+UwNjZGeHg4zp49i27dusHS0hLh4eGYPn06RowYAVtb28o+PSIiItIjlQ48crlc87OBgQE++uijSrVz4cIFdOvWTXN7xowZAIBRo0Zh/vz5+O233wAAzZs317pfWFgYunbtCplMhq1bt2L+/PkoKiqCt7c3pk+frmmHiIiI6LkCT3Z2tqa/y7OGcle0X0zXrl0hCMITjz/tGAC0bNkSZ86cqdBjERERUd30XIHH1tYWiYmJcHR0hI2NDSQSSZlzBEGARCKBSqXSWZFEREREL+K5As+xY8c0X2WFhYVVSUFEREREuvZcgeell14q92cioqpWqFThwt2H+DsuDRFxD+E04hscSzKEec59mBpJ4Wgpg5OVCVxtTCE1KHv1mYjqtkp3Wl6/fj0sLCwwePBgrf07duxAfn4+Ro0a9cLFERHFpuTg//66g70RCShQ/u+rcpN6/nhYDDwsLgAA3ErJLd1vZIAGjpZoWs8KjpYmotRMRDVPpSceDA0Nhb29fZn9jo6O+Oqrr16oKCKixKwCTNx0CT2XnsTW8/dQoFTB0VKG11u5YUxzK6Tu/godHJTo3cQZHX3t0MDRAmbGUhQq1bj6IAtbzt3D3ogHSMoqFPupEFENUOkrPAqFAt7e3mX2e3p6QqFQvFBRRFR3CYKAn/6+i2/+jEFesQoSCdCrsRPe6VwfrT1tIZFIcOnSJcy/+TdcTAW4OVtq7qsWBNx/WICohCzcSs7F3fR83E3Ph7+LJTr52sPMuNK/8oiolqv0p9/R0RGRkZHw8vLS2n/lypUKzYZMRPS4nEIlPtxxBX9GJQMAWnrY4MvXAuDvUrFpLgwkEnjIzeAhN0P7+sU4dzcD0Yk5iE7Mwe3UPHT3c0RDJ8tnN0REeqfSgWfo0KGYMmUKLC0t0aVLFwDAiRMnMHXqVAwZMkRnBRJR3XA3LQ9v/3Qet1PzYCw1wCd9/fFWO08YVLIDso2ZMXo1dkZAPWuE3UhFam4RDlxLwt30PHRt6Ahjw0p/o09EtVClA8/nn3+Ou3fvokePHjA0LG1GrVZj5MiR7MNDRM8lOjEbb609h7TcIrhYm2D1iFZo7m6jk7ZdrE3xZht3nL2TjvN3HyI6MQcp2UXo18wV1qZGOnkMIqr5Kh14jI2NsW3bNnz++ee4cuUKTE1NERAQAE9PT13WR0R67rLiIUatO4fswhL4u1jh57fbwsFSptPHkBpI0MHHHh5yMxy4loT0vGJsPadASIALPORmOn0sIqqZXrgHX8OGDdGwYUNd1EJEdUx0YrYm7LTytMW60W2q9KqLm60ZhrbxwL6rCUjOLsLeiAd42d8JfhXsI0REtVelA49KpcKGDRtw9OhRpKSkQK1Wax0/duzYCxdHRPrrbloe3lpbGnZae9ri57Ftq2UUlYWJIV5v6YbD0cm4mZyLP68nI69YhVaetlX+2EQknkr/dpk6dSo2bNiAvn37omnTpuWuq0VEVJ6MvGKMXFfaZ8ffxQprR7ep1iHjhlID9G7iDAtZGi4pMnEqNg1KlRpu1VYBEVW3Sv+G2bp1K7Zv344+ffrosh4i0nPFJWq8t/EiFBn5cJeb4qe3q/ZrrCeRSCTo3MABpkZSnI5Lx9k7Gci2klZ7HURUPSo9LtPY2Bi+vr66rIWI9JwgCPh0z1Wcu5sBS5kh1o1qI/ryD6295OjkWzprfHS2FNadhkEQBFFrIiLdq3Tg+eCDD/Dtt9/yFwMRVdgvZ+Kx/cJ9GEiA74a1QIMaMglgK09bdP4n9Nh0HIatUbn83UakZyr9ldapU6cQFhaGAwcOoEmTJjAy0r4kvWvXrhcujoj0x2XFQyzcdx0AMCfEH10bOYpckbaWnrbITEvC1UxD7LieC7ejsZjas4HYZRGRjlQ68NjY2OC1117TZS1EpKcy8ooxcdMlKFUCQpo6453OZdfhqwkaWqlx4tf/Qt5jHJYduQm5uRHeau8ldllEpAOVDjzr16/XZR1EpKcEQcCsnVeQkFUIb3tzLH49sEaP6sy5sBfvTZ6O7ddzMfe3KNiaG+OVQFexyyKiF/RCi8mUlJTgyJEj+OGHH5CTkwMASEhIQG5urk6KI6Lab/M5BY5Ep8BYaoCVw1rC0qTmL+fwZhMLjGjnAUEApm+LwKlbaWKXREQv6LkDz6MJBuPj4xEQEID+/ftj4sSJSE1NBQAsWrQIH374oW6rJKJaKTYlF5//029nVu9GaOxaO2Y0lkgkWPBqU/QJcIZSJeDdjRcQeT9T7LKI6AU8V+C5evWqZmX0qVOnonXr1nj48CFMTU0157z22ms4evSobqskolqnuESNadsuo1CpRidfe7zdsWb223kSqYEEy95sjg4+dsgrVmHM+vOIT88TuywiqqQKB56dO3di+PDhWLVqFQDgr7/+wqeffgpjY2Ot87y8vPDgwQPdVklEtc6yIzdx7UE2bMyM8J/BzWBgUHP77TyJzFCKH0e2RtN6VkjPK8aYDeeRmV8sdllEVAkVDjxqtRoqlUrT2fDR7cfdv38flpY1Y24NIhLHmdvpWHMiDgDw9cAAOFuLO7ngi7D4Z4JEV2sT3E7Nw7sbL6K4RP3sOxJRjVLhwPPGG29g48aNGD9+PADg5ZdfxvLlyzXHJRIJcnNzMW/ePC43QVSHZeUrMWNbBAQBeKO1G3o3dRG7pBfmaGWCdWPawEJmiLN3MvDRrkhOTEhUyzxXH56WLVvir7/+AgAsXboUp0+fRuPGjVFYWIhhw4Zpvs5atGhRlRRLRDWbIAj4dO81JGQVwsvODPP6NRG7JJ3xc7bCyuEtITWQYNelB/j+WKzYJRHRc3jueXgMDUvv4ubmhitXrmDr1q2IjIxEbm4uxo4di+HDh2t1YiaiumNPxAP8fiVB0+HXXFZ9K6BXh5caOmBh/yb4ZPc1LDl8Ex52ZujfvJ7YZRFRBbzQbyNDQ0OMGDFCV7UQUS12LyMfn+2JAgBM7dEALTxsRa6oagwP8kR8ej5+PHkbs3ZGwtveHIFuNmKXRUTPUOnA8/PPPz/1+MiRIyvbNBHVMiUqNaZvi0BuUQlaedpiQlcfreMKhQJpabqbvC86OlpnbVXGR739EJeSi6M3UjD+54v4bVJHOFrV3o7ZRHVBpQPP1KlTtW4rlUrk5+fD2NgYZmZmDDxEdcjq43G4EP8QFjJDLH+zOQyl/+seqFAo4Ofvj4L8fJ0/rlizuhsYSLB8SHO8tupvxKbk4r1fLmLL+HaQGUpFqYeInq3Sgefhw4dl9t26dQvvv/8+Zs6c+UJFEVHtcVnxEMuP3gIALOzfBO5yM63jaWlpKMjPx/DZ38DJw6e8Jp5b9LkTOPDTtygsLNRJe5VhaWKE/45sjf7fn8IlRSY+3X2txq8TRlSX6bRHYYMGDfD1119jxIgRuHHjhi6bJqIaKK+oBNO3RUClFvBKoAtea/HkDrxOHj5wa6CbUVvJijidtPOivO3N8f2wlhi9/hx2XLyPxq5WGFPLZpQmqiteaPHQ8hgaGiIhIaHC5588eRL9+vWDq6srJBIJ9uzZo3VcEATMnTsXLi4uMDU1Rc+ePXHr1i2tczIyMjB8+HBYWVnBxsYGY8eO5QKmRNVg4e/XcTc9H67WJvhyQECdvLrRpaEDPu7jDwD44o9onI7lQqNENVGlr/D89ttvWrcFQUBiYiK+//57dOzYscLt5OXloVmzZnj77bcxcODAMscXL16MFStW4KeffoK3tzc+++wzBAcH4/r16zAxKe0kOHz4cCQmJuLw4cNQKpUYM2YMxo8fj82bN1f26RHRMxy8lohtF+5BIgGWvNEc1mY1fxX0qjK2kzeuJ2Zj16UHmLDpEn6b1BGeduZil0VE/1LpwDNgwACt2xKJBA4ODujevTuWLFlS4XZCQkIQEhJS7jFBELB8+XJ8+umn6N+/P4DS0WFOTk7Ys2cPhgwZgujoaBw8eBDnz59H69atAQDfffcd+vTpg//85z9wdXWt3BMkoie6/zAfs3ZGAgDe7eKD9j52IlckLolEgq9eC0Bcah6u3MvEuJ8vYNeEjrDQs3mIiGqzSn+lpVartTaVSoWkpCRs3rwZLi66mUr+zp07SEpKQs+ePTX7rK2tERQUhPDwcABAeHg4bGxsNGEHAHr27AkDAwOcPXu23HaLioqQnZ2ttRFRxRSXqDFp82VkF5agmZs1ZrzcUOySagQTIyl+fKsVHC1luJmci5k7rnD5CaIaROd9eHQpKSkJAODk5KS138nJSXMsKSkJjo6OWscNDQ0hl8s15zwuNDQU1tbWms3d3b0KqifST9/8eQMR9zJhZWKI74e1hLFhjf41Uq2crEyw5q1WMJJKcOBaEtaeuiN2SUT0j0pfb50xY0aFz126dGllH6ZKzJkzR6v+7Oxshh6iCjhyPRn//av0H/FvBjcrMwSdgJYetvjslcaYuzcKoQduINDNBm295WKXRVTnVTrwXL58GZcvX4ZSqUSjRo0AADdv3oRUKkXLli01573IqA1nZ2cAQHJystbXZMnJyWjevLnmnJSUFK37lZSUICMjQ3P/x8lkMshkskrXRVQXPcgswAc7rgAAxnT0QnCT8j9fBLzVzhMX7j7Eb1cSMGnzJeyb0gmOlpyJmUhMlb4W3a9fP3Tp0gX379/HpUuXcOnSJdy7dw/dunXDK6+8grCwMISFheHYsWOVLs7b2xvOzs44evSoZl92djbOnj2L9u3bAwDat2+PzMxMXLx4UXPOsWPHoFarERQUVOnHJqL/UarUmLz5ErIKlGjmZo05If5il1SjSSQShA4MQANHC6TkFGHKlssoUanFLouoTqt04FmyZAlCQ0Nha/u/BQJtbW3xxRdfPNcordzcXERERCAiIgJAaUfliIgIKBQKSCQSTJs2DV988QV+++03XL16FSNHjoSrq6tmlJi/vz969+6NcePG4dy5czh9+jQmTZqEIUOGcIQWkY4s/P06LikyYcl+OxVmLjPE6hGtYG4sxZnbGfjPoZtil0RUp1X6t1Z2djZSU1PL7E9NTUVOTk6F27lw4QJatGiBFi1aACjtG9SiRQvMnTsXADBr1ixMnjwZ48ePR5s2bZCbm4uDBw9q5uABgE2bNsHPzw89evRAnz590KlTJ/z444+VfWpE9C8bz8Rj45l4SCTA0jeas9/Oc/B1tMCi1wMBAGtOxOFQVPkDKYio6lW6D89rr72GMWPGYMmSJWjbti0A4OzZs5g5c2a5Ewg+SdeuXZ86dFMikWDhwoVYuHDhE8+Ry+WcZJCoCvwdl4b5v0UBAD7s1QgvN3Z6xj3oca8EuuJSfCbWnb6DD3ZcwT5nS05KSCSCSl/hWbNmDUJCQjBs2DB4enrC09MTw4YNQ+/evbFq1Spd1khEIohPz8OETZegUgvo39wVE7rqZuHPumhOHz+08rRFTmEJ3vvlEgqVKrFLIqpzKh14zMzMsGrVKqSnp2tGbGVkZGDVqlUwN+dfL0S1WU6hEu/8dAGZ+aWdlBcN4irgL8JIaoCVw1rCztwY0YnZ+HzfdbFLIqpzXrjnYWJiIhITE9GgQQOYm5tzZlGiWk6pUmPylsu4lZILJysZfhzZGiZGUrHLqvWcrU3w7ZAWkEiATWcV2H81UeySiOqUSgee9PR09OjRAw0bNkSfPn2QmFj64R07diw++OADnRVIRNVHrRYwa2ckjsekQmZogB/fag0nK84foyudGtjjvZdKvxqc/Wsk7mXki1wRUd1R6cAzffp0GBkZQaFQwMzsf6M23nzzTRw8eFAnxRFR9REEAZ//cR27Lz+A1ECC1SNaopm7jdhl6Z0ZLzdECw8b5BSWYMrWy1Byfh6ialHpwHPo0CEsWrQIbm5uWvsbNGiA+Pj4Fy6MiKrXyrBYrD99FwDwn8GB6O7HEVlVwUhqgBVDWsDSxBCXFZlYepjz8xBVh0oHnry8PK0rO49kZGRw2QaiWuaXM/GaifHmvtIYr7Vwe8Y96EW4y82waFDp/Dyrj8fh5M2yc5oRkW5VOvB07twZP//8s+a2RCKBWq3G4sWL0a1bN50UR0RVb2/EA3y29xoAYHJ3X7zdyVvkiuqGPgEuGB7kAQCYsf0K0nOLRK6ISL9VeuLBxYsXo0ePHrhw4QKKi4sxa9YsREVFISMjA6dPn9ZljURURfZGPMD0bREQBGB4kAdmvNxQ7JLqlM9eaYxzdzJwKyUXc3ZdxQ9vteLwf6IqUukrPE2bNsXNmzfRqVMn9O/fH3l5eRg4cCAuX74MHx9OUEZU0z0KO2oBGNLGHZ/3b8p/bKuZiZEUy4c0h5FUgkPXk7H9wj2xSyLSW5W6wqNUKtG7d2+sWbMGn3zyia5rIqIq9njY+eq1ABgYMOyIoYmrNT7s1QihB25gwe/XEeRtBy97Tt5KpGuVusJjZGSEyMhIXddCRNWAYafmeadzfbSrL0d+sQrTtkWghEPViXSu0l9pjRgxAmvXrtVlLURUxRh2aiapgQRL3mgOSxNDRNzLxPdhsWKXRKR3Kt1puaSkBOvWrcORI0fQqlWrMutnLV269IWLIyLdYdip2erZmOKLAU0xdWsEvjsWi5caOqCFh63YZRHpjecOPLdv34aXlxeuXbuGli1bAgBu3tSeOIsdH4lqlt+vJDDs1AL9m9fDsRsp2BuRgA93XMEfUzpzHTMiHXnuwNOgQQMkJiYiLCwMQOlSEitWrICTE2dlJaqJDlxNxLR/ws6brRl2aroFrzZBeFw64lLzsPTwTXzcx1/skoj0wnP34Xl8NfQDBw4gLy9PZwURke4cvp6MyVsuQ6UWMKilG0IHMuzUdDZmxggdGAAA+O9ft3ExPkPkioj0Q6U7LT/yeAAiopoh7EYKJmy6iBK1gP7NXbH49UCGnVqih78TXm/lBkEAPtwRiYJildglEdV6zx14JBJJmT467LNDVLP8dSsV7/5yEUqVgD4BzlgyuBmkDDu1ymevNIazlQnupOXhmz9jxC6HqNZ77j48giBg9OjRmgVCCwsL8d5775UZpbVr1y7dVEhEz+Xagyy8t/EiikvUeLmxE74d0gKG0he+mEvVzNrUCF8PCsDo9eex/u87CG7ihKD6dmKXRVRrPXfgGTVqlNbtESNG6KwYInox9zLyMWbDeeQVq9DBxw7fD2sBI4ad5xYdHa3T9uzt7eHh4fHc9+vayBFD2rhj6/l7mLkzEgendYaZcaVnEyGq0577k7N+/fqqqIOIXlBmfjFGrz+H1Jwi+DlbYs1brSAz5JDm55GdkQpA93/ImZqZ4UZ0dKVCzyd9/XHyZioUGfn4+sANLOzfVKe1EdUV/FOBSA8UKlUY9/MFxKXmwcXaBOvHtIGViZHYZdU6BbnZAIC+736CRoGtdNJmsiIOmxbNRFpaWqUCj6WJERa/3gwj1p7Fz+Hx6N3EGR187XVSG1FdwsBDVMup1QJmbI/A+bsPYWliiA1j2sLF2lTssmo1O1dPuDVoInYZGp0a2GN4kAc2nVVg5s5I/Dm9Cyxk/PVN9Dz45T5RLbf8yE3sv5oEY6kBfnyrNRo5W4pdElWBOX384WZrigeZBVh04IbY5RDVOgw8RLXYn1FJWHGsdKHJ0IEBaO/DUTz6ykJmiEWDAgEAG8/E48ztdJErIqpdGHiIaqnYlFx8sP0KAGBMRy8MauUmckVU1Tr62mNo29J+QLN/5YSERM+DgYeoFiooVmHipkvILSpBkLec6y3VIXP6+MHF2gTx6fn4zyFOSEhUUQw8RLXQwn1RiEnOgb2FDN8Pa8m5duoQKxMjfPXPWlvrTt/hWltEFcTfkkS1zG9XErDl3D1IJMC3Q5rDwVImdklUzbo1csSglqVrbc3cGYlCJb/aInoWBh6iWiQhswCf7LoKAJjczRcdOR9LnTX3lcZwsJThdmoelh+5JXY5RDUeAw9RLaFWC/hwxxXkFJWghYcNpvRoIHZJJCJrMyN8OaB01uUfT8bhyr1McQsiquFq/MxVXl5eiI+PL7N/woQJWLlyJbp27YoTJ05oHXv33XexZs2a6iqRqFps+Psu/o5Lh6mRFEvfaM4FQWsZXa/PBQB+9vZ4tZkrfruSgFk7I/Hb5I5cToToCWp84Dl//jxUqv99P33t2jW8/PLLGDx4sGbfuHHjsHDhQs1tMzOzaq2RqKrdScvDooOlk8193McP3vbmIldEFVVV63MBpWt0nbl0Fadj0xCTnIOVYXGY8XJDnT8OkT6o8YHHwcFB6/bXX38NHx8fvPTSS5p9ZmZmcHZ2ru7SiKqFIAiYsysSRSVqdPK1x4h2ngAAhUKBtLQ0nT5WZVf1pierivW5gP+t0VWSl4mF/Zti4uZLWBUWi+AmTmjiaq2zxyHSFzU+8PxbcXExfvnlF8yYMQMSiUSzf9OmTfjll1/g7OyMfv364bPPPuNVHtIbW8/fw5nbGTA1kiJ0YAAkEgkUCgX8/P1RkJ+v08d6kVW96emqcn2uPgHO6N3EGQejkjBrZyT2TOzIqQqIHlOrAs+ePXuQmZmJ0aNHa/YNGzYMnp6ecHV1RWRkJGbPno2YmBjs2rXrie0UFRWhqKhIczs7O7sqyyaqtOTsQny1v7Tvxwe9GsJdXhrk09LSUJCfj+Gzv4GTh49uHusFV/Um8UgkEiwc0ARn7qQjKiEbP5yIw6Tu7NRO9G+1KvCsXbsWISEhcHV11ewbP3685ueAgAC4uLigR48eiIuLg49P+f8QhIaGYsGCBVVeL9GLCt0fjZzCEjRzs8aYjt5ljjt5+NSoVb1JPI6WJpjXrzGmb7uCFUdj0auJMxo6cSFZokdqzTXP+Ph4HDlyBO+8885TzwsKCgIAxMbGPvGcOXPmICsrS7Pdu3dPp7US6cL5uxnYE5EAiQT4YkAApAaSZ9+J6rQBzeuhh58jilVqzNwZiRKVWuySiGqMWnOFZ/369XB0dETfvn2fel5ERAQAwMXF5YnnyGQyyGScnZZqLpVawLy9UQCAIW3cEeBWfZ1QdT18uiqGY1P5JBIJvnwtAOeWncCVe5lYd/oOxnfRzVeeRLVdrQg8arUa69evx6hRo2Bo+L+S4+LisHnzZvTp0wd2dnaIjIzE9OnT0aVLFwQGBopYMdGL2XxOgeuJ2bAyMcSHvRpVy2NW5fBpAMjNza2Sdkmbs7UJPuvbGLN+jcSSQzfR098J9R0sxC6LSHS1IvAcOXIECoUCb7/9ttZ+Y2NjHDlyBMuXL0deXh7c3d0xaNAgfPrppyJVSvTiHuYVY8k/q2B/0KsR7Cyq52pkVQ2fjj53Agd++haFhYU6a5OebnBrN/wemYC/bqVh1s5IbH+3PQz4lSjVcbUi8PTq1QuCIJTZ7+7uXmaWZaLa7j+HYpCZr4SfsyWGB1X/aCldD59OVsTprC2qGIlEgtCBAQhedhIX4h/ip/C75XZ6J6pLak2nZaK64NqDLGw+pwAAzH+1CZePoEpzszXDR338AQCLD8ZAka7bOZuIahv+NiWqIQRBwMLfr0MQgFcCXdCuvp3YJVEtN7ytB9rVl6NAqcLsXyOhVpe9Uk5UVzDwENUQR6NTcO5uBmSGBvj4n7/MiV6EgYEEiwYFwtRIivDb6dhyXiF2SUSiqRV9eIhqu2ete6VSC1hwqPR4X19TJN2ORtJT2uNQb6ooTztzzAxuhIX7riN0/w10beSIejamYpdFVO0YeIiqWEXWvTIP6An7PtOgKsjB8vfexLKivAq1zaHeVBGjO3hh/9VEXIh/iI9+jcTPb7fVWo+QqC5g4CH6l6pYgTw6Ovqp616p1MCfiUYoUAHNXUzxxtKNz26TQ73pORgYSLDo9UD0+fYv/HUrDZvOKjCinafYZRFVKwYeon9U1Qrkj5jLncod7n0x/iEKVGmwkBmiS3OfCo3M4lBvel4+DhaY1dsPn++7ji//iEZHX3t425uLXRZRtWHgIfpHVaxADjz9akyhUoXzdzMAAO3r23EYOlWpMR28cDQ6GX/HpWPG9gjseLc933NUZzDwED1G1yuQP+1qzIX4hygqUcPO3Bh+LlzZmqqWgYEE3wxuht7LTuKyIhNrTsRhUvcGYpdFVC0Y7YlEkltYgoh7mQCADj52MGAnUqoG9WxMsaB/aaBffuQWrj3IErkiourBwEMkknN3M6BSC3C1NmFfCqpWr7Woh5CmzihRC5i2LQKFSpXYJRFVOQYeIhFkFygRlVD6l3V7HzsOEaZqJZFI8OVrAbC3kCE2JReLD8aIXRJRlWPgIRLB+bsZUAuAm60p3GzNxC6H6iC5uTG+eT0QALDu9B38Havb6RiIahoGHqJqllWgxPXEbADgelkkqm5+jhgW5AEA+HDHFWQVKEWuiKjqMPAQVbNzd0qv7njIzTjFP4nukz7+8LQzQ0JWIeb/FiV2OURVhoGHqBpl5hcjOunR1R25yNUQAeYyQyx9ozkMJMDuyw+wN+KB2CURVQkGHqJqdO5uBgQB8LQzg4s1r+5QzdDK0xZTepTOx/Pp7mu4l1E1s40TiYmBh6ia5KuluJGYA4B9d6jmmdTNF609bZFTVIKpWy+jRKUWuyQinWLgIaom8UpLCAC87c3hbGUidjlEWgylBlj2ZnNYygxxSZGJFcdixS6JSKcYeIiqgaGdG1JUpV9htfNm3x2qmdzlZvjitaYAgO+P3dKs80akDxh4iKqBTYehACTwcTCHI6/uUA3Wv3k9DGxZD2oBmLY1gkPVSW8w8BBVsTzIYObfGQAQ5M2+O1TzLezfFB5yMzzILMDHu69CEASxSyJ6YQw8RFVMAXtIJAawlxbAwVImdjlEz2QhM8S3Q5pDaiDBH5GJ2HnxvtglEb0wBh6iKpSaU4R0WEEQ1PAyyhG7HKIKa+FhixkvNwQAzPstCnfT8kSuiOjFGIpdAJE+O3snHQCQf+MUzFv5ilwN6aPo6Gidtmdvbw8Pj9LlJt57yQcnb6bi7J0MTNl6GTvf6wBjQ/6dTLUTAw9RFUnJKURcah4AAZmnNwOt5opdEumR7IxUAMCIESN02q6pmRluREfDw8MDUgMJlr3ZHCHf/oXI+1lYduQmZvf20+njEVUXBh6iKnL2dumQXgdkIz6dfSBItwpyS5co6fvuJ2gU2EonbSYr4rBp0UykpaVprvK42pgidGAAJmy6hDUn4tC5gT06+Ng/d9sKhQJpabpdkf3fV6OInoWBh6gKJGcX4nZaHiQAPJCGC2IXRHrLztUTbg2aVOlj9AlwwZA27th6/h5mbLuCA1M7w9bcuML3VygU8PP3R0G+bpes+PfVKKJnYeAhqgLht0v77jRytoRpUrHI1RC9uLn9GuPcnQzcTsvDR7sisWZEK0gkkgrdNy0tDQX5+Rg++xs4efjopJ7yrkYRPQ0DD5GOJWQWID49HxIJEOQtR1yS2BURvTgzY0N8O6QFBq4+jT+jkrHl3D0MC3q+oOHk4VPlV6OInoTd7Yl07NHVnSYuVrAxq/hlf6KaLsDNGjODGwEAFu6LQmxKrsgVEVUcAw+RDt3LyMf9hwWQSiRowzWzSA+906k+Ovnao1CpxrRtl1FcwlXVqXZg4CHSEUEQNFd3mtazgpWJkcgVEemegYEE/xncDDZmRrj2IBvLjtwUuySiCqnxgWf+/PmQSCRam5/f/+aBKCwsxMSJE2FnZwcLCwsMGjQIycnJIlZMddXd9HwkZhXC0ECCNl68ukP6y9naBKGvBQAA1pyIQ3hcusgVET1bjQ88ANCkSRMkJiZqtlOnTmmOTZ8+Hb///jt27NiBEydOICEhAQMHDhSxWqqL/n11p5mbDcxlHA9A+i0kwAVvtHaDIAAfbI9AVj5XVaearVb8VjY0NISzs3OZ/VlZWVi7di02b96M7t27AwDWr18Pf39/nDlzBu3atavuUqmOikvNQ2pOEYykErTytBW7HKJqMa9fE5y9k4H49Hx8uvcaVgxpXuGh6kTVrVZc4bl16xZcXV1Rv359DB8+HAqFAgBw8eJFKJVK9OzZU3Oun58fPDw8EB4e/sT2ioqKkJ2drbURVZb6X1d3WrjbwtRYKnJFRNXDXGaI5W+Wrqr++5UE7Il4IHZJRE9U4wNPUFAQNmzYgIMHD2L16tW4c+cOOnfujJycHCQlJcHY2Bg2NjZa93FyckJS0pMnPwkNDYW1tbVmc3d3r+JnQfosOjEbGXnFkBkaoKWHjdjlEFWrFh62mNqjAQBg7p4o3MvQ7WzKRLpS4wNPSEgIBg8ejMDAQAQHB2P//v3IzMzE9u3bK93mnDlzkJWVpdnu3bunw4qpLlGq1JqrO2295ZAZ8eoO1T0TuvqglactcopKMH1bBEpUHKpONU+t6MPzbzY2NmjYsCFiY2Px8ssvo7i4GJmZmVpXeZKTk8vt8/OITCaDTCarhmpJ312+l4m8IhWsTAwR6GYtdjlEOhEdHf3c93mniSGuP5DgQvxDfLblLwxubPlC7RHpWq0LPLm5uYiLi8Nbb72FVq1awcjICEePHsWgQYMAADExMVAoFGjfvr3IlZK+yy8uwcW7DwEA7X3sYGhQ4y+YEj1VdkYqAGDEiBGVur95k+6wf2UGNkdmYems8ShO1J6jJzeXMzOTeGp84Pnwww/Rr18/eHp6IiEhAfPmzYNUKsXQoUNhbW2NsWPHYsaMGZDL5bCyssLkyZPRvn17jtCiKnfuTgaKVWo4WsrQyMny2XcgquEKcksHcPR99xM0Cmz13PcXBOBcugr386XwfXsJejorYWgARJ87gQM/fYvCwkJdl0xUYTU+8Ny/fx9Dhw5Feno6HBwc0KlTJ5w5cwYODg4AgGXLlsHAwACDBg1CUVERgoODsWrVKpGrJn2XmV+Mqw+yAACdfO05FJf0ip2rZ6UX+XRQqrDprAK5RSWIVdujZyMnJCvidFwh0fOr8YFn69atTz1uYmKClStXYuXKldVUERHwd1w61ALgaWcGd7mZ2OUQ1RgmRlIEN3HCr5ceICohG9725mKXRASgFozSIqppkrIKceufVaI7+dqLXA1RzeNma6aZgPNodAqKwdGLJD4GHqLnIAgCjt9MAQD4u1jC3oKj/YjK066+HPYWxihQqnALLmKXQ8TAQ/Q8ohKykZxdBGOpATr68OoO0ZMYGhgguIkzpBIJHsISFs2CxS6J6jgGHqIKKlSqcDouDQAQVF/OBUKJnsHeQoYOPnYAANvu76BAza+2SDwMPEQV9HdcOgqVatiZG6OZm43Y5RDVCi08bGCNPBgYm+JGsS3UakHskqiOYuAhqoDk7ELNMPRujRwhNeAwdKKKkEgkaIAEqIvykK02xgXFQ7FLojqKgYfoGQRBwPGY0hloGzlZop6tqcgVEdUuJihBxuE1AICzt9ORks0JCKn6MfAQPcP1xGwkZRfCWGqATg3YUZmoMvKiwmAvLYBaAP6MSuYCo1TtGHiInqKgWIXTsaWroQfVl8OCHZWJKq2hcRbMjKXIyC/G6bh0scuhOoaBh+gpTtxKRYFSBTsLdlQmelFGEjVe9ncCAETcy4QiI1/kiqguYeAheoI7aXmIScqBBEBPfyd2VCbSAS97cwTUswYAHL6ejEKlSuSKqK5g4CEqR1GJCsdulM6o3MLDBs5WJiJXRKQ/Ojewh42pEXKLSjQDAoiqGgMPUTlO3ExFblEJrE2N0K6+ndjlEOkVI2npLMwSCRCTnIOYpByxS6I6gIGH6DEP8iWITiz9BdyrsROMpPyYEOmas7UJ2nrJAQBhMSnILSwRuSLSd/xNTvQvBmY2uJRROhKrlactXG045w5RVWnjJYeTlQxFJWocjk6GIHAWZqo6DDxE/1ALAuz7TEOxWgJ7C2O0qy8XuyQivSY1kCC4sTMMDSRQZOTjyv0ssUsiPcbAQ/SP32/mwdSnNQwkAoKbOMPQgB8Poqpma26MTr6lE3qeik1DRl6xyBWRvuJvdCKUzgnyS2Rpv51mNirYW8hEroio7gh0s4an3AwqtYA/o5Kg4gKjVAUYeKjOe5hXjEmbL0ElAHk3/oK3Bae8J6pOEokEPRs7QWZogJScIpy7kyF2SaSHGHioTlOpBUzZehn3HxbA2UKK9IPfQ8L5BYmqnYXMED38HAEA5+9mIDGrQOSKSN8w8FCdtuzwTfx1Kw0mRgaY1cEWQlGe2CUR1VkNnCzh52wJAaULjBaX8Gor6Q4DD9VZ+yIT8H1YLABg0aBAeNkYiVwREXVt6AALmSGyCpT46xZnYSbdYeChOiniXiY+2H4FAPBOJ2/0b15P5IqICABkRlL0aly6wOi1hGzEpuSKXBHpCwYeqnMSMgsw7ucLKCpRo4efI+b08Re7JCL6F3e5GVp52gIAjkQnI7tAKXJFpA8YeKhOycwvxqh155CaUwQ/Z0t8O7QFV0EnqoHa17eDs5UJikrUOHCNQ9XpxTHwUJ1RqFRh3M8XcCslF85WJlg7ug0sZIZil0VE5ZAaSBDS1BkyQwMkZRciPC5d7JKolmPgoTqhuESNSZsv4fzdh7A0McRPb7dFPa6TRVSjWZkaoad/aX+ei4qHuJvGUZRUeQw8pPeUKjUmb7mEI9EpkBka4L8jW6ORs6XYZRFRBfg6WqCZmzUA4ND1ZK6qTpXGwEN6rUSlxrStEfgzKhnG/4SddvXtxC6LiJ5DJ197OFjKUKBUYf+1RPbnoUph4CG9pVIL+GDHFfxxNRFGUgl+GNEKXRo6iF0WET0nQ6kB+jR1hrGhARKzCnHyJufnoefHwEN6SaUWMGtnJPZGJMDQQIKVw1qi2z/T1hNR7WNjZozeTZwBAJEPsnA3l/980fPhO4b0TqFShYmbLuHXS/chNZDgu6Et0OufX5REVHt525ujXX05AOByhhTGzg1ErohqkxofeEJDQ9GmTRtYWlrC0dERAwYMQExMjNY5Xbt2hUQi0dree+89kSomMeUUKjFm/XkcjEqCsdQA3w9tgZAAF7HLIiIdaeslR317c6ghgcNrHyOzUCV2SVRL1PjAc+LECUycOBFnzpzB4cOHoVQq0atXL+TlaQ9PHDduHBITEzXb4sWLRaqYxJKaU4QhP55B+O10WMgMsWFMG4YdIj0jkUjQq4kTLAwFGFo5YEl4JhcZpQqp8bOuHTx4UOv2hg0b4OjoiIsXL6JLly6a/WZmZnB25tcWdZUiPR8j153F3fR82Jkb46e326JpPWuxyyKiKiAzlKK9gxJ/3i1BVCowZ9dV/GdwICQSzppOT1bjr/A8LisrCwAgl8u19m/atAn29vZo2rQp5syZg/z8/Ce2UVRUhOzsbK2Naq8zt9MxYNVp3E3Ph5utKXa+34Fhh0jPWRkBqXu/hoEE+PXSfSw/ckvskqiGq/FXeP5NrVZj2rRp6NixI5o2barZP2zYMHh6esLV1RWRkZGYPXs2YmJisGvXrnLbCQ0NxYIFC6qrbKpCm88qMHfvNZSoBQTUs8b/jWoNJysTscsiompQeOcS3m1pjdUXs/Dt0VuoZ2OKN9q4i10W1VC1KvBMnDgR165dw6lTp7T2jx8/XvNzQEAAXFxc0KNHD8TFxcHHx6dMO3PmzMGMGTM0t7Ozs+Huzg9JbaJUqfH5vuv4OTweANCvmSsWDwqEqbFU5MqIqDq97GMGqZUDvg+LxZzdV+FkbYKXON8WlaPWfKU1adIk7Nu3D2FhYXBzc3vquUFBQQCA2NjYco/LZDJYWVlpbVR7pOUWYdS6c/g5PB4SCTAzuBFWDGnOsENUR33QqyFea1EPKrWACb9cRFRCltglUQ1U46/wCIKAyZMnY/fu3Th+/Di8vb2feZ+IiAgAgIsLR+jom9OxaZi2LQKpOUUwNTLA1LbWaGudjcuXL79w29HR0TqokIiqm0QiwaJBgUjKKkT47XSMXHsOW8e3QwMnrplH/1PjA8/EiROxefNm7N27F5aWlkhKSgIAWFtbw9TUFHFxcdi8eTP69OkDOzs7REZGYvr06ejSpQsCAwNFrp50pUSlxrdHb+H7sFgIAuAtl+HCigl4/wvdd1TMzc3VeZtEVLWMDQ2w5q1WGP5/Z3DtQTaG/d9ZbBvfDvUdLMQujWqIGh94Vq9eDaB0csF/W79+PUaPHg1jY2McOXIEy5cvR15eHtzd3TFo0CB8+umnIlRLVSEhswDTtkbg3N0MAMDQtu541U2JDrNvYfjsb+DkUbafVmVEnzuBAz99i8LCQp20R0TVy9rUCBvfDsLQ/57BjaQcDPvvWWx7tx087czFLo1qgBofeATh6aviuru748SJE9VUDVUnQRCw/cI9fLEvGjlFJbCQGeKrgQF4tZkrLl26BABw8vCBW4MmOnm8ZEWcTtohIvHYmhvjl3eCMPTHM7iVkqsJPW62ZmKXRiKrNZ2WqW65/zAfI9edw+xfryKnqATN3W2wb3InvNrMVezSiKiGs7eQYdO4INS3N8eDzAIM/e8ZKNKfPDcb1Q0MPFSjqNUCNp6JR/Cyk/jrVhpkhgb4pI8/fn2/A7zseVmaiCrG0dIEm8e1g6edGe5lFGDQmr9xPYGTzNZlDDxUY1yMz0D/lafx2Z5ryCtWoY2XLQ5M7YxxXepDasAp44no+Thbm2D7u+3h52yJ1JwivPlDOM7eThe7LBIJAw+JLjm7ENO3RWDQ6nBcfZAFS5kh5vVrjG3j23OEBRG9ECcrE2x7tz3aesmRU1SCt9adw59RSWKXRSJg4CHR5BWV4Ptjt9DtP8ex+/IDSCTAG63dcOzDrhjT0RsGvKpDRDpgbWqEn8e2xcuNnVBcosb7v1zExjPxYpdF1azGj9Ii/VOoVGHzWQVWhsUiPa8YANDCwwbz+zVBM3cbcYsjIr1kYiTF6uEt8fHuq9h+4T4+23MN0YnZmN+vCYwN+bd/XcDAQ9VGqVLj14v38e3RW0jMKp3rxsvODNNfboh+ga68okNEVcpQaoBFgwLhaWeO/xyKweazCtxKzsGq4a3gYCkTuzyqYgw8VOUKlSpsv3APP5y4jQeZBQAAZysTTO3ZAK+3coORlH9dEVH1kEgkmNjNF/4ulpi6JQLn7z7Eq9+fwg9vtUKgm43Y5VEVYuAhLQqFAmlpaTppq0Cpxp9x+dh3qwAZBSUASufHeO+l+hjRzhMmRlzsk4jE0d3PCXsmdcT4ny8gLjUPr68Jx6d9/fFWO09IJLzarI8YeEhDoVDAz98fBfkvNkGXgYkFLFv1g2WrVyE1LV28z8nCCBN7NMQbrd0ZdIioRvBxsMDuiR0xY1sEjkSnYO7eKJy8mYbFrwdCbm4sdnmkYww8pJGWloaC/PxKr09VpAJu5UgRl2OAEqH0LyQTFOH+/tXYvu5rtG3tpeOKiYhejJWJEX58qzXW/30Xiw7cwJHoZIR8exLL3miODr72YpdHOsTAQ2U87/pUeUUluBj/EFcTs1CiLl37zM7CGG295DDNUmDZ1SMwZIdkIqqhDAwkGNvJG+3qyzF5y2XcTs3D8LVnMb5zfUx/uSGvSusJBh6qtOxCJS7efYioxGyo/gk6jpYyBHnL4W1vDolEgvucyZ2IaokmrtbYN7kTFv5+HVvP38MPJ2/j0PVkLBoUiLbecrHLoxfEwEPPLadQiXN3MnA9MRv/5By4WJsgyFsOD7kZO/wRUbWJjo7WeZsT2tijh78TPt1zFXfS8vDGD+F4q50nZof4wULGfzZrK/6fowrLLy7BhfiHiLyfpbmi425rirbectSzMWXQIaJqk52RCgAYMWKEzts2NTPDjehoHJr+EkL3R2Pr+XvYeCYeR6OTMbdfEwQ3ceLvu1qIgYeeqahEhcuKTFxSPIRSVRp06tmYooOPHVxtTEWujojqooLc0u/L+777CRoFttJZu8mKOGxaNBNpaWnw8PDA14MC0a+ZKz7aFYl7GQV475eL6NzAHvP6NYGvI9f6q00YeOiJSlRqRN7Pwvn4DBQq1QBK++h08LHjV1dEVCPYuXo+1yCLyujoa48/p3XBqrA4/HjyNv66lYbey0/i7U7emNzdF5YmRlX6+KQbDDxUhiAA1xOyEX47HblFpRMG2poZoX19O/g6WjDoEFGdY2ZsiA+DG+H1Vm74fN91HL2Rgh9P3savF+9jcndfDAvy5JpcNRwDD2kIggCT+q1xJMkQ2cpkAICFzBBB9eVo7Gz1Qmtd6bpjYVV0VCQiehYve3OsHd0GYTdSsHDfddxJy8P8369j3em7+KAX1wWsyRh4CABw5V4m5h3PgNPg+chWAjJDA7TxkqOZmzUMX2Ctq6rsWAgAubm5VdIuEdHTdPNzRKcG9th2/h6+PXoLiox8TN0agR9O3MbUng3wsr8Tg08Nw8BTx91Ny8M3f8bgj6uJAAChpBgNbaXo3qK+TibbqqqOhdHnTuDAT9+isLBQZ20SET0PI6kBRrTzxMCW9bDu1B2sOXEb1xOz8e7Gi2jkZImJ3X3RN8AFUgafGoGBp45KzSnCd8duYfNZBUrUAiQSoKunKTbOGoPXv/5B5zOL6rpjYbIiTmdtERG9CDNjQ0zq3gDDgjyx9tRt/PR3PGKSczBly2UsP3wT47vUx4AW9Thjs8gYeOqYlJxC/HjiNn45G68ZedW1kQNm9/ZDQWIsNuSkilwhEZH4KttPsIcDEBRih/238rDvVh5up+Xho11XEbr/Ooa388KIdp6czkMkDDx1RHJ2IdaciMPmswoUlZQGnWbuNpjduxE6+JQukHcpUcwKiYjEp8t+hxIjE1g0D4FVq1eQBSesOh6HH07eRnATJwxp44GOvvb8uqsaMfDouWsPsrD+9F38HpmA4n+CTksPG0zt2RBdGthziDkR0b9URb/DJEUcdu/6L7q/txDXUoqx/2oS9l9NgrOVCQa2rIdBrdzg48BJDKsaA48eUqrU+DMqCRtO38WF+Iea/W28bDG1R0N09LVj0CEiegpd9zssuHUGC7vawczVF5vOKPDblQQkZRdi1fE4rDoeh2Zu1ujd1AW9mzrD295cZ49L/8PAoycEQcAlRSb2RjzAH5GJSM8rBgAYSSXoG+CCUR280MLDVuQqiYjqNj9nK3w+oCk+fcUfR6NTsPPifZy4mYor97Nw5X4WFh28gUZOlghu4oSXGjm+8NQg9D8MPNVAoVAgLS1Np23a29vDtZ4brtzPxNHoFPx2JQH3Hxb877iFDMODPDA8yAOOViY6fWwiInoxMkMp+gS4oE+AC1JzinDoehIOXktCeFw6YpJzEJOcgxXHYmFpYogOPnbo1MABHXzsUN/enFfoK4mBp4opFAr4+fujID9fJ+0Z2jjDxLMZzH3bwL5xB+QWqzXHzI2lCG7ijFebu6Kjrz2M+FcBEVGN52Apw/AgTwwP8kRWvhJHbyTj8PVk/B2XjqwCJf6MSsafUaWz39uaGaGlhy1aetqipYctmtSzghXX8qoQBp4qlpaWhoL8fAyf/Q2cPHye675FKiBLKUFGkQTpRQbIKJagWP2/ZJ9brIa1qRE6NbBH7ybO6OnvBFNjzvNARFRbWZsZYWBLNwxs6QaVWsC1B1k4FZuGv26l4rIiEw/zlTh6IwVHb6Ro7uNmawp/Fyv4u1ih8T+bm60pZ3p+DANPNXHy8Cm3A5wgCMgrUiGzoBiZ+Upk5BUjLa8I6bnFyC9WlTlfKpHAxkiFuGNbsGbeFAzuEcRhjUREekhqIEEzdxs0c7fBxG6+KC5R43piNi7GP8QlxUNcjn+IhKxC3H9YgPsPC3D4erLmvubGUtR3sIC3vTnqO5iX/tfeAt4O5rCQ1c1/+uvms652EhSUAPcf5iMzX4nMAiUy84uRWaBEVr4SJWrhife0NjWCk5UMzlYmcLE2hb2lMZLionHp9BY0tPuQYYeIqJbQ1aLHLcyAFn4A/GyRnlOAxAID3M0swZ1MJe5mKnEvuwR5xSpcfZCFqw+yytzfxsQAjuZSOJhJ4WguhaOZFA7mUjiaG8LBTIp6zg7w8PDQSa01iV4FnpUrV+Kbb75BUlISmjVrhu+++w5t27YVrZ4/o5Lw5Z+pcJ+xA/sTjIGEB+WeJ5EAViZGsDE1gq2ZMewsjGFvIYPc3BjGhk/uh8MVyImIar6qXURZAuCxP5oNpDC0dYGRbT0Yyd1gKHeFkbwejOT1IDW3RWahGpmFatxMV5bboir/BrwdbeBkJYPcVApbEwPYmkohNzWA3EQKW1MDWMkMYPCcnaft7e1FDVJ6E3i2bduGGTNmYM2aNQgKCsLy5csRHByMmJgYODo6ilKTIACKrBIYGJlAAgFWpsawMSsNNjZmxrA2NYKNmRGsTIye60oNVyAnIqo9qnoR5edpt1hdjLwSCfJL8M9/JchX/e92iSCB1MwGilxAkVv0xHYEVQlU+VlQ52dBVZANdUF26e2CbKjyS2//7+fSc0xlxrgRHS1a6NGbwLN06VKMGzcOY8aMAQCsWbMGf/zxB9atW4ePPvpIlJraeNni0862eH/E65j0xUp4NGyok3a5AjkRUe1TVYso66pdQRBw7tgf2LV+JdoNngA7d18UqiQoUAGFKgkKVUCBSoIiNSCRGsLQ0g6wtKtw+3nRJ5GWlsbA8yKKi4tx8eJFzJkzR7PPwMAAPXv2RHh4uGh12VnI0NLFBCWZiaiKrjZcgZyIiHRFIpHAEGooU27D29EKzVv6l3ueSi2goFiFvOISFCpVKFCqUFD8z3+VKhQWqzU/FxSrUFiigiAAglLcP6b1IvCkpaVBpVLByclJa7+TkxNu3LhR5vyioiIUFf3vUl1WVmmnruzsbJ3X9ujrofu3olBUoJu5eB4Fk6S7NxFnbqaTNquqXdZae2qt68+/qtplraxV32s1+mezKm+nWWn3jqQH8dh9bB1ycwfp9N/aR20JwpMH/2gIeuDBgwcCAOHvv//W2j9z5kyhbdu2Zc6fN2+egNJeXty4cePGjRu3Wr7du3fvmVlBL67w2NvbQyqVIjk5WWt/cnIynJ2dy5w/Z84czJgxQ3NbrVYjIyMDdna6X1QzOzsb7u7uuHfvHqysrJ59B6oUvs7Vg69z9eDrXD34OlefqnqtBUFATk4OXF1dn3muXgQeY2NjtGrVCkePHsWAAQMAlIaYo0ePYtKkSWXOl8lkkMlkWvtsbGyqtEYrKyt+oKoBX+fqwde5evB1rh58natPVbzW1tbWFTpPLwIPAMyYMQOjRo1C69at0bZtWyxfvhx5eXmaUVtERERUd+lN4HnzzTeRmpqKuXPnIikpCc2bN8fBgwfLdGQmIiKiukdvAg8ATJo0qdyvsMQkk8kwb968Ml+hkW7xda4efJ2rB1/n6sHXufrUhNdaIggVGctFREREVHs9eaEmIiIiIj3BwENERER6j4GHiIiI9B4DDxEREek9Bp4qtHLlSnh5ecHExARBQUE4d+6c2CXpnfnz50MikWhtfn5+YpdV6508eRL9+vWDq6srJBIJ9uzZo3VcEATMnTsXLi4uMDU1Rc+ePXHr1i1xiq3FnvU6jx49usz7u3fv3uIUW4uFhoaiTZs2sLS0hKOjIwYMGICYmBitcwoLCzFx4kTY2dnBwsICgwYNKjN7Pz1dRV7nrl27lnlPv/fee9VSHwNPFdm2bRtmzJiBefPm4dKlS2jWrBmCg4ORkpIidml6p0mTJkhMTNRsp06dErukWi8vLw/NmjXDypUryz2+ePFirFixAmvWrMHZs2dhbm6O4OBgFBaKuxpybfOs1xkAevfurfX+3rJlSzVWqB9OnDiBiRMn4syZMzh8+DCUSiV69eqFvLw8zTnTp0/H77//jh07duDEiRNISEjAwIEDRay69qnI6wwA48aN03pPL168uHoK1MnqnVRG27ZthYkTJ2puq1QqwdXVVQgNDRWxKv0zb948oVmzZmKXodcACLt379bcVqvVgrOzs/DNN99o9mVmZgoymUzYsmWLCBXqh8dfZ0EQhFGjRgn9+/cXpR59lpKSIgAQTpw4IQhC6fvXyMhI2LFjh+ac6OhoAYAQHh4uVpm13uOvsyAIwksvvSRMnTpVlHp4hacKFBcX4+LFi+jZs6dmn4GBAXr27Inw8HARK9NPt27dgqurK+rXr4/hw4dDoVCIXZJeu3PnDpKSkrTe39bW1ggKCuL7uwocP34cjo6OaNSoEd5//32kp6eLXVKtl5WVBQCQy+UAgIsXL0KpVGq9p/38/ODh4cH39At4/HV+ZNOmTbC3t0fTpk0xZ84c5OfnV0s9ejXTck2RlpYGlUpVZlkLJycn3LhxQ6Sq9FNQUBA2bNiARo0aITExEQsWLEDnzp1x7do1WFpail2eXkpKSgKAct/fj46RbvTu3RsDBw6Et7c34uLi8PHHHyMkJATh4eGQSqVil1crqdVqTJs2DR07dkTTpk0BlL6njY2Nyywizfd05ZX3OgPAsGHD4OnpCVdXV0RGRmL27NmIiYnBrl27qrwmBh6q1UJCQjQ/BwYGIigoCJ6enti+fTvGjh0rYmVEL27IkCGanwMCAhAYGAgfHx8cP34cPXr0ELGy2mvixIm4du0a+/pVsSe9zuPHj9f8HBAQABcXF/To0QNxcXHw8fGp0pr4lVYVsLe3h1QqLdPDPzk5Gc7OziJVVTfY2NigYcOGiI2NFbsUvfXoPcz3d/WrX78+7O3t+f6upEmTJmHfvn0ICwuDm5ubZr+zszOKi4uRmZmpdT7f05XzpNe5PEFBQQBQLe9pBp4qYGxsjFatWuHo0aOafWq1GkePHkX79u1FrEz/5ebmIi4uDi4uLmKXore8vb3h7Oys9f7Ozs7G2bNn+f6uYvfv30d6ejrf389JEARMmjQJu3fvxrFjx+Dt7a11vFWrVjAyMtJ6T8fExEChUPA9/Rye9TqXJyIiAgCq5T3Nr7SqyIwZMzBq1Ci0bt0abdu2xfLly5GXl4cxY8aIXZpe+fDDD9GvXz94enoiISEB8+bNg1QqxdChQ8UurVbLzc3V+ovrzp07iIiIgFwuh4eHB6ZNm4YvvvgCDRo0gLe3Nz777DO4urpiwIAB4hVdCz3tdZbL5ViwYAEGDRoEZ2dnxMXFYdasWfD19UVwcLCIVdc+EydOxObNm7F3715YWlpq+uVYW1vD1NQU1tbWGDt2LGbMmAG5XA4rKytMnjwZ7du3R7t27USuvvZ41uscFxeHzZs3o0+fPrCzs0NkZCSmT5+OLl26IDAwsOoLFGVsWB3x3XffCR4eHoKxsbHQtm1b4cyZM2KXpHfefPNNwcXFRTA2Nhbq1asnvPnmm0JsbKzYZdV6YWFhAoAy26hRowRBKB2a/tlnnwlOTk6CTCYTevToIcTExIhbdC30tNc5Pz9f6NWrl+Dg4CAYGRkJnp6ewrhx44SkpCSxy651ynuNAQjr16/XnFNQUCBMmDBBsLW1FczMzITXXntNSExMFK/oWuhZr7NCoRC6dOkiyOVyQSaTCb6+vsLMmTOFrKysaqlP8k+RRERERHqLfXiIiIhI7zHwEBERkd5j4CEiIiK9x8BDREREeo+Bh4iIiPQeAw8RERHpPQYeIiIi0nsMPERUa82fPx/NmzcXuwydGT16NGerJqoiDDxEVK7w8HBIpVL07dtX7FKq1OjRoyGRSMpsvXv3rtD9vby8sHz58qotkoheGNfSIqJyrV27FpMnT8batWuRkJAAV1dXsUuqMr1798b69eu19slkMpGqIaKqwCs8RFRGbm4utm3bhvfffx99+/bFhg0btI4fP34cEokER48eRevWrWFmZoYOHTogJiZG67zVq1fDx8cHxsbGaNSoETZu3Kg5JggC5s+fDw8PD8hkMri6umLKlClPrevrr7+Gk5MTLC0tMXbsWBQWFpY55//+7//g7+8PExMT+Pn5YdWqVc98vjKZDM7Ozlqbra3tM+vs2rUr4uPjMX36dM2VIaD8r9qWL18OLy8vzW2VSoUZM2bAxsYGdnZ2mDVrFh5f6aeoqAhTpkyBo6MjTExM0KlTJ5w/f/6Zz4eIymLgIaIytm/fDj8/PzRq1AgjRozAunXryvxjDACffPIJlixZggsXLsDQ0BBvv/225tju3bsxdepUfPDBB7h27RreffddjBkzBmFhYQCAX3/9FcuWLcMPP/yAW7duYc+ePQgICHhqTfPnz8dXX32FCxcuwMXFpUyY2bRpE+bOnYsvv/wS0dHR+Oqrr/DZZ5/hp59+qvRr8bQ6d+3aBTc3NyxcuBCJiYlITEyscLtLlizBhg0bsG7dOpw6dQoZGRnYvXu31jmzZs3Cr7/+ip9++gmXLl3SrJSekZFR6edDVGdVyxKlRFSrdOjQQVi+fLkgCIKgVCoFe3t7ISwsTHP80SrfR44c0ez7448/BABCQUGBpo1x48ZptTt48GChT58+giAIwpIlS4SGDRsKxcXFFaqpffv2woQJE7T2BQUFCc2aNdPc9vHxETZv3qx1zueffy60b9/+ie2OGjVKkEqlgrm5udb25ZdfVqhOT09PYdmyZVr75s2bp1WXIAjCsmXLBE9PT81tFxcXYfHixZrbSqVScHNzE/r37y8IgiDk5uYKRkZGwqZNmzTnFBcXC66urlr3I6KK4RUeItISExODc+fOYejQoQAAQ0NDvPnmm1i7dm2ZcwMDAzU/u7i4AABSUlIAANHR0ejYsaPW+R07dkR0dDQAYPDgwSgoKED9+vUxbtw47N69GyUlJU+sKzo6GkFBQVr72rdvr/k5Ly8PcXFxGDt2LCwsLDTbF198gbi4uKc+527duiEiIkJre++99ypVZ0VkZWUhMTFR6/kYGhqidevWmttxcXFQKpVar6GRkRHatm2reQ2JqOLYaZmItKxduxYlJSVanZQFQYBMJsP3338Pa2trzX4jIyPNz4/6r6jV6go9jru7O2JiYnDkyBEcPnwYEyZMwDfffIMTJ05otVtRubm5AID//ve/ZYKRVCp96n3Nzc3h6+urszoNDAzKfAWoVCor+lSIqArwCg8RaZSUlODnn3/GkiVLtK52XLlyBa6urtiyZUuF2/L398fp06e19p0+fRqNGzfW3DY1NUW/fv2wYsUKHD9+HOHh4bh69eoT2zt79qzWvjNnzmh+dnJygqurK27fvg1fX1+tzdvbu8J1l+dpdRobG0OlUmmd7+DggKSkJK3QExERofnZ2toaLi4uWs+npKQEFy9e1Nx+1Nn736+hUqnE+fPntV5DIqoYXuEhIo19+/bh4cOHGDt2rNaVHAAYNGgQ1q5dq/mq51lmzpyJN954Ay1atEDPnj3x+++/Y9euXThy5AgAYMOGDVCpVAgKCoKZmRl++eUXmJqawtPTs9z2pk6ditGjR6N169bo2LEjNm3ahKioKNSvX19zzoIFCzBlyhRYW1ujd+/eKCoqwoULF/Dw4UPMmDHjibUWFRUhKSlJa5+hoSHs7e2fWaeXlxdOnjyJIUOGQCaTwd7eHl27dkVqaioWL16M119/HQcPHsSBAwdgZWWl9Xy+/vprNGjQAH5+fli6dCkyMzM1x83NzfH+++9j5syZkMvl8PDwwOLFi5Gfn4+xY8dW6P8BEf2LyH2IiKgGeeWVVzSdih939uxZAYBw5coVTaflhw8fao5fvnxZACDcuXNHs2/VqlVC/fr1BSMjI6Fhw4bCzz//rDm2e/duISgoSLCyshLMzc2Fdu3aaXWCLs+XX34p2NvbCxYWFsKoUaOEWbNmlekcvGnTJqF58+aCsbGxYGtrK3Tp0kXYtWvXE9scNWqUAKDM1qhRowrVGR4eLgQGBgoymUz496/U1atXC+7u7oK5ubkwcuRI4csvv9TqtKxUKoWpU6cKVlZWgo2NjTBjxgxh5MiRmk7LgiAIBQUFwuTJkwV7e3tBJpMJHTt2FM6dO/fU14iIyicRhHLGmhIRERHpEfbhISIiIr3HwENERER6j4GHiIiI9B4DDxEREek9Bh4iIiLSeww8REREpPcYeIiIiEjvMfAQERGR3mPgISIiIr3HwENERER6j4GHiIiI9B4DDxEREem9/wdrl+5VlbMuWQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["#### Experiência (Anos)"],"metadata":{"id":"E-act-Lp_Ehs"}},{"cell_type":"code","source":["## Experiência (Anos)\n","\n","# Defina os parâmetros da distribuição\n","media_anos = 9 # Média de anos de estudo\n","desvio_padrao = 4 # Desvio padrão dos anos de estudo\n","tamanho_amostra = 1000 # Tamanho da amostra\n","\n","# Gere a variável de anos de estudo\n","experiencia = np.random.normal(loc=media_anos, scale=desvio_padrao, size=tamanho_amostra)\n","\n","# Arredonde os valores para garantir que sejam inteiros\n","experiencia = np.round(experiencia).astype(int)\n","\n","\n","# Visualizando a distribuição\n","\n","# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(experiencia, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Anos de Experiência')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição dos Anos de Experiência')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":473},"id":"SHKTPgnb_K8u","executionInfo":{"status":"ok","timestamp":1695060291822,"user_tz":180,"elapsed":539,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"fecc3aac-a2ff-44ae-a8ff-857f61c34354"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Agora vamos gerar Y3 (salario) com todas essas variáveis"],"metadata":{"id":"rnwXoXve_w-u"}},{"cell_type":"code","source":["# Gerando Y3\n","\n","salario = 1320 + 400*genero_dummy + 200 * anos_estudo + 5*(-anos_estudo**2) + 100 * experiencia + 6*(experiencia**2) +erro\n","\n","\n","# Criar um gráfico de distribuição usando Seaborn\n","sns.histplot(salario, kde=True) # kde=True adiciona a estimativa de densidade do kernel\n","plt.xlabel('Salário')\n","plt.ylabel('Frequência')\n","plt.title('Distribuição de Salários')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"zk9UhlF4_0NM","executionInfo":{"status":"ok","timestamp":1695060304513,"user_tz":180,"elapsed":790,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"dd2b71f4-86a5-42e5-cfd2-0ee5a817134e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 1 - A partir do processo de geração dos dados, explique o impacto de mais anos de estudo mantendo o resto constante."],"metadata":{"id":"GU7Q9bHxBHG2"}},{"cell_type":"markdown","source":["O coeficiente positivo antes da variável anos_estudo indica que há um efeito linear positivo dos anos de estudo no salário (Y3). Mantendo todas as outras variáveis constantes, a cada ano adicional de estudo, espera-se que o salário médio (Y3) aumente em 200 unidades. Isso significa que mais educação tende a estar associada a salários mais altos.\n","\n","No entanto, o termo -anos_estudo^2 introduz um efeito não linear. Isso significa que, à medida que a variável anos_estudo aumenta, o efeito positivo inicial dos anos de estudo se atenua e, eventualmente, se torna negativo. Em outras palavras, a relação entre anos de estudo e salário não é estritamente linear, mas segue uma parábola invertida. Isso implica que, após um certo número de anos de estudo, o efeito dos anos de estudo pode chegar em um *plateau* e deixar de afetar o salário."],"metadata":{"id":"bQ4o9RmdD64W"}},{"cell_type":"markdown","source":["Exercício 2 - Faça um coefplot da relação de anos de estudo e o salário (Y3). Essa relação parece ser corretamente resumida por uma regressão linear?"],"metadata":{"id":"QKpDoANxB2Ci"}},{"cell_type":"code","source":["sns.scatterplot(x=anos_estudo, y=salario)\n","plt.ylabel('Salário (R$)')\n","plt.xlabel(\"Anos de Estudo\")\n","plt.title(\"Relação entre Salário e Anos de Estudo\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"WML1Nw_-B6JI","executionInfo":{"status":"ok","timestamp":1695060343951,"user_tz":180,"elapsed":577,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"9259223f-95a0-4c1b-8982-7d1952d5de34"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Relação entre Salário e Anos de Estudo')"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Exercício 3 - Faça um coefplot da relação de anos de experiência e o salário (Y3). Essa relação parece ser corretamente resumida por uma regressão linear?"],"metadata":{"id":"1zIGkPSUExF-"}},{"cell_type":"code","source":["sns.scatterplot(x=experiencia, y=salario)\n","plt.ylabel('Salário')\n","plt.xlabel(\"Anos de Experiência\")\n","plt.title(\"Relação de anos de experiência e salário\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"SDgUAxYwE0vK","executionInfo":{"status":"ok","timestamp":1695036602945,"user_tz":180,"elapsed":973,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"89c619f2-c57f-48ec-82d2-9a47f1839dde"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Relação de anos de experiência e salário')"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["
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4 - Olhando para o processo gerador dos dados, o que significa agora o coeficiente na variável de gênero?"],"metadata":{"id":"WUC1JGxJFMl6"}},{"cell_type":"markdown","source":["Resposta: Mantendo todo o resto constante, ser homem está correlacionado com um salário R$400 reais maior."],"metadata":{"id":"z42MHxJ8Fsgd"}},{"cell_type":"markdown","source":["Agora, vamos rodar a regressão e ver o que encontramos no coeficiente. Primeiro, vamos fazer uma regressão errada que assume uma relação linear entre as variáveis de anos de experiência e estudo."],"metadata":{"id":"Vcw218wuF_cs"}},{"cell_type":"code","source":["# Primeiro, juntar tudo em um dataframe\n","\n","df = pd.DataFrame({'salario': salario, 'anos_estudo':anos_estudo, 'genero':genero_dummy, 'experiencia':experiencia})\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"uBVg5dA1H1gG","executionInfo":{"status":"ok","timestamp":1695060599236,"user_tz":180,"elapsed":345,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"ac866679-8797-4db2-cfdc-4ecb9e4c6879"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" salario anos_estudo genero experiencia\n","0 3844.113066 15 0 5\n","1 5029.195163 9 0 13\n","2 4126.152417 13 1 5\n","3 5610.240503 7 0 16\n","4 4134.272154 14 0 7\n",".. ... ... ... ...\n","995 6811.053357 17 1 16\n","996 4825.774763 8 1 11\n","997 5577.730186 12 0 14\n","998 6818.444105 13 0 18\n","999 4410.635531 25 1 6\n","\n","[1000 rows x 4 columns]"],"text/html":["\n","
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...............
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\n"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["# Separando os regressores\n","\n","regressores = df[['genero', 'experiencia', 'anos_estudo']]\n","\n","# Adicionar a constante\n","regressores = sm.add_constant(regressores)\n","\n","# Encaixando o modelo linear nas variáveis\n","\n","modelo_linear = sm.OLS(df['salario'], regressores).fit()\n","\n","# Pegando a tabela de resultados\n","resultado = modelo_linear.summary()\n","\n","# Imprimindo a regressão\n","\n","print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3jtzmr-RGGHp","executionInfo":{"status":"ok","timestamp":1695060602932,"user_tz":180,"elapsed":296,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"5335dd9d-f9a0-41a2-821c-8962d96a066f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: salario R-squared: 0.960\n","Model: OLS Adj. R-squared: 0.960\n","Method: Least Squares F-statistic: 8012.\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 0.00\n","Time: 18:10:02 Log-Likelihood: -6626.7\n","No. Observations: 1000 AIC: 1.326e+04\n","Df Residuals: 996 BIC: 1.328e+04\n","Df Model: 3 \n","Covariance Type: nonrobust \n","===============================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","-------------------------------------------------------------------------------\n","const 1573.2996 23.279 67.585 0.000 1527.618 1618.981\n","genero 387.4377 11.591 33.427 0.000 364.693 410.183\n","experiencia 206.5208 1.468 140.667 0.000 203.640 209.402\n","anos_estudo 81.1357 1.447 56.077 0.000 78.296 83.975\n","==============================================================================\n","Omnibus: 261.786 Durbin-Watson: 2.030\n","Prob(Omnibus): 0.000 Jarque-Bera (JB): 2275.235\n","Skew: 0.942 Prob(JB): 0.00\n","Kurtosis: 10.145 Cond. No. 63.3\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"]}]},{"cell_type":"markdown","source":["Exercício 5 - Os resultados batem com o do processo gerador das variáveis? O que pode ter ocorrido?"],"metadata":{"id":"PIQbdyNyI4RB"}},{"cell_type":"markdown","source":["Resposta: Não, com exceção da variável de gênero, a maior parte dos valores estão mais distantes do verdadeiro processo. A explicação se encontra no fato de que estamos sofrendo de viés de variáveis omitidas. Neste caso, viés originado pelo erro na forma funcional do modelo."],"metadata":{"id":"5KIbHM6bJObj"}},{"cell_type":"markdown","source":["Agora, vamos rodar o modelo com a forma funcional apropriada. Primeiro, criar as variáveis ao quadrado"],"metadata":{"id":"JUe5iRDRJly_"}},{"cell_type":"code","source":["# Gerando anos de estudo ao quadrado\n","df['anos_estudo_sqr'] = df['anos_estudo'] * df['anos_estudo']\n","\n","# Gerando anos de experiência ao quadrado\n","df['experiencia_sqr'] = df['experiencia'] * df['experiencia']\n","\n","# Vendo o banco\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"anwuDqd7J8OH","executionInfo":{"status":"ok","timestamp":1695060652595,"user_tz":180,"elapsed":340,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"65ad7c68-301a-4b66-be33-9d4ceceab8f4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" salario anos_estudo genero experiencia anos_estudo_sqr \\\n","0 3844.113066 15 0 5 225 \n","1 5029.195163 9 0 13 81 \n","2 4126.152417 13 1 5 169 \n","3 5610.240503 7 0 16 49 \n","4 4134.272154 14 0 7 196 \n",".. ... ... ... ... ... \n","995 6811.053357 17 1 16 289 \n","996 4825.774763 8 1 11 64 \n","997 5577.730186 12 0 14 144 \n","998 6818.444105 13 0 18 169 \n","999 4410.635531 25 1 6 625 \n","\n"," experiencia_sqr \n","0 25 \n","1 169 \n","2 25 \n","3 256 \n","4 49 \n",".. ... \n","995 256 \n","996 121 \n","997 196 \n","998 324 \n","999 36 \n","\n","[1000 rows x 6 columns]"],"text/html":["\n","
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\n"]},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","source":["Agora, rodar o modelo com todas essas variáveis:"],"metadata":{"id":"wtlaDtPhKUyh"}},{"cell_type":"code","source":["# Separando os regressores\n","\n","regressores = df[['genero', 'experiencia','experiencia_sqr', 'anos_estudo', 'anos_estudo_sqr']]\n","\n","# Adicionar a constante\n","regressores = sm.add_constant(regressores)\n","\n","# Encaixando o modelo linear nas variáveis\n","\n","modelo_linear = sm.OLS(df['salario'], regressores).fit()\n","\n","# Pegando a tabela de resultados\n","resultado = modelo_linear.summary()\n","\n","# Imprimindo a regressão\n","\n","print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uqHG4ksGKaBb","executionInfo":{"status":"ok","timestamp":1695060657393,"user_tz":180,"elapsed":292,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"d91afc05-7384-4f96-d631-5d0416ee635c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: salario R-squared: 1.000\n","Model: OLS Adj. R-squared: 1.000\n","Method: Least Squares F-statistic: 1.769e+08\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 0.00\n","Time: 18:10:56 Log-Likelihood: -1389.3\n","No. Observations: 1000 AIC: 2791.\n","Df Residuals: 994 BIC: 2820.\n","Df Model: 5 \n","Covariance Type: nonrobust \n","===================================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","-----------------------------------------------------------------------------------\n","const 1319.4905 0.231 5708.182 0.000 1319.037 1319.944\n","genero 400.0737 0.062 6484.730 0.000 399.953 400.195\n","experiencia 99.9887 0.025 4045.934 0.000 99.940 100.037\n","experiencia_sqr 6.0005 0.001 4530.530 0.000 5.998 6.003\n","anos_estudo 200.0822 0.034 5963.175 0.000 200.016 200.148\n","anos_estudo_sqr -5.0030 0.001 -3660.276 0.000 -5.006 -5.000\n","==============================================================================\n","Omnibus: 3.403 Durbin-Watson: 1.844\n","Prob(Omnibus): 0.182 Jarque-Bera (JB): 3.227\n","Skew: 0.092 Prob(JB): 0.199\n","Kurtosis: 2.790 Cond. No. 1.59e+03\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n","[2] The condition number is large, 1.59e+03. This might indicate that there are\n","strong multicollinearity or other numerical problems.\n"]}]},{"cell_type":"markdown","source":["Exercício 6 - Pensando no verdadeiro processo gerador, os resultados agora se aproximam mais da realidade?\n","\n","$salario = 1320 + 400*genero\\_dummy + 200 * anos\\_estudo + 5*(-anos\\_estudo**2) + 100 * experiencia + 6*(experiencia**2) + erro$"],"metadata":{"id":"mUbsrJlUK0Mc"}},{"cell_type":"markdown","source":["Resposta: Agora que não estamos mais induzindo no modelo o viés da variável omitida, os resultados são muito mais próximos do verdadeiro processo gerador do salário."],"metadata":{"id":"ogQysGduLJMa"}},{"cell_type":"markdown","source":["Exercício 7 - Por fim, o que os p-valores e intervalos de confiança de todos os coeficientes nos indicam?"],"metadata":{"id":"FqLbw2wzNacw"}},{"cell_type":"markdown","source":["Resposta: Que todos os coeficientes são estatisticamente significativos e diferentes de zero."],"metadata":{"id":"ERFZsCVzNgQn"}},{"cell_type":"markdown","source":["### Métricas de *Goodness of fit*\n","\n","Métricas de \"goodness of fit\" (qualidade de ajuste) são utilizadas para avaliar o quão bem um modelo estatístico se ajusta aos dados observados. Essas métricas desempenham um papel crucial em estatísticas, ciência de dados e análise de dados, permitindo que os pesquisadores e analistas determinem o quão bem as previsões ou estimativas de um modelo se alinham com os dados reais. Aqui estão algumas métricas comuns de \"goodness of fit\":\n","\n","#### **RMSE - Erro quadrático médio**\n","\n","Erro Quadrático Médio (RMSE - Root Mean Square Error): Esta métrica quantifica a média dos quadrados dos erros residuais entre as previsões do modelo e os valores reais. O RMSE é frequentemente usado em problemas de regressão, onde é importante medir o quão perto as previsões estão dos valores reais.\n","\n","\n","Aqui estão os principais pontos sobre o RMSE no contexto de regressão:\n","\n","**Cálculo do RMSE**: O RMSE é calculado como a raiz quadrada da média dos quadrados dos erros residuais entre os valores previstos pelo modelo (ŷ) e os valores reais observados (y) em um conjunto de dados de teste. A fórmula é a seguinte:\n","\n","* RMSE = $\\sqrt{(Σ(y - ŷ)² / n)}$\n","\n","Onde:\n","\n","* Σ representa a soma sobre todas as observações no conjunto de teste.\n","* y é o valor real observado.\n","* ŷ é o valor previsto pelo modelo.\n","* n é o número de observações no conjunto de teste.\n","\n","**Interpretação**: O RMSE é uma medida de erro que quantifica a discrepância média entre as previsões do modelo e os valores reais. Quanto menor o valor do RMSE, melhor é o desempenho do modelo, pois indica que as previsões estão mais próximas dos valores reais. Por outro lado, um RMSE maior indica que o modelo está fazendo previsões menos precisas.\n","\n","**Unidades**: O RMSE é expresso na mesma unidade que a variável de resposta (y). Isso o torna facilmente interpretável, pois você pode entender o erro médio em termos das unidades da variável de destino.\n","\n","**Sensibilidade a outliers**: O RMSE é sensível a valores extremos (outliers) nos dados. Isso significa que um único valor atípico pode aumentar significativamente o valor do RMSE, tornando-o uma métrica que pode ser afetada por pontos discrepantes.\n","\n","**Comparação com outras métricas**: Além do RMSE, outras métricas de avaliação de regressão incluem o MAE (Mean Absolute Error), que calcula a média dos valores absolutos dos erros residuais, e o R² (Coeficiente de Determinação), que mede a proporção da variância na variável de resposta explicada pelo modelo. A escolha entre essas métricas depende do contexto e dos requisitos específicos do problema.\n","\n","\n","#### **R² - R Quadrado**\n","\n","\n","O coeficiente de determinação, comumente conhecido como R-quadrado ou R², é uma métrica estatística usada para avaliar a qualidade do ajuste de um modelo de regressão. Essa métrica fornece informações sobre a proporção da variância na variável de resposta (variável dependente) que é explicada pelo modelo de regressão. Em outras palavras, o R-quadrado quantifica o quão bem as previsões do modelo se ajustam aos dados observados.\n","\n","Para calcular o R-quadrado, utilizamos as fórmulas para o cálculo do SSE (Soma dos Quadrados dos Erros) e do SST (Soma dos Quadrados Totais). Aqui estão as fórmulas relevantes:\n","\n","**SSE (Soma dos Quadrados dos Erros)**: A SSE representa a soma dos quadrados dos erros residuais entre as previsões do modelo e os valores reais da variável de resposta. Ela é calculada da seguinte forma:\n","\n","$SSE = Σ(y - ŷ)²$\n","\n","Onde:\n","\n","* Σ representa a soma sobre todas as observações no conjunto de dados.\n","* y é o valor real observado.\n","* ŷ é o valor previsto pelo modelo.\n","\n","**SST (Soma dos Quadrados Totais)**: A SST representa a soma dos quadrados totais da variável de resposta e reflete a variância total dos dados. Ela é calculada da seguinte forma:\n","\n","$SST = Σ(y - ȳ)²$\n","\n","Onde:\n","\n","* Σ representa a soma sobre todas as observações no conjunto de dados.\n","* y é o valor real observado.\n","* ȳ é a média dos valores reais observados.\n","\n","Com as fórmulas para SSE e SST em mãos, o R-quadrado pode ser calculado da seguinte forma:\n","\n","$R² = 1 - (SSE / SST)$\n","\n","O resultado do R-quadrado varia de 0 a 1, onde 0 indica que o modelo não é capaz de explicar nenhuma variância na variável de resposta, e 1 indica que o modelo é capaz de explicar toda a variância na variável de resposta.\n","\n","É importante lembrar que o R-quadrado é uma métrica útil para avaliar o ajuste de um modelo de regressão aos dados, mas não deve ser considerado isoladamente. Outros aspectos do modelo, como a relevância das variáveis independentes e a validade das suposições subjacentes, também devem ser levados em consideração para uma avaliação completa do desempenho do modelo.\n","\n"],"metadata":{"id":"ImNvU4n5G4aY"}},{"cell_type":"markdown","source":["Vamos voltar ao modelo de regressão múltiplo:"],"metadata":{"id":"ObkJr5AsI6h7"}},{"cell_type":"code","source":["# Separando os regressores\n","\n","regressores = df[['genero', 'experiencia','experiencia_sqr', 'anos_estudo', 'anos_estudo_sqr']]\n","\n","# Adicionar a constante\n","regressores = sm.add_constant(regressores)\n","\n","# Encaixando o modelo linear nas variáveis\n","\n","modelo_linear = sm.OLS(df['salario'], regressores).fit()\n","\n","# Pegando a tabela de resultados\n","resultado = modelo_linear.summary()\n","\n","# Imprimindo a regressão\n","\n","print(resultado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Vx4U5ieJq0O","executionInfo":{"status":"ok","timestamp":1695038229607,"user_tz":180,"elapsed":260,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"8235a421-8479-4eab-b6e0-8e6ebbc2874d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" OLS Regression Results \n","==============================================================================\n","Dep. Variable: salario R-squared: 1.000\n","Model: OLS Adj. R-squared: 1.000\n","Method: Least Squares F-statistic: 1.692e+08\n","Date: Mon, 18 Sep 2023 Prob (F-statistic): 0.00\n","Time: 11:57:00 Log-Likelihood: -1435.3\n","No. Observations: 1000 AIC: 2883.\n","Df Residuals: 994 BIC: 2912.\n","Df Model: 5 \n","Covariance Type: nonrobust \n","===================================================================================\n"," coef std err t P>|t| [0.025 0.975]\n","-----------------------------------------------------------------------------------\n","const 1319.9649 0.247 5343.939 0.000 1319.480 1320.450\n","genero 400.0271 0.065 6194.194 0.000 399.900 400.154\n","experiencia 100.0218 0.028 3610.980 0.000 99.967 100.076\n","experiencia_sqr 5.9988 0.001 4095.237 0.000 5.996 6.002\n","anos_estudo 199.9844 0.036 5484.749 0.000 199.913 200.056\n","anos_estudo_sqr -4.9994 0.001 -3411.958 0.000 -5.002 -4.997\n","==============================================================================\n","Omnibus: 6.198 Durbin-Watson: 2.113\n","Prob(Omnibus): 0.045 Jarque-Bera (JB): 7.262\n","Skew: -0.090 Prob(JB): 0.0265\n","Kurtosis: 3.376 Cond. No. 1.62e+03\n","==============================================================================\n","\n","Notes:\n","[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n","[2] The condition number is large, 1.62e+03. This might indicate that there are\n","strong multicollinearity or other numerical problems.\n"]}]},{"cell_type":"markdown","source":["Exercício 1 - Procure o R-Squared na tabela, o que ele indica?"],"metadata":{"id":"kcxg5PGKJtq-"}},{"cell_type":"markdown","source":["Resposta: Temos um encaixe perfeito olhando pelo R-squared de 1 (máximo)."],"metadata":{"id":"Y3gqX99kJyxE"}},{"cell_type":"markdown","source":["Infelizmente, o Statsmodels não nos fornece o valor do RMSE diretamente, então temos que calcular à mão:"],"metadata":{"id":"BtSYClnyJ3BW"}},{"cell_type":"code","source":["from statsmodels.tools.eval_measures import rmse # Importando o RMSE\n","\n","# COmo já fizemos o 'fit' do modelo, vamos gerar predições\n","\n","# Gerando as predições\n","y_pred = modelo_linear.predict(regressores)\n","\n","# calculando o rmse\n","rmse = rmse(df['salario'], y_pred)\n","\n","# Imprimindo o resultado\n","\n","print(f'O RMSE é igual a {rmse}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Abyu6jzuJ9A7","executionInfo":{"status":"ok","timestamp":1695062275640,"user_tz":180,"elapsed":462,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"bd0478ad-a78a-4a59-b168-9b6edb534e3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["O RMSE é igual a 0.9708101301145947\n"]}]},{"cell_type":"markdown","source":["Exercício 2 - O que esse valor de RMSE nos indica com relação à qualidade de previsão do modelo?"],"metadata":{"id":"ZJsvB0lXKkVH"}},{"cell_type":"markdown","source":["Resposta: Temos um RMSE bem próximo à zero, o que indica que o modelo está bom em predizer os valores reais de Y."],"metadata":{"id":"N2ATqh1-LFKm"}},{"cell_type":"markdown","source":["## Desafio - Explore o Corpus da Pesquisa\n","\n","\n"],"metadata":{"id":"NLM-PaQ47B9t"}},{"cell_type":"markdown","source":["

Agora que você revisou os modelos de regressão linear e não-linear tradicionalmente utilizados em análises estatísticas, é hora de aplicar sua criatividade na solução de problemas do mundo real.\n","\n","

Nas últimas aulas, conhecemos o desenho da pesquisa que orienta nossa disciplina, especialmente no que diz respeito à definição e seleção do corpus composto pelos tweets publicados por candidatos a prefeito nas capitais brasileiras durante as eleições de 2020.\n","\n","

Conforme as instruções anteriores, nosso objetivo principal era que você fosse capaz de identificar as etapas essenciais para a construção da base de dados e, acima de tudo, traduzisse de maneira eficaz as etapas lógicas do desenho de pesquisa que produziu o corpus, conforme delineado no Vaccine Codebook of Political Elites 2023, em um código na linguagem Python. Essa iniciativa tinha como foco destacar as etapas de amostragem e aprofundar seu entendimento do processo de seleção da amostra de tweets.\n","\n","

Considerando que você já está completamente familiarizado com as etapas do desenho da nossa pesquisa, é hora de explorar e obter insights acerca do corpus que construímos. Vamos investigar como esses dados observados podem ser utilizados para testar modelos estatísticos e concentraremos nossa análise em todas as etapas do projeto. Por exemplo, podemos conceber um modelo para investigar como as características demográficas dos candidatos à prefeitura nas capitais brasileiras em 2020 influenciam variáveis dependentes específicas. Além disso, podemos explorar a criação de um modelo que busca explicar quais características estão relacionadas aos padrões observados de uso do Twitter durante a campanha eleitoral."],"metadata":{"id":"bRPbACTIhkbg"}},{"cell_type":"markdown","source":["### A Tarefa"],"metadata":{"id":"FcqzqMVlYJ3X"}},{"cell_type":"markdown","source":["

Com base nas discussões realizadas em sala de aula e nas atividades de laboratório anteriores, além das orientações estabelecidas no Vaccine Codebook of Political Elites 2023, este desafio tem como objetivo promover a compreensão de como diferentes abordagens metodológicas podem ser aplicadas para atender aos objetivos de pesquisa deste projeto. Pretendemos contrastar as análises realizadas com modelos estatísticos de regressão utilizando dados observacionais e modelos que empregam técnicas de aprendizado de máquina. Para conduzir os exercícios propostos, utilizaremos como referência uma base de dados fictícia contendo detalhes demográficos e afiliações partidárias dos candidatos à prefeitura nas capitais brasileiras em 2020.\n","\n","[**Link do Codebook**](https://github.com/PedroSchmalz/covid19-tweets-brazilian-mayoral-candidates/blob/main/Codebook.pdf)\n"],"metadata":{"id":"AYGfmdTCYWTr"}},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount('/content/drive')"],"metadata":{"id":"Uj7KUrPLWZf5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699222638409,"user_tz":180,"elapsed":27191,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"fa197d82-203e-46aa-b392-954e73c80564"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","## Carrega os dados\n","\n","candidatos = pd.read_csv(\"/content/drive/MyDrive/FLS6513 FLP0442 - PLN na Ciência Política (Main)/Semana 6 (18-09)/Laboratório/candidatos_prefeito_2020_fict.csv\",\n"," encoding = 'utf-8')\n","\n","candidatos.head()\n","\n","## Carrega o dicionário de variáveis\n","\n","dicionario = pd.read_csv(\"/content/drive/MyDrive/FLS6513 FLP0442 - PLN na Ciência Política (Main)/Semana 6 (18-09)/Laboratório/dicionario_candidatos_prefeito_2020_fict.csv\",\n"," encoding = 'utf-8')\n","\n","dicionario"],"metadata":{"id":"wCEJ4Eq7gzOQ","colab":{"base_uri":"https://localhost:8080/","height":990},"executionInfo":{"status":"ok","timestamp":1699224304785,"user_tz":180,"elapsed":351,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"1c59c226-2243-47d3-a44e-391fca6114d0"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" variavel \\\n","0 sigla_uf \n","1 nome_municipio \n","2 nome_candidato \n","3 genero \n","4 cor_raca \n","5 idade \n","6 grau_instrucao \n","7 ocupacao \n","8 estado_civil \n","9 renda_media \n","10 ficha_limpa \n","11 idade_minima \n","12 inelegibilidade \n","13 problemas_justica_eleitoral \n","14 incumbente \n","15 tempo_experiencia_politica \n","16 recursos_campanha \n","17 apoio_logistico_partido \n","18 reduto_eleitoral_consolidado \n","19 historico_realizacoes_publicas \n","20 estrategia_campanha_predominante \n","21 sigla_partido \n","22 situacao_candidatura \n","23 possui_twitter \n","24 publicou_ano_eleitoral \n","25 alinhado_bolsonaro \n","26 numero_seguidores \n","27 afirmacao_outsider_antiestablishment \n","28 credibilidade_publica \n","29 indice_oposicao_vacinas \n","\n"," descricao \n","0 Sigla da unidade federativa \n","1 Nome do município onde o candidato concorreu \n","2 Nome do candidato à prefeitura \n","3 Gênero do candidato \n","4 Informação sobre a raça ou cor do candidato \n","5 Idade do candidato \n","6 Nível de instrução do candidato \n","7 Ocupação ou profissão do candidato \n","8 Estado civil do candidato \n","9 Renda média do candidato \n","10 Dummy que indica se o candidato possui ficha l... \n","11 Dummy que indica se o candidato cumpre o requi... \n","12 Dummy que indica se o candidato foi julgado in... \n","13 Dummy que indica se o candidato tem problemas ... \n","14 Dummy que indica se o candidato é incumbente (... \n","15 Tempo de experiência política do candidato \n","16 Recursos financeiros disponíveis para a campan... \n","17 Dummy que indica se o candidato recebeu apoio ... \n","18 Dummy que indica se o candidato possui um redu... \n","19 Dummy que indica se o candidato possui um hist... \n","20 Estratégia de campanha predominante do candidato \n","21 Sigla do partido ao qual o candidato é filiado \n","22 Situação da candidatura \n","23 Dummy que indica se o candidato possui uma con... \n","24 Dummy que indica se o candidato fez publicaçõe... \n","25 Dummy que indica se o candidato declarou alinh... \n","26 Número de seguidores da conta do candidato no ... \n","27 Dummy que indica se o candidato se posicionou ... \n","28 Avaliação da credibilidade pública do candidato \n","29 Índice de oposição ou apoio às vacinas por par... "],"text/html":["\n","

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variaveldescricao
0sigla_ufSigla da unidade federativa
1nome_municipioNome do município onde o candidato concorreu
2nome_candidatoNome do candidato à prefeitura
3generoGênero do candidato
4cor_racaInformação sobre a raça ou cor do candidato
5idadeIdade do candidato
6grau_instrucaoNível de instrução do candidato
7ocupacaoOcupação ou profissão do candidato
8estado_civilEstado civil do candidato
9renda_mediaRenda média do candidato
10ficha_limpaDummy que indica se o candidato possui ficha l...
11idade_minimaDummy que indica se o candidato cumpre o requi...
12inelegibilidadeDummy que indica se o candidato foi julgado in...
13problemas_justica_eleitoralDummy que indica se o candidato tem problemas ...
14incumbenteDummy que indica se o candidato é incumbente (...
15tempo_experiencia_politicaTempo de experiência política do candidato
16recursos_campanhaRecursos financeiros disponíveis para a campan...
17apoio_logistico_partidoDummy que indica se o candidato recebeu apoio ...
18reduto_eleitoral_consolidadoDummy que indica se o candidato possui um redu...
19historico_realizacoes_publicasDummy que indica se o candidato possui um hist...
20estrategia_campanha_predominanteEstratégia de campanha predominante do candidato
21sigla_partidoSigla do partido ao qual o candidato é filiado
22situacao_candidaturaSituação da candidatura
23possui_twitterDummy que indica se o candidato possui uma con...
24publicou_ano_eleitoralDummy que indica se o candidato fez publicaçõe...
25alinhado_bolsonaroDummy que indica se o candidato declarou alinh...
26numero_seguidoresNúmero de seguidores da conta do candidato no ...
27afirmacao_outsider_antiestablishmentDummy que indica se o candidato se posicionou ...
28credibilidade_publicaAvaliação da credibilidade pública do candidato
29indice_oposicao_vacinasÍndice de oposição ou apoio às vacinas por par...
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1 - Dado que é factível conceber uma diversidade de modelos estatísticos para fins de inferência ao tratar esses dados como observacionais, sua tarefa é descrever conceitualmente a relação estatística passível de análise em cada uma das etapas de seleção efetuadas no processo de construção do corpus da pesquisa. Para cada modelo, você deve apresentar na sua resposta a pergunta de pesquisa, a variável dependente, as variáveis explicativas e as razões teóricas e metodológicas do porquê determinado modelo é importante.\n","\n","

Dica: Sua resposta deve apresentar diferentes modelos, e cada modelo possui uma variável dependente diferente ($Y_{1}$, $Y_{2}$, $Y_{3}$, etc.) e várias variáveis explicativas ($X_{1}$, $X_{2}$, $X_{3}$, etc.)"],"metadata":{"id":"ZAtDODx9dJLL"}},{"cell_type":"markdown","source":["**RESPOSTA:**\n","\n","**Etapas de Seleção do Corpus**\n","\n","1. Deferimento de Candidaturas;\n","2. Perfil no Twitter;\n","3. Publicações em 2020 no Twitter; e\n","4. Posicionamento sobre Vacinas de Covid-19 no Twitter.\n","\n","Agora, com as etapas de seleção do corpus e as perguntas de pesquisa estabelecidas, podemos definir os modelos estatísticos que ajudarão a abordar cada uma das questões. Vamos identificar as variáveis dependentes (Y) e as variáveis explicativas (X) para cada modelo:\n","\n","**Modelo 1: Impacto das Características nas Candidaturas Deferidas**\n","\n","- Pergunta de Pesquisa: Quais características impactam no deferimento das candidaturas pelo TSE?\n","- Variável Dependente (Y1): Deferimento da candidatura (Sim/Não).\n","- Variáveis Explicativas (X1):\n"," - Gênero do candidato\n"," - Cor/Raça do candidato\n"," - Idade do candidato\n"," - Grau de instrução do candidato\n"," - Ficha limpa do candidato\n"," - Inelegibilidade do candidato\n","\n","Aqui, poderíamos usar uma regressão logística para analisar como as variáveis explicativas (X1) afetam a probabilidade de deferimento da candidatura (Y1). Após a análise, podemos gerar estatísticas descritivas, como coeficientes de regressão, odds ratios e valores-p, para interpretar o impacto das características dos candidatos no deferimento das candidaturas.\n","\n","**Modelo 2: Fatores Influenciando a Criação de Perfis no Twitter**\n","\n","- Pergunta de Pesquisa: Quais fatores influenciam na criação de um perfil no Twitter por um candidato político?\n","- Variável Dependente (Y2): Perfil no Twitter (Sim/Não).\n","- Variáveis Explicativas (X2):\n"," - Idade do candidato\n"," - Incumbência (candidato atual prefeito)\n"," - Recursos de campanha disponíveis\n"," - Alinhamento político com Jair Bolsonaro\n"," - Apoio logístico do partido\n","\n","Para esta pergunta, poderíamos usar uma regressão logística para entender como as variáveis explicativas (X2) influenciam a probabilidade de um candidato criar um perfil no Twitter (Y2). Os coeficientes da regressão podem ajudar a interpretar quais fatores são significativos.\n","\n","**Modelo 3: Fatores Influenciando uma Presença Ativa no Twitter**\n","\n","- Pergunta de Pesquisa: Quais fatores influenciam um candidato a ter uma presença mais ativa no Twitter?\n","- Variável Dependente (Y3): Publicações em 2020 no Twitter (Sim/Não).\n","- Variáveis Explicativas (X3):\n"," - Número de seguidores no Twitter\n"," - Alinhamento político com Jair Bolsonaro\n"," - Recursos de campanha disponíveis\n"," - Estratégia de campanha predominante\n","\n","Neste caso, uma regressão logística pode ser usada para determinar quais variáveis explicativas (X3) estão relacionadas com a presença ativa no Twitter (Y3). Os coeficientes da regressão podem ser interpretados para avaliar o impacto relativo de cada variável.\n","\n","**Modelo 4: Fatores Determinantes de um Posicionamento Negativo sobre Vacinas de Covid-19**\n","\n","- Pergunta de Pesquisa: Quais são os fatores determinantes de um posicionamento negativo acerca das vacinas de Covid-19?\n","- Variável Dependente (Y4): Posicionamento negativo sobre vacinas de Covid-19 (Sim/Não).\n","- Variáveis Explicativas (X4):\n"," - Alinhamento político com Jair Bolsonaro\n"," - Sigla do partido\n"," - Apoio logístico do partido\n"," - Histórico de realizações públicas\n"," - Grau de instrução do candidato\n","\n","Neste caso, uma regressão logística pode ser usada para analisar como as variáveis explicativas (X4) afetam a probabilidade de um candidato ter um posicionamento negativo sobre as vacinas de Covid-19 (Y4). Os coeficientes da regressão podem ajudar a interpretar a influência de cada variável."],"metadata":{"id":"fsHvFESRdOuc"}},{"cell_type":"markdown","source":["

2 - Com base no que você descreveu no exercício anterior, desenvolva um modelo estatístico adequado ao problema que você busca responder em cada uma das etapas de construção do corpus. Use a base de dados fictícia apresentada no início dessa seção. Não esqueça de gerar estatísticas descritivas, figuras e gráficos e de interpretar os resultados encontrados."],"metadata":{"id":"PTFX1dIkdXQ2"}},{"cell_type":"code","source":["### RESPOSTA ###\n","\n","import statsmodels.api as sm\n","import pandas as pd\n","\n","## Criando a variável dummy \"deferimento\"\n","\n","candidatos['deferimento'] = candidatos['situacao_candidatura'].apply(lambda x: 1 if x in ['Deferido', 'Cassado'] else 0).astype(float)\n","\n","## Criando a variável dummy \"posicionamento_negativo_vacinas\"\n","\n","candidatos['posicionamento_negativo_vacinas'] = candidatos['indice_oposicao_vacinas'].apply(lambda x: 1 if x >= 70 else 0)\n","\n","## Transformando outras variáveis em dummy\n","\n","candidatos['genero'] = candidatos['genero'].apply(lambda x: 1 if x in ['M'] else 0).astype(float)\n","\n","candidatos['cor_raca'] = candidatos['cor_raca'].apply(lambda x: 1 if x in ['Branca'] else 0).astype(float)\n","\n","candidatos['grau_instrucao'] = candidatos['grau_instrucao'].apply(lambda x: 1 if x in ['Ensino Médio Incompleto',\n"," 'Ensino Médio Completo',\n"," 'Ensino Superior Incompleto',\n"," 'Ensino Superior Incompleto'] else 0).astype(float)\n","\n","candidatos['estrategia_campanha_predominante'] = candidatos['estrategia_campanha_predominante'].apply(lambda x: 1 if x in ['Digital'] else 0).astype(float)\n","\n","## Modelo 1: Impacto das Características Sociodemográficas nas Candidaturas Deferidas\n","\n","X1 = candidatos[[\"genero\", \"cor_raca\", \"idade\", \"grau_instrucao\", \"ficha_limpa\", \"inelegibilidade\"]]\n","X1 = sm.add_constant(X1)\n","Y1 = candidatos[\"deferimento\"]\n","\n","model1 = sm.Logit(Y1, X1)\n","result1 = model1.fit()\n","\n","print(\"\\nModelo 1: Impacto das Características Sociodemográficas nas Candidaturas Deferidas\\n\\n\",\n"," result1.summary(),\n"," end = \"\\n\")\n","\n","## Modelo 2: Fatores Influenciando a Criação de Perfis no Twitter\n","\n","X2 = candidatos[[\"idade\", \"incumbente\", \"recursos_campanha\", \"alinhado_bolsonaro\", \"apoio_logistico_partido\"]]\n","X2 = sm.add_constant(X2)\n","Y2 = candidatos[\"possui_twitter\"]\n","\n","model2 = sm.Logit(Y2, X2)\n","result2 = model2.fit()\n","print(\"\\nModelo 2: Fatores Influenciando a Criação de Perfis no Twitter\\n\\n\",\n"," result2.summary(),\n"," end = \"\\n\")\n","\n","## Modelo 3: Fatores Influenciando uma Presença Ativa no Twitter\n","\n","X3 = candidatos[[\"numero_seguidores\", \"alinhado_bolsonaro\", \"recursos_campanha\", \"estrategia_campanha_predominante\"]]\n","X3 = sm.add_constant(X3)\n","Y3 = candidatos[\"publicou_ano_eleitoral\"]\n","\n","model3 = sm.Logit(Y3, X3)\n","result3 = model3.fit()\n","print(\"\\nModelo 3: Fatores Influenciando uma Presença Ativa no Twitter\\n\\n\",\n"," result3.summary(),\n"," end = \"\\n\")\n","\n","## Modelo 4: Fatores Determinantes de um Posicionamento Negativo sobre Vacinas\n","\n","X4 = candidatos[[\"alinhado_bolsonaro\", \"apoio_logistico_partido\", \"historico_realizacoes_publicas\", \"grau_instrucao\"]]\n","X4 = sm.add_constant(X4)\n","Y4 = candidatos[\"posicionamento_negativo_vacinas\"]\n","\n","model4 = sm.Logit(Y4, X4)\n","result4 = model4.fit()\n","print(\"\\nModelo 4: Fatores Determinantes de um Posicionamento Negativo sobre Vacinas\\n\\n\",\n"," result4.summary(),\n"," end =\"\\n\")\n"],"metadata":{"id":"h-NyYrVXe4Be","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699224310802,"user_tz":180,"elapsed":354,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"bb8d9a37-730f-4762-ab1a-3b18dff322d0"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["Optimization terminated successfully.\n"," Current function value: 0.622914\n"," Iterations 5\n","\n","Modelo 1: Impacto das Características Sociodemográficas nas Candidaturas Deferidas\n","\n"," Logit Regression Results \n","==============================================================================\n","Dep. Variable: deferimento No. Observations: 150\n","Model: Logit Df Residuals: 143\n","Method: MLE Df Model: 6\n","Date: Sun, 05 Nov 2023 Pseudo R-squ.: 0.05211\n","Time: 22:45:10 Log-Likelihood: -93.437\n","converged: True LL-Null: -98.574\n","Covariance Type: nonrobust LLR p-value: 0.1136\n","===================================================================================\n"," coef std err z P>|z| [0.025 0.975]\n","-----------------------------------------------------------------------------------\n","const -1.5721 0.827 -1.902 0.057 -3.192 0.048\n","genero 0.3766 0.364 1.036 0.300 -0.336 1.089\n","cor_raca 0.7471 0.365 2.049 0.040 0.032 1.462\n","idade 0.0228 0.016 1.471 0.141 -0.008 0.053\n","grau_instrucao -0.3112 0.358 -0.868 0.385 -1.014 0.391\n","ficha_limpa -0.1517 0.361 -0.420 0.674 -0.859 0.556\n","inelegibilidade -0.6963 0.365 -1.908 0.056 -1.412 0.019\n","===================================================================================\n","Optimization terminated successfully.\n"," Current function value: 0.615887\n"," Iterations 5\n","\n","Modelo 2: Fatores Influenciando a Criação de Perfis no Twitter\n","\n"," Logit Regression Results \n","==============================================================================\n","Dep. Variable: possui_twitter No. Observations: 150\n","Model: Logit Df Residuals: 144\n","Method: MLE Df Model: 5\n","Date: Sun, 05 Nov 2023 Pseudo R-squ.: 0.01752\n","Time: 22:45:10 Log-Likelihood: -92.383\n","converged: True LL-Null: -94.030\n","Covariance Type: nonrobust LLR p-value: 0.6546\n","===========================================================================================\n"," coef std err z P>|z| [0.025 0.975]\n","-------------------------------------------------------------------------------------------\n","const -1.4998 0.872 -1.720 0.085 -3.209 0.209\n","idade 0.0221 0.016 1.425 0.154 -0.008 0.053\n","incumbente -0.2235 0.535 -0.417 0.676 -1.273 0.826\n","recursos_campanha -1.676e-07 3.16e-07 -0.531 0.596 -7.87e-07 4.51e-07\n","alinhado_bolsonaro -0.3406 0.411 -0.829 0.407 -1.145 0.464\n","apoio_logistico_partido 0.0092 0.369 0.025 0.980 -0.714 0.733\n","===========================================================================================\n","Optimization terminated successfully.\n"," Current function value: 0.528638\n"," Iterations 5\n","\n","Modelo 3: Fatores Influenciando uma Presença Ativa no Twitter\n","\n"," Logit Regression Results \n","==================================================================================\n","Dep. Variable: publicou_ano_eleitoral No. Observations: 150\n","Model: Logit Df Residuals: 145\n","Method: MLE Df Model: 4\n","Date: Sun, 05 Nov 2023 Pseudo R-squ.: 0.01229\n","Time: 22:45:10 Log-Likelihood: -79.296\n","converged: True LL-Null: -80.283\n","Covariance Type: nonrobust LLR p-value: 0.7406\n","====================================================================================================\n"," coef std err z P>|z| [0.025 0.975]\n","----------------------------------------------------------------------------------------------------\n","const -1.2043 0.596 -2.021 0.043 -2.372 -0.036\n","numero_seguidores -5.853e-07 6.6e-07 -0.887 0.375 -1.88e-06 7.08e-07\n","alinhado_bolsonaro 0.0794 0.441 0.180 0.857 -0.785 0.944\n","recursos_campanha 3.104e-07 3.5e-07 0.886 0.375 -3.76e-07 9.97e-07\n","estrategia_campanha_predominante -0.1718 0.449 -0.383 0.702 -1.052 0.708\n","====================================================================================================\n","Optimization terminated successfully.\n"," Current function value: 0.600867\n"," Iterations 5\n","\n","Modelo 4: Fatores Determinantes de um Posicionamento Negativo sobre Vacinas\n","\n"," Logit Regression Results \n","===========================================================================================\n","Dep. Variable: posicionamento_negativo_vacinas No. Observations: 150\n","Model: Logit Df Residuals: 145\n","Method: MLE Df Model: 4\n","Date: Sun, 05 Nov 2023 Pseudo R-squ.: 0.03357\n","Time: 22:45:10 Log-Likelihood: -90.130\n","converged: True LL-Null: -93.261\n","Covariance Type: nonrobust LLR p-value: 0.1804\n","==================================================================================================\n"," coef std err z P>|z| [0.025 0.975]\n","--------------------------------------------------------------------------------------------------\n","const -1.2238 0.396 -3.092 0.002 -2.000 -0.448\n","alinhado_bolsonaro 0.4404 0.392 1.124 0.261 -0.327 1.208\n","apoio_logistico_partido 0.5744 0.367 1.564 0.118 -0.145 1.294\n","historico_realizacoes_publicas -0.3889 0.364 -1.069 0.285 -1.102 0.324\n","grau_instrucao 0.4526 0.361 1.254 0.210 -0.255 1.160\n","==================================================================================================\n"]}]},{"cell_type":"markdown","source":["

3 - Explique a diferença de interpretação e abordagem SE no exercício 1 você tivesse que realizar uma análise com técnicas de aprendizado de máquina. De que forma as perguntas de pesquisa, variáveis dependentes e independentes, bem como fundamentações téoricas mudariam com o uso desse método? Descreva as diferenças para cada uma das etapas de construção do corpus."],"metadata":{"id":"vI2-GPew8VYs"}},{"cell_type":"markdown","source":["**RESPOSTA:**\n","\n","A utilização de técnicas de aprendizado de máquina (Machine Learning) difere da abordagem estatística tradicional (como as regressões) em várias maneiras, incluindo a abordagem das perguntas de pesquisa, a escolha das variáveis dependentes e independentes e a fundamentação teórica. Vamos analisar como as perguntas de pesquisa e os modelos mudariam para cada uma das etapas de construção do corpus:\n","\n","**Etapas de Seleção do Corpus:**\n","\n","1. **Deferimento de Candidaturas:**\n","\n"," - Na abordagem de aprendizado de máquina, as perguntas podem ser mais voltadas para a previsão, como \"Podemos prever o deferimento de candidaturas com base em características dos candidatos?\"\n"," - Variáveis dependentes e independentes: As variáveis dependentes podem incluir \"deferimento\" (como em abordagens estatísticas). No entanto, as variáveis independentes podem ser mais diversas, incluindo variáveis não tradicionais, como análise de texto de documentos de candidatura.\n"," - Fundamentação teórica: A fundamentação teórica pode envolver o uso de algoritmos de classificação, como árvores de decisão, random forests ou redes neurais, para prever o deferimento.\n","\n","2. **Perfil no Twitter:**\n","\n"," - Perguntas de pesquisa: \"Podemos prever a criação de perfis no Twitter com base nas características dos candidatos?\"\n"," - Variáveis dependentes e independentes: A variável dependente pode ser \"criação de perfil no Twitter\" e as variáveis independentes podem incluir dados de perfil dos candidatos, histórico de campanhas anteriores, e até mesmo análise de texto de discursos políticos.\n"," - Fundamentação teórica: Algoritmos de classificação e mineração de texto podem ser usados para essa análise.\n","\n","3. **Publicações em 2020 no Twitter:**\n","\n"," - Perguntas de pesquisa: \"Podemos prever a atividade no Twitter em 2020 com base nas características dos candidatos?\"\n"," - Variáveis dependentes e independentes: A variável dependente pode ser \"atividade no Twitter em 2020\" e as variáveis independentes podem incluir dados de atividade anterior no Twitter e informações sobre eventos políticos.\n"," - Fundamentação teórica: A análise pode envolver séries temporais e modelos preditivos de atividade no Twitter.\n","\n","4. **Posicionamento sobre Vacinas de Covid-19 no Twitter:**\n","\n"," - Perguntas de pesquisa: \"Podemos prever o posicionamento sobre vacinas de Covid-19 com base nas características dos candidatos?\"\n"," - Variáveis dependentes e independentes: A variável dependente pode ser \"posicionamento sobre vacinas de Covid-19\" e as variáveis independentes podem incluir histórico de posicionamento político, análise de texto de tweets e dados sobre a pandemia.\n"," - Fundamentação teórica: Técnicas de análise de sentimento e classificação de texto podem ser usadas para prever o posicionamento dos candidatos.\n","\n","Em resumo, a abordagem de aprendizado de máquina permite uma análise mais orientada para a previsão e pode incorporar uma gama mais ampla de variáveis, incluindo dados não tradicionais, como análise de texto. A fundamentação teórica muda para incorporar algoritmos de aprendizado de máquina e técnicas de mineração de dados. Além disso, a análise se torna mais complexa, uma vez que envolve o desenvolvimento de modelos preditivos em vez de modelos estatísticos descritivos."],"metadata":{"id":"C1T5vArk8-15"}}]}