diff --git a/Testes_R/.ipynb_checkpoints/TESTE_COHEND-checkpoint.ipynb b/Testes_R/.ipynb_checkpoints/TESTE_COHEND-checkpoint.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b977bf15-ee73-4730-8651-f8a3caffb393",
+ "metadata": {},
+ "source": [
+ "# Analise estatistica com Teste CohenD\n",
+ "Teste de effect size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36e8ccea-ef95-4da0-bca9-45bd2fbe5530",
+ "metadata": {},
+ "source": [
+ "## Instalação dos pacotes\n",
+ "Para fazer o jupyter-lab processar R após a instaçação, dentro do ambiente virtual do python e dentro seu client com sudo `sudo R`, instale os pacotes com o comando a seguir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "57a4780e-976b-4832-b04a-4680e62ccb5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))\n",
+ "#devtools::install_github('IRkernel/IRkernel')\n",
+ "#IRkernel::installspec()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"dataframe\">\n",
+ "<caption>A data.frame: 6 × 10</caption>\n",
+ "<thead>\n",
+ "\t<tr><th></th><th scope=col>ANO_CENSO</th><th scope=col>NUM_COMPUTADOR</th><th scope=col>NUM_COMPUTADOR_ADM</th><th scope=col>NUM_COMPUTADPR_ALUNO</th><th scope=col>NUM_DVD</th><th scope=col>NUM_FUNCIONARIOS</th><th scope=col>NUM_SALAS</th><th scope=col>NUM_SALAS_UTILIZADAS</th><th scope=col>NUM_SOM</th><th scope=col>NUM_TV</th></tr>\n",
+ "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n",
+ "</thead>\n",
+ "<tbody>\n",
+ "\t<tr><th scope=row>1</th><td>2013</td><td>165</td><td>35</td><td>130</td><td>1</td><td>42</td><td> 8</td><td> 8</td><td>1</td><td>3</td></tr>\n",
+ "\t<tr><th scope=row>2</th><td>2013</td><td> 15</td><td> 4</td><td> 11</td><td>5</td><td>16</td><td> 5</td><td> 5</td><td>7</td><td>6</td></tr>\n",
+ "\t<tr><th scope=row>3</th><td>2013</td><td> 25</td><td> 5</td><td> 20</td><td>2</td><td>40</td><td> 8</td><td> 8</td><td>3</td><td>3</td></tr>\n",
+ "\t<tr><th scope=row>4</th><td>2013</td><td> 15</td><td> 8</td><td> 7</td><td>2</td><td>45</td><td>10</td><td>10</td><td>2</td><td>4</td></tr>\n",
+ "\t<tr><th scope=row>5</th><td>2013</td><td> 16</td><td> 8</td><td> 8</td><td>2</td><td>47</td><td>12</td><td>12</td><td>1</td><td>6</td></tr>\n",
+ "\t<tr><th scope=row>6</th><td>2013</td><td> 10</td><td> 7</td><td> 3</td><td>1</td><td>25</td><td> 4</td><td> 4</td><td>0</td><td>1</td></tr>\n",
+ "</tbody>\n",
+ "</table>\n"
+ ],
+ "text/latex": [
+ "A data.frame: 6 × 10\n",
+ "\\begin{tabular}{r|llllllllll}\n",
+ " & ANO\\_CENSO & NUM\\_COMPUTADOR & NUM\\_COMPUTADOR\\_ADM & NUM\\_COMPUTADPR\\_ALUNO & NUM\\_DVD & NUM\\_FUNCIONARIOS & NUM\\_SALAS & NUM\\_SALAS\\_UTILIZADAS & NUM\\_SOM & NUM\\_TV\\\\\n",
+ " & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
+ "\\hline\n",
+ "\t1 & 2013 & 165 & 35 & 130 & 1 & 42 & 8 & 8 & 1 & 3\\\\\n",
+ "\t2 & 2013 & 15 & 4 & 11 & 5 & 16 & 5 & 5 & 7 & 6\\\\\n",
+ "\t3 & 2013 & 25 & 5 & 20 & 2 & 40 & 8 & 8 & 3 & 3\\\\\n",
+ "\t4 & 2013 & 15 & 8 & 7 & 2 & 45 & 10 & 10 & 2 & 4\\\\\n",
+ "\t5 & 2013 & 16 & 8 & 8 & 2 & 47 & 12 & 12 & 1 & 6\\\\\n",
+ "\t6 & 2013 & 10 & 7 & 3 & 1 & 25 & 4 & 4 & 0 & 1\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "A data.frame: 6 × 10\n",
+ "\n",
+ "| <!--/--> | ANO_CENSO <int> | NUM_COMPUTADOR <int> | NUM_COMPUTADOR_ADM <int> | NUM_COMPUTADPR_ALUNO <int> | NUM_DVD <int> | NUM_FUNCIONARIOS <int> | NUM_SALAS <int> | NUM_SALAS_UTILIZADAS <int> | NUM_SOM <int> | NUM_TV <int> |\n",
+ "|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| 1 | 2013 | 165 | 35 | 130 | 1 | 42 | 8 | 8 | 1 | 3 |\n",
+ "| 2 | 2013 | 15 | 4 | 11 | 5 | 16 | 5 | 5 | 7 | 6 |\n",
+ "| 3 | 2013 | 25 | 5 | 20 | 2 | 40 | 8 | 8 | 3 | 3 |\n",
+ "| 4 | 2013 | 15 | 8 | 7 | 2 | 45 | 10 | 10 | 2 | 4 |\n",
+ "| 5 | 2013 | 16 | 8 | 8 | 2 | 47 | 12 | 12 | 1 | 6 |\n",
+ "| 6 | 2013 | 10 | 7 | 3 | 1 | 25 | 4 | 4 | 0 | 1 |\n",
+ "\n"
+ ],
+ "text/plain": [
+ " ANO_CENSO NUM_COMPUTADOR NUM_COMPUTADOR_ADM NUM_COMPUTADPR_ALUNO NUM_DVD\n",
+ "1 2013 165 35 130 1 \n",
+ "2 2013 15 4 11 5 \n",
+ "3 2013 25 5 20 2 \n",
+ "4 2013 15 8 7 2 \n",
+ "5 2013 16 8 8 2 \n",
+ "6 2013 10 7 3 1 \n",
+ " NUM_FUNCIONARIOS NUM_SALAS NUM_SALAS_UTILIZADAS NUM_SOM NUM_TV\n",
+ "1 42 8 8 1 3 \n",
+ "2 16 5 5 7 6 \n",
+ "3 40 8 8 3 3 \n",
+ "4 45 10 10 2 4 \n",
+ "5 47 12 12 1 6 \n",
+ "6 25 4 4 0 1 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Carrega CSV\n",
+ "options(warn = -1)\n",
+ "library(effsize)\n",
+ "df = read.csv(\"../dados/escola-integers-ks.csv\", sep=\"|\")\n",
+ "head(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c7eca10-6c0b-427d-be50-c4bf3d224f2d",
+ "metadata": {},
+ "source": [
+ "## Limpeza de Outliers\n",
+ "Para limpar dados que se diferenciam drasticamente do resto do conjunto de dados é utilizado a remoção dos outliers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Function to remove outliers using the IQR method\n",
+ "remove_outliers <- function(data) {\n",
+ " # Calculate the interquartile range (IQR)\n",
+ " Q1 <- quantile(data, 0.25)\n",
+ " Q3 <- quantile(data, 0.75)\n",
+ " IQR <- Q3 - Q1\n",
+ " \n",
+ " # Define the lower and upper bounds\n",
+ " lower_bound <- Q1 - 1.5 * IQR\n",
+ " upper_bound <- Q3 + 1.5 * IQR\n",
+ " \n",
+ " # Remove outliers\n",
+ " cleaned_data <- data[data >= lower_bound & data <= upper_bound]\n",
+ " \n",
+ " # Return the cleaned data\n",
+ " return(cleaned_data)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ca6c624-62c0-4fce-b3cd-c47d20c0b85e",
+ "metadata": {},
+ "source": [
+ "## Plot de Grafico\n",
+ "Funções para plot de gráficos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4c578156-afa1-4432-acb3-d6a143c0b349",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_ecdf <- function(x, x2){\n",
+ " plot(ecdf(x),\n",
+ " xlim = range(c(x, x2)), \n",
+ " col = \"blue\")\n",
+ " plot(ecdf(x2), \n",
+ " add = TRUE, \n",
+ " lty = \"dashed\",\n",
+ " col = \"red\")\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69c80406-3d20-408d-8fce-e4a7aea65dd4",
+ "metadata": {},
+ "source": [
+ "## Teste Cohen D\n",
+ "Utilizando testes de effect size é possivel determinar a magnitude da relação entre duas amostras\n",
+ "\n",
+ "Primeiro é selecionado duas amostras que foram agrupadas pelos anos do censo. \n",
+ "Em seguida é feito uma limpeza dos dados fazendo a retirada dos nulos, outliers e os ordenando. \n",
+ "Finalmente é executado o teste."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "a9629868-ef70-4073-856b-fa3cee413898",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1] -0.03455845\n"
+ ]
+ },
+ {
+ "data": {
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KMR7AAAqXB2llWr5M03xdU1+WCFCrJhg9SooV1bAFLFGjsAQOq8vGTGDJk0SQ4f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\nhCHmHljmmQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "Plot with title “ecdf(x)”"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 420,
+ "width": 420
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Dados agrupados por ano\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "# Amostras\n",
+ "X <- df[\"NUM_SALAS\"]\n",
+ "Xi <- X[ANO == 2013]\n",
+ "\n",
+ "Y <- df[\"NUM_SALAS\"]\n",
+ "Yi <- Y[ANO == 2014]\n",
+ "\n",
+ "# Limpeza dos dados\n",
+ "data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ "data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ "# Executa teste Cohen D\n",
+ "#print(cohen.d(data1, data2))\n",
+ "result = cohen.d(data1, data2)\n",
+ "print(result$estimate)\n",
+ "\n",
+ "# Plotagem dos dados\n",
+ "plot_ecdf(data1, data2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6c8c14d-5101-4201-b172-28e5b17016df",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos subsequentes\n",
+ "Execução do teste T entre uma coluna de um ano contra todas as colunas dos anos subsequentes para todos os anos do csv."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "9aa86d14-0e60-4945-9d5f-d058b3c45654",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0));\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra de um ano\n",
+ " X <- df[col1]\n",
+ " Xi <- X[ANO == ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " Yi <- Y[ANO == ano+1]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ " data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ "\n",
+ " # Teste Cohen D\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_subsequente.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d52a6568-693a-4d59-b050-2f8d6899433e",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos acumulados\n",
+ "O mesmo teste anterior, porém com o acumulo da amostra conforme o passar dos anos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "bd76fa5b-a42c-434a-b3df-83f12f80a262",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
+ "ano_start = min(df$ANO_CENSO)\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO_COLUMN <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra acumulada dos anos\n",
+ " X <- df[col1]\n",
+ " Xi <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " Yi <- Y[ANO_COLUMN == ano+1]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ " data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ "\n",
+ " # Teste Cohend\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_acumulado.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "4.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Testes_R/Result_COHEND/.ipynb_checkpoints/COHEND_subsequente-checkpoint.csv b/Testes_R/Result_COHEND/.ipynb_checkpoints/COHEND_subsequente-checkpoint.csv
new file mode 100644
index 0000000000000000000000000000000000000000..90865b1f69c6bb70ccafda511864f4c65f04634c
--- /dev/null
+++ b/Testes_R/Result_COHEND/.ipynb_checkpoints/COHEND_subsequente-checkpoint.csv
@@ -0,0 +1,630 @@
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diff --git a/Testes_R/Result_COHEND/COHEND_subsequente.csv b/Testes_R/Result_COHEND/COHEND_subsequente.csv
new file mode 100644
index 0000000000000000000000000000000000000000..90865b1f69c6bb70ccafda511864f4c65f04634c
--- /dev/null
+++ b/Testes_R/Result_COHEND/COHEND_subsequente.csv
@@ -0,0 +1,630 @@
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diff --git a/Testes_R/Result_F/.ipynb_checkpoints/F_subsequente-checkpoint.csv b/Testes_R/Result_F/.ipynb_checkpoints/F_subsequente-checkpoint.csv
index 3e200684ba0f85ca32f32e81886b77ef7edb5e2f..8a424a73c73096d32d930dc573047607d2b42335 100644
--- a/Testes_R/Result_F/.ipynb_checkpoints/F_subsequente-checkpoint.csv
+++ b/Testes_R/Result_F/.ipynb_checkpoints/F_subsequente-checkpoint.csv
@@ -1,4 +1,4 @@
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+"coluna1","ano_coluna1","coluna2","ano_coluna2","tamanho_amostra1","estatistica_f","p_valor"
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diff --git a/Testes_R/Result_F/F_acumulado.csv b/Testes_R/Result_F/F_acumulado.csv
deleted file mode 100644
index 036ad8396d3c01cb621ee9ee716d29735a7bd2ca..0000000000000000000000000000000000000000
--- a/Testes_R/Result_F/F_acumulado.csv
+++ /dev/null
@@ -1,906 +0,0 @@
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-"NUM_COMPUTADOR","2007","NUM_SALAS","2008","91705","2.78071733181179","0"
-"NUM_COMPUTADOR","2007","NUM_SALAS_UTILIZADAS","2008","91705","3.19549276092199","0"
-"NUM_COMPUTADOR_ADM","2007","ANO_CENSO","2008","74158","Inf","0"
-"NUM_COMPUTADOR_ADM","2007","NUM_COMPUTADOR","2008","74158","0.0284353946044987","0"
-"NUM_COMPUTADOR_ADM","2007","NUM_COMPUTADOR_ADM","2008","74158","0.933843016706879","1.12999697640713e-22"
-"NUM_COMPUTADOR_ADM","2007","NUM_COMPUTADPR_ALUNO","2008","74158","0.0413772931384778","0"
-"NUM_COMPUTADOR_ADM","2007","NUM_FUNCIONARIOS","2008","74158","0.00528313621595065","0"
-"NUM_COMPUTADOR_ADM","2007","NUM_SALAS","2008","74158","0.0904938748693735","0"
-"NUM_COMPUTADOR_ADM","2007","NUM_SALAS_UTILIZADAS","2008","74158","0.103992059439012","0"
-"NUM_COMPUTADPR_ALUNO","2007","ANO_CENSO","2008","44225","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_COMPUTADOR","2008","44225","0.680937338811909","0"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_COMPUTADOR_ADM","2008","44225","22.3625726848139","0"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_COMPUTADPR_ALUNO","2008","44225","0.990854681949717","0.307243158593186"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_FUNCIONARIOS","2008","44225","0.126514323627536","0"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_SALAS","2008","44225","2.16704073178503","0"
-"NUM_COMPUTADPR_ALUNO","2007","NUM_SALAS_UTILIZADAS","2008","44225","2.49027935771174","0"
-"NUM_FUNCIONARIOS","2007","ANO_CENSO","2008","187765","Inf","0"
-"NUM_FUNCIONARIOS","2007","NUM_COMPUTADOR","2008","187765","5.12423033487457","0"
-"NUM_FUNCIONARIOS","2007","NUM_COMPUTADOR_ADM","2008","187765","168.284167699333","0"
-"NUM_FUNCIONARIOS","2007","NUM_COMPUTADPR_ALUNO","2008","187765","7.45643883702745","0"
-"NUM_FUNCIONARIOS","2007","NUM_FUNCIONARIOS","2008","187765","0.952053144947942","1.26412040218539e-26"
-"NUM_FUNCIONARIOS","2007","NUM_SALAS","2008","187765","16.3075443536354","0"
-"NUM_FUNCIONARIOS","2007","NUM_SALAS_UTILIZADAS","2008","187765","18.7399989687205","0"
-"NUM_SALAS","2007","ANO_CENSO","2008","191233","Inf","0"
-"NUM_SALAS","2007","NUM_COMPUTADOR","2008","191233","0.311808681990297","0"
-"NUM_SALAS","2007","NUM_COMPUTADOR_ADM","2008","191233","10.2400675030248","0"
-"NUM_SALAS","2007","NUM_COMPUTADPR_ALUNO","2008","191233","0.453723235329878","0"
-"NUM_SALAS","2007","NUM_FUNCIONARIOS","2008","191233","0.0579322975180431","0"
-"NUM_SALAS","2007","NUM_SALAS","2008","191233","0.992311738369549","0.0907376011368406"
-"NUM_SALAS","2007","NUM_SALAS_UTILIZADAS","2008","191233","1.14032625332392","0"
-"NUM_SALAS_UTILIZADAS","2007","ANO_CENSO","2008","189299","Inf","0"
-"NUM_SALAS_UTILIZADAS","2007","NUM_COMPUTADOR","2008","189299","0.270972503545125","0"
-"NUM_SALAS_UTILIZADAS","2007","NUM_COMPUTADOR_ADM","2008","189299","8.89897199158828","0"
-"NUM_SALAS_UTILIZADAS","2007","NUM_COMPUTADPR_ALUNO","2008","189299","0.394301147130203","0"
-"NUM_SALAS_UTILIZADAS","2007","NUM_FUNCIONARIOS","2008","189299","0.0503451654853973","0"
-"NUM_SALAS_UTILIZADAS","2007","NUM_SALAS","2008","189299","0.862353140159127","1.33353552168743e-229"
-"NUM_SALAS_UTILIZADAS","2007","NUM_SALAS_UTILIZADAS","2008","189299","0.990982860865397","0.048245517890925"
-"ANO_CENSO","2008","ANO_CENSO","2009","487737","Inf","0"
-"ANO_CENSO","2008","NUM_COMPUTADOR","2009","487737","0.00341706534235493","0"
-"ANO_CENSO","2008","NUM_COMPUTADOR_ADM","2009","487737","0.0828025331097315","0"
-"ANO_CENSO","2008","NUM_COMPUTADPR_ALUNO","2009","487737","0.0056243182725756","0"
-"ANO_CENSO","2008","NUM_FUNCIONARIOS","2009","487737","0.000719737113691783","0"
-"ANO_CENSO","2008","NUM_SALAS","2009","487737","0.0125688357709998","0"
-"ANO_CENSO","2008","NUM_SALAS_UTILIZADAS","2009","487737","0.0127306403267224","0"
-"NUM_COMPUTADOR","2008","ANO_CENSO","2009","194397","Inf","0"
-"NUM_COMPUTADOR","2008","NUM_COMPUTADOR","2009","194397","0.786412628676249","0"
-"NUM_COMPUTADOR","2008","NUM_COMPUTADOR_ADM","2009","194397","19.0563981662111","0"
-"NUM_COMPUTADOR","2008","NUM_COMPUTADPR_ALUNO","2009","194397","1.29439576774374","0"
-"NUM_COMPUTADOR","2008","NUM_FUNCIONARIOS","2009","194397","0.165642239414753","0"
-"NUM_COMPUTADOR","2008","NUM_SALAS","2009","194397","2.89262574395492","0"
-"NUM_COMPUTADOR","2008","NUM_SALAS_UTILIZADAS","2009","194397","2.92986388055722","0"
-"NUM_COMPUTADOR_ADM","2008","ANO_CENSO","2009","166399","Inf","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_COMPUTADOR","2009","166399","0.0254207204504243","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_COMPUTADOR_ADM","2009","166399","0.615996428478848","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_COMPUTADPR_ALUNO","2009","166399","0.0418412316437661","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_FUNCIONARIOS","2009","166399","0.00535437111435069","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_SALAS","2009","166399","0.0935038778923916","0"
-"NUM_COMPUTADOR_ADM","2008","NUM_SALAS_UTILIZADAS","2009","166399","0.0947075974489497","0"
-"NUM_COMPUTADPR_ALUNO","2008","ANO_CENSO","2009","100316","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_COMPUTADOR","2009","100316","0.589052756836448","0"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_COMPUTADOR_ADM","2009","100316","14.2739618691969","0"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_COMPUTADPR_ALUNO","2009","100316","0.969551311390211","1.30879128076883e-05"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_FUNCIONARIOS","2009","100316","0.124072292608098","0"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_SALAS","2009","100316","2.16668591886791","0"
-"NUM_COMPUTADPR_ALUNO","2008","NUM_SALAS_UTILIZADAS","2009","100316","2.19457868943794","0"
-"NUM_FUNCIONARIOS","2008","ANO_CENSO","2009","378843","Inf","0"
-"NUM_FUNCIONARIOS","2008","NUM_COMPUTADOR","2009","378843","4.60083986492936","0"
-"NUM_FUNCIONARIOS","2008","NUM_COMPUTADOR_ADM","2009","378843","111.487828613145","0"
-"NUM_FUNCIONARIOS","2008","NUM_COMPUTADPR_ALUNO","2009","378843","7.57275179984796","0"
-"NUM_FUNCIONARIOS","2008","NUM_FUNCIONARIOS","2009","378843","0.969075763315735","2.84814843796539e-15"
-"NUM_FUNCIONARIOS","2008","NUM_SALAS","2009","378843","16.9230596659042","0"
-"NUM_FUNCIONARIOS","2008","NUM_SALAS_UTILIZADAS","2009","378843","17.1409182011415","0"
-"NUM_SALAS","2008","ANO_CENSO","2009","384292","Inf","0"
-"NUM_SALAS","2008","NUM_COMPUTADOR","2009","384292","0.269191099244773","0"
-"NUM_SALAS","2008","NUM_COMPUTADOR_ADM","2009","384292","6.52305492428747","0"
-"NUM_SALAS","2008","NUM_COMPUTADPR_ALUNO","2009","384292","0.44307505611048","0"
-"NUM_SALAS","2008","NUM_FUNCIONARIOS","2009","384292","0.056699771701886","0"
-"NUM_SALAS","2008","NUM_SALAS","2009","384292","0.990153356298044","0.0123484887081914"
-"NUM_SALAS","2008","NUM_SALAS_UTILIZADAS","2009","384292","1.00290006783379","0.464110113159391"
-"NUM_SALAS_UTILIZADAS","2008","ANO_CENSO","2009","380424","Inf","0"
-"NUM_SALAS_UTILIZADAS","2008","NUM_COMPUTADOR","2009","380424","0.234101699871625","0"
-"NUM_SALAS_UTILIZADAS","2008","NUM_COMPUTADOR_ADM","2009","380424","5.67276648602383","0"
-"NUM_SALAS_UTILIZADAS","2008","NUM_COMPUTADPR_ALUNO","2009","380424","0.385319663603969","0"
-"NUM_SALAS_UTILIZADAS","2008","NUM_FUNCIONARIOS","2009","380424","0.0493088849333575","0"
-"NUM_SALAS_UTILIZADAS","2008","NUM_SALAS","2009","380424","0.861085617218701","2.40668437253216e-316"
-"NUM_SALAS_UTILIZADAS","2008","NUM_SALAS_UTILIZADAS","2009","380424","0.872170778825687","3.04003443910776e-265"
-"ANO_CENSO","2009","ANO_CENSO","2010","743182","Inf","0"
-"ANO_CENSO","2009","NUM_COMPUTADOR","2010","743182","0.00775331984693693","0"
-"ANO_CENSO","2009","NUM_COMPUTADOR_ADM","2010","743182","0.206147905489487","0"
-"ANO_CENSO","2009","NUM_COMPUTADPR_ALUNO","2010","743182","0.0122135928588385","0"
-"ANO_CENSO","2009","NUM_FUNCIONARIOS","2010","743182","0.00172326836574777","0"
-"ANO_CENSO","2009","NUM_SALAS","2010","743182","0.0330675333485313","0"
-"ANO_CENSO","2009","NUM_SALAS_UTILIZADAS","2010","743182","0.0335900863414798","0"
-"NUM_COMPUTADOR","2009","ANO_CENSO","2010","304935","Inf","0"
-"NUM_COMPUTADOR","2009","NUM_COMPUTADOR","2010","304935","0.740669227140714","0"
-"NUM_COMPUTADOR","2009","NUM_COMPUTADOR_ADM","2010","304935","19.6931653601131","0"
-"NUM_COMPUTADOR","2009","NUM_COMPUTADPR_ALUNO","2010","304935","1.16675599123402","0"
-"NUM_COMPUTADOR","2009","NUM_FUNCIONARIOS","2010","304935","0.164622622800566","0"
-"NUM_COMPUTADOR","2009","NUM_SALAS","2010","304935","3.15891835397226","0"
-"NUM_COMPUTADOR","2009","NUM_SALAS_UTILIZADAS","2010","304935","3.20883747624092","0"
-"NUM_COMPUTADOR_ADM","2009","ANO_CENSO","2010","266874","Inf","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_COMPUTADOR","2010","266874","0.0225906571329395","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_COMPUTADOR_ADM","2010","266874","0.600648076375497","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_COMPUTADPR_ALUNO","2010","266874","0.0355864447852417","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_FUNCIONARIOS","2010","266874","0.0050210446063345","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_SALAS","2010","266874","0.0963480577166999","0"
-"NUM_COMPUTADOR_ADM","2009","NUM_SALAS_UTILIZADAS","2010","266874","0.0978706075057628","0"
-"NUM_COMPUTADPR_ALUNO","2009","ANO_CENSO","2010","165866","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_COMPUTADOR","2010","165866","0.510889433516031","0"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_COMPUTADOR_ADM","2010","165866","13.5837020444408","0"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_COMPUTADPR_ALUNO","2010","165866","0.804790162153903","3.88544718394164e-283"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_FUNCIONARIOS","2010","165866","0.113551306608458","0"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_SALAS","2010","165866","2.17891867144857","0"
-"NUM_COMPUTADPR_ALUNO","2009","NUM_SALAS_UTILIZADAS","2010","165866","2.21335125101705","0"
-"NUM_FUNCIONARIOS","2009","ANO_CENSO","2010","567183","Inf","0"
-"NUM_FUNCIONARIOS","2009","NUM_COMPUTADOR","2010","567183","3.95879371152865","0"
-"NUM_FUNCIONARIOS","2009","NUM_COMPUTADOR_ADM","2010","567183","105.257753840633","0"
-"NUM_FUNCIONARIOS","2009","NUM_COMPUTADPR_ALUNO","2010","567183","6.23617954105724","0"
-"NUM_FUNCIONARIOS","2009","NUM_FUNCIONARIOS","2010","567183","0.879889402769021","3.54374292571268e-258"
-"NUM_FUNCIONARIOS","2009","NUM_SALAS","2010","567183","16.884063299368","0"
-"NUM_FUNCIONARIOS","2009","NUM_SALAS_UTILIZADAS","2010","567183","17.150875393189","0"
-"NUM_SALAS","2009","ANO_CENSO","2010","575494","Inf","0"
-"NUM_SALAS","2009","NUM_COMPUTADOR","2010","575494","0.231180863630999","0"
-"NUM_SALAS","2009","NUM_COMPUTADOR_ADM","2010","575494","6.14671544159359","0"
-"NUM_SALAS","2009","NUM_COMPUTADPR_ALUNO","2010","575494","0.364172896370215","0"
-"NUM_SALAS","2009","NUM_FUNCIONARIOS","2010","575494","0.0513827208120325","0"
-"NUM_SALAS","2009","NUM_SALAS","2010","575494","0.985975178191629","0.000164575481602551"
-"NUM_SALAS","2009","NUM_SALAS_UTILIZADAS","2010","575494","1.00155614925792","0.678581918246155"
-"NUM_SALAS_UTILIZADAS","2009","ANO_CENSO","2010","578133","Inf","0"
-"NUM_SALAS_UTILIZADAS","2009","NUM_COMPUTADOR","2010","578133","0.228090850165802","0"
-"NUM_SALAS_UTILIZADAS","2009","NUM_COMPUTADOR_ADM","2010","578133","6.06455711247007","0"
-"NUM_SALAS_UTILIZADAS","2009","NUM_COMPUTADPR_ALUNO","2010","578133","0.359305282607686","0"
-"NUM_SALAS_UTILIZADAS","2009","NUM_FUNCIONARIOS","2010","578133","0.0506959282432451","0"
-"NUM_SALAS_UTILIZADAS","2009","NUM_SALAS","2010","578133","0.972796420533621","1.70967675772133e-13"
-"NUM_SALAS_UTILIZADAS","2009","NUM_SALAS_UTILIZADAS","2010","578133","0.988169132967947","0.00145938341261703"
-"ANO_CENSO","2010","ANO_CENSO","2011","1003013","Inf","0"
-"ANO_CENSO","2010","NUM_COMPUTADOR","2011","1003013","0.0123192765754822","0"
-"ANO_CENSO","2010","NUM_COMPUTADOR_ADM","2011","1003013","0.369412493590629","0"
-"ANO_CENSO","2010","NUM_COMPUTADPR_ALUNO","2011","1003013","0.020288998020806","0"
-"ANO_CENSO","2010","NUM_FUNCIONARIOS","2011","1003013","0.00314314866896193","0"
-"ANO_CENSO","2010","NUM_SALAS","2011","1003013","0.0526242828504733","0"
-"ANO_CENSO","2010","NUM_SALAS_UTILIZADAS","2011","1003013","0.0624004252871628","0"
-"NUM_COMPUTADOR","2010","ANO_CENSO","2011","426175","Inf","0"
-"NUM_COMPUTADOR","2010","NUM_COMPUTADOR","2011","426175","0.716976295502779","0"
-"NUM_COMPUTADOR","2010","NUM_COMPUTADOR_ADM","2011","426175","21.4996391666519","0"
-"NUM_COMPUTADOR","2010","NUM_COMPUTADPR_ALUNO","2011","426175","1.18081046003721","0"
-"NUM_COMPUTADOR","2010","NUM_FUNCIONARIOS","2011","426175","0.182929823442056","0"
-"NUM_COMPUTADOR","2010","NUM_SALAS","2011","426175","3.06270933527978","0"
-"NUM_COMPUTADOR","2010","NUM_SALAS_UTILIZADAS","2011","426175","3.63167638018849","0"
-"NUM_COMPUTADOR_ADM","2010","ANO_CENSO","2011","387850","Inf","0"
-"NUM_COMPUTADOR_ADM","2010","NUM_COMPUTADOR","2011","387850","0.0292377201733839","0"
-"NUM_COMPUTADOR_ADM","2010","NUM_COMPUTADOR_ADM","2011","387850","0.876738098213535","5.16820167873912e-174"
-"NUM_COMPUTADOR_ADM","2010","NUM_COMPUTADPR_ALUNO","2011","387850","0.0481525066099466","0"
-"NUM_COMPUTADOR_ADM","2010","NUM_FUNCIONARIOS","2011","387850","0.00745973196424126","0"
-"NUM_COMPUTADOR_ADM","2010","NUM_SALAS","2011","387850","0.124894838335661","0"
-"NUM_COMPUTADOR_ADM","2010","NUM_SALAS_UTILIZADAS","2011","387850","0.148096859589729","0"
-"NUM_COMPUTADPR_ALUNO","2010","ANO_CENSO","2011","245074","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_COMPUTADOR","2011","245074","0.46997605843344","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_COMPUTADOR_ADM","2011","245074","14.0929564012972","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_COMPUTADPR_ALUNO","2011","245074","0.774018122002351","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_FUNCIONARIOS","2011","245074","0.119910013665005","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_SALAS","2011","245074","2.00759783907879","0"
-"NUM_COMPUTADPR_ALUNO","2010","NUM_SALAS_UTILIZADAS","2011","245074","2.38055422664974","0"
-"NUM_FUNCIONARIOS","2010","ANO_CENSO","2011","753766","Inf","0"
-"NUM_FUNCIONARIOS","2010","NUM_COMPUTADOR","2011","753766","3.43921826749119","0"
-"NUM_FUNCIONARIOS","2010","NUM_COMPUTADOR_ADM","2011","753766","103.130259996345","0"
-"NUM_FUNCIONARIOS","2010","NUM_COMPUTADPR_ALUNO","2011","753766","5.66415504958476","0"
-"NUM_FUNCIONARIOS","2010","NUM_FUNCIONARIOS","2011","753766","0.877484505969166","3.98923641485744e-286"
-"NUM_FUNCIONARIOS","2010","NUM_SALAS","2011","753766","14.691316798031","0"
-"NUM_FUNCIONARIOS","2010","NUM_SALAS_UTILIZADAS","2011","753766","17.420558847907","0"
-"NUM_SALAS","2010","ANO_CENSO","2011","763928","Inf","0"
-"NUM_SALAS","2010","NUM_COMPUTADOR","2011","763928","0.197097446638052","0"
-"NUM_SALAS","2010","NUM_COMPUTADOR_ADM","2011","763928","5.91027068811945","0"
-"NUM_SALAS","2010","NUM_COMPUTADPR_ALUNO","2011","763928","0.324605887386601","0"
-"NUM_SALAS","2010","NUM_FUNCIONARIOS","2011","763928","0.0502875776236026","0"
-"NUM_SALAS","2010","NUM_SALAS","2011","763928","0.84194162842561","0"
-"NUM_SALAS","2010","NUM_SALAS_UTILIZADAS","2011","763928","0.998351195207821","0.650320432403673"
-"NUM_SALAS_UTILIZADAS","2010","ANO_CENSO","2011","767566","Inf","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_COMPUTADOR","2011","767566","0.194366300941966","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_COMPUTADOR_ADM","2011","767566","5.828373075401","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_COMPUTADPR_ALUNO","2011","767566","0.320107878978161","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_FUNCIONARIOS","2011","767566","0.0495907512388068","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_SALAS","2011","767566","0.830274986903599","0"
-"NUM_SALAS_UTILIZADAS","2010","NUM_SALAS_UTILIZADAS","2011","767566","0.984517213000122","1.79993383379446e-05"
-"ANO_CENSO","2011","ANO_CENSO","2012","1260893","Inf","0"
-"ANO_CENSO","2011","NUM_COMPUTADOR","2012","1260893","0.0191340046973561","0"
-"ANO_CENSO","2011","NUM_COMPUTADOR_ADM","2012","1260893","0.55701275408687","0"
-"ANO_CENSO","2011","NUM_COMPUTADPR_ALUNO","2012","1260893","0.0311776384305419","0"
-"ANO_CENSO","2011","NUM_FUNCIONARIOS","2012","1260893","0.00462535393602012","0"
-"ANO_CENSO","2011","NUM_SALAS","2012","1260893","0.083969451891755","0"
-"ANO_CENSO","2011","NUM_SALAS_UTILIZADAS","2012","1260893","0.0847627801013678","0"
-"NUM_COMPUTADOR","2011","ANO_CENSO","2012","553793","Inf","0"
-"NUM_COMPUTADOR","2011","NUM_COMPUTADOR","2012","553793","0.748629703936526","0"
-"NUM_COMPUTADOR","2011","NUM_COMPUTADOR_ADM","2012","553793","21.7934666462448","0"
-"NUM_COMPUTADOR","2011","NUM_COMPUTADPR_ALUNO","2012","553793","1.21984428230655","0"
-"NUM_COMPUTADOR","2011","NUM_FUNCIONARIOS","2012","553793","0.180969818001709","0"
-"NUM_COMPUTADOR","2011","NUM_SALAS","2012","553793","3.28535645850045","0"
-"NUM_COMPUTADOR","2011","NUM_SALAS_UTILIZADAS","2012","553793","3.31639591271199","0"
-"NUM_COMPUTADOR_ADM","2011","ANO_CENSO","2012","503862","Inf","0"
-"NUM_COMPUTADOR_ADM","2011","NUM_COMPUTADOR","2012","503862","0.0295162288431656","0"
-"NUM_COMPUTADOR_ADM","2011","NUM_COMPUTADOR_ADM","2012","503862","0.859251169749201","1.14892347793861e-252"
-"NUM_COMPUTADOR_ADM","2011","NUM_COMPUTADPR_ALUNO","2012","503862","0.0480948094902739","0"
-"NUM_COMPUTADOR_ADM","2011","NUM_FUNCIONARIOS","2012","503862","0.0071350983453595","0"
-"NUM_COMPUTADOR_ADM","2011","NUM_SALAS","2012","503862","0.129531773252606","0"
-"NUM_COMPUTADOR_ADM","2011","NUM_SALAS_UTILIZADAS","2012","503862","0.130755566042094","0"
-"NUM_COMPUTADPR_ALUNO","2011","ANO_CENSO","2012","334442","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2011","NUM_COMPUTADOR","2012","334442","0.47522195952572","0"
-"NUM_COMPUTADPR_ALUNO","2011","NUM_COMPUTADOR_ADM","2012","334442","13.8342545988063","0"
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-"NUM_COMPUTADOR_ADM","2012","NUM_COMPUTADPR_ALUNO","2013","624228","0.0483388713452444","0"
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-"NUM_COMPUTADPR_ALUNO","2012","ANO_CENSO","2013","433248","Inf","0"
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-"NUM_COMPUTADPR_ALUNO","2012","NUM_COMPUTADOR_ADM","2013","433248","15.0315558333511","0"
-"NUM_COMPUTADPR_ALUNO","2012","NUM_COMPUTADPR_ALUNO","2013","433248","0.830939516778256","4.00707080453588e-315"
-"NUM_COMPUTADPR_ALUNO","2012","NUM_DVD","2013","433248","105.928130500602","0"
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-"NUM_COMPUTADPR_ALUNO","2012","NUM_SOM","2013","433248","33.8706822309167","0"
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-"NUM_FUNCIONARIOS","2012","NUM_COMPUTADPR_ALUNO","2013","1126778","5.69588010240138","0"
-"NUM_FUNCIONARIOS","2012","NUM_DVD","2013","1126778","726.110527445244","0"
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-"NUM_FUNCIONARIOS","2012","NUM_SOM","2013","1126778","232.174955069951","0"
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-"NUM_SALAS","2012","ANO_CENSO","2013","1135818","Inf","0"
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-"NUM_SALAS","2012","NUM_DVD","2013","1135818","39.1175266018862","0"
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-"NUM_SALAS","2012","NUM_SALAS","2013","1135818","0.928909984704728","8.80403837834288e-98"
-"NUM_SALAS","2012","NUM_SALAS_UTILIZADAS","2013","1135818","0.934276021669225","1.95382489351248e-83"
-"NUM_SALAS","2012","NUM_SOM","2013","1135818","12.507889140783","0"
-"NUM_SALAS","2012","NUM_TV","2013","1135818","13.0865867120872","0"
-"NUM_SALAS_UTILIZADAS","2012","ANO_CENSO","2013","1141209","Inf","0"
-"NUM_SALAS_UTILIZADAS","2012","NUM_COMPUTADOR","2013","1141209","0.421296460481443","0"
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-"NUM_SALAS_UTILIZADAS","2012","NUM_COMPUTADPR_ALUNO","2013","1141209","0.303203888475164","0"
-"NUM_SALAS_UTILIZADAS","2012","NUM_DVD","2013","1141209","38.6524174361274","0"
-"NUM_SALAS_UTILIZADAS","2012","NUM_FUNCIONARIOS","2013","1141209","0.0447663722694641","0"
-"NUM_SALAS_UTILIZADAS","2012","NUM_SALAS","2013","1141209","0.917865202849062","7.59333279640875e-132"
-"NUM_SALAS_UTILIZADAS","2012","NUM_SALAS_UTILIZADAS","2013","1141209","0.923167437390635","4.8675085405347e-115"
-"NUM_SALAS_UTILIZADAS","2012","NUM_SOM","2013","1141209","12.359169771512","0"
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-"ANO_CENSO","2013","ANO_CENSO","2014","1791660","Inf","0"
-"ANO_CENSO","2013","NUM_COMPUTADOR","2014","1791660","0.0850238241759003","0"
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-"ANO_CENSO","2013","NUM_COMPUTADPR_ALUNO","2014","1791660","0.0593997604138011","0"
-"ANO_CENSO","2013","NUM_DVD","2014","1791660","7.54079890667831","0"
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-"ANO_CENSO","2013","NUM_SALAS_UTILIZADAS","2014","1791660","0.185718476312415","0"
-"ANO_CENSO","2013","NUM_SOM","2014","1791660","2.34554421508087","0"
-"ANO_CENSO","2013","NUM_TV","2014","1791660","2.4550630017763","0"
-"NUM_COMPUTADOR","2013","ANO_CENSO","2014","943266","Inf","0"
-"NUM_COMPUTADOR","2013","NUM_COMPUTADOR","2014","943266","1.70449218337593","0"
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-"NUM_COMPUTADOR","2013","NUM_COMPUTADPR_ALUNO","2014","943266","1.19080067617595","0"
-"NUM_COMPUTADOR","2013","NUM_DVD","2014","943266","151.172132251447","0"
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-"NUM_COMPUTADOR","2013","NUM_SOM","2014","943266","47.021665034695","0"
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-"NUM_COMPUTADOR_ADM","2013","ANO_CENSO","2014","746575","Inf","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_COMPUTADOR","2014","746575","0.0692128038501851","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_COMPUTADOR_ADM","2014","746575","0.56116409690557","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_COMPUTADPR_ALUNO","2014","746575","0.0483537879660994","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_DVD","2014","746575","6.13851283049619","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_FUNCIONARIOS","2014","746575","0.0071440886301237","0"
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-"NUM_COMPUTADOR_ADM","2013","NUM_SALAS_UTILIZADAS","2014","746575","0.151182290339863","0"
-"NUM_COMPUTADOR_ADM","2013","NUM_SOM","2014","746575","1.90936708921103","0"
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-"NUM_COMPUTADPR_ALUNO","2013","ANO_CENSO","2014","531952","Inf","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_COMPUTADOR","2014","531952","1.19134354184532","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_COMPUTADOR_ADM","2014","531952","9.65918421988798","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_COMPUTADPR_ALUNO","2014","531952","0.832302259302506","1.97626258336499e-323"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_DVD","2014","531952","105.660762320449","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_FUNCIONARIOS","2014","531952","0.122969499549405","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_SALAS","2014","531952","2.58865247949713","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_SALAS_UTILIZADAS","2014","531952","2.60226482989531","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_SOM","2014","531952","32.8654818791518","0"
-"NUM_COMPUTADPR_ALUNO","2013","NUM_TV","2014","531952","34.4000458734788","0"
-"NUM_DVD","2013","ANO_CENSO","2014","239451","Inf","0"
-"NUM_DVD","2013","NUM_COMPUTADOR","2014","239451","0.010952687968431","0"
-"NUM_DVD","2013","NUM_COMPUTADOR_ADM","2014","239451","0.0888022867242456","0"
-"NUM_DVD","2013","NUM_COMPUTADPR_ALUNO","2014","239451","0.0076518205046384","0"
-"NUM_DVD","2013","NUM_DVD","2014","239451","0.971398525743373","1.18053721968283e-12"
-"NUM_DVD","2013","NUM_FUNCIONARIOS","2014","239451","0.00113052743469157","0"
-"NUM_DVD","2013","NUM_SALAS","2014","239451","0.0237989310981792","0"
-"NUM_DVD","2013","NUM_SALAS_UTILIZADAS","2014","239451","0.0239240770541452","0"
-"NUM_DVD","2013","NUM_SOM","2014","239451","0.302150769539498","0"
-"NUM_DVD","2013","NUM_TV","2014","239451","0.316258875226141","0"
-"NUM_FUNCIONARIOS","2013","ANO_CENSO","2014","1312805","Inf","0"
-"NUM_FUNCIONARIOS","2013","NUM_COMPUTADOR","2014","1312805","8.26724498687581","0"
-"NUM_FUNCIONARIOS","2013","NUM_COMPUTADOR_ADM","2014","1312805","67.0292317155544","0"
-"NUM_FUNCIONARIOS","2013","NUM_COMPUTADPR_ALUNO","2014","1312805","5.77570317804893","0"
-"NUM_FUNCIONARIOS","2013","NUM_DVD","2014","1312805","733.225452542575","0"
-"NUM_FUNCIONARIOS","2013","NUM_FUNCIONARIOS","2014","1312805","0.85333822107582","0"
-"NUM_FUNCIONARIOS","2013","NUM_SALAS","2014","1312805","17.9637723982937","0"
-"NUM_FUNCIONARIOS","2013","NUM_SALAS_UTILIZADAS","2014","1312805","18.058234349558","0"
-"NUM_FUNCIONARIOS","2013","NUM_SOM","2014","1312805","228.067707393468","0"
-"NUM_FUNCIONARIOS","2013","NUM_TV","2014","1312805","238.716706648116","0"
-"NUM_SALAS","2013","ANO_CENSO","2014","1319318","Inf","0"
-"NUM_SALAS","2013","NUM_COMPUTADOR","2014","1319318","0.429500568041371","0"
-"NUM_SALAS","2013","NUM_COMPUTADOR_ADM","2014","1319318","3.48230796872595","0"
-"NUM_SALAS","2013","NUM_COMPUTADPR_ALUNO","2014","1319318","0.300059790141505","0"
-"NUM_SALAS","2013","NUM_DVD","2014","1319318","38.0925869342643","0"
-"NUM_SALAS","2013","NUM_FUNCIONARIOS","2014","1319318","0.0443326950229863","0"
-"NUM_SALAS","2013","NUM_SALAS","2014","1319318","0.933255330098632","1.1931675389596e-86"
-"NUM_SALAS","2013","NUM_SALAS_UTILIZADAS","2014","1319318","0.938162824891727","2.42044372875171e-74"
-"NUM_SALAS","2013","NUM_SOM","2014","1319318","11.8485916448455","0"
-"NUM_SALAS","2013","NUM_TV","2014","1319318","12.4018292997359","0"
-"NUM_SALAS_UTILIZADAS","2013","ANO_CENSO","2014","1325212","Inf","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_COMPUTADOR","2014","1325212","0.425037595679841","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_COMPUTADOR_ADM","2014","1325212","3.4461230475054","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_COMPUTADPR_ALUNO","2014","1325212","0.296941846534783","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_DVD","2014","1325212","37.696764028972","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_FUNCIONARIOS","2014","1325212","0.0438720306902195","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_SALAS","2014","1325212","0.92355780452032","1.44728810085217e-114"
-"NUM_SALAS_UTILIZADAS","2013","NUM_SALAS_UTILIZADAS","2014","1325212","0.928414305169854","2.64945469663362e-100"
-"NUM_SALAS_UTILIZADAS","2013","NUM_SOM","2014","1325212","11.7254720474137","0"
-"NUM_SALAS_UTILIZADAS","2013","NUM_TV","2014","1325212","12.2729609686658","0"
-"NUM_SOM","2013","ANO_CENSO","2014","255124","Inf","0"
-"NUM_SOM","2013","NUM_COMPUTADOR","2014","255124","0.0342537464271482","0"
-"NUM_SOM","2013","NUM_COMPUTADOR_ADM","2014","255124","0.277722785527228","0"
-"NUM_SOM","2013","NUM_COMPUTADPR_ALUNO","2014","255124","0.0239305200720954","0"
-"NUM_SOM","2013","NUM_DVD","2014","255124","3.037979250064","0"
-"NUM_SOM","2013","NUM_FUNCIONARIOS","2014","255124","0.0035356435049073","0"
-"NUM_SOM","2013","NUM_SALAS","2014","255124","0.0744294508730517","0"
-"NUM_SOM","2013","NUM_SALAS_UTILIZADAS","2014","255124","0.0748208358786683","0"
-"NUM_SOM","2013","NUM_SOM","2014","255124","0.944954870658685","1.42701511465824e-46"
-"NUM_SOM","2013","NUM_TV","2014","255124","0.989076959788818","0.00539708719251382"
-"NUM_TV","2013","ANO_CENSO","2014","255839","Inf","0"
-"NUM_TV","2013","NUM_COMPUTADOR","2014","255839","0.0327390229701024","0"
-"NUM_TV","2013","NUM_COMPUTADOR_ADM","2014","255839","0.265441699173976","0"
-"NUM_TV","2013","NUM_COMPUTADPR_ALUNO","2014","255839","0.0228722965528199","0"
-"NUM_TV","2013","NUM_DVD","2014","255839","2.90363778636813","0"
-"NUM_TV","2013","NUM_FUNCIONARIOS","2014","255839","0.00337929499675141","0"
-"NUM_TV","2013","NUM_SALAS","2014","255839","0.0711381310353157","0"
-"NUM_TV","2013","NUM_SALAS_UTILIZADAS","2014","255839","0.0715122087355839","0"
-"NUM_TV","2013","NUM_SOM","2014","255839","0.903168337571548","1.12710257801398e-146"
-"NUM_TV","2013","NUM_TV","2014","255839","0.945339318564607","4.5780181828752e-46"
-"ANO_CENSO","2014","ANO_CENSO","2015","2064100","Inf","0"
-"ANO_CENSO","2014","NUM_COMPUTADOR","2015","2064100","0.0517505601529809","0"
-"ANO_CENSO","2014","NUM_COMPUTADOR_ADM","2015","2064100","1.45366621797589","0"
-"ANO_CENSO","2014","NUM_COMPUTADPR_ALUNO","2015","2064100","0.097272230968076","0"
-"ANO_CENSO","2014","NUM_DVD","2015","2064100","6.75071111363995","0"
-"ANO_CENSO","2014","NUM_FUNCIONARIOS","2015","2064100","0.0118571031040683","0"
-"ANO_CENSO","2014","NUM_SALAS","2015","2064100","0.241757132812232","0"
-"ANO_CENSO","2014","NUM_SALAS_UTILIZADAS","2015","2064100","0.246592059133719","0"
-"ANO_CENSO","2014","NUM_SOM","2015","2064100","1.87364028895372","0"
-"ANO_CENSO","2014","NUM_TV","2015","2064100","2.58065508842693","0"
-"NUM_COMPUTADOR","2014","ANO_CENSO","2015","1205734","Inf","0"
-"NUM_COMPUTADOR","2014","NUM_COMPUTADOR","2015","1205734","0.790579951828418","0"
-"NUM_COMPUTADOR","2014","NUM_COMPUTADOR_ADM","2015","1205734","22.2072836542192","0"
-"NUM_COMPUTADOR","2014","NUM_COMPUTADPR_ALUNO","2015","1205734","1.48600276877495","0"
-"NUM_COMPUTADOR","2014","NUM_DVD","2015","1205734","103.128871479889","0"
-"NUM_COMPUTADOR","2014","NUM_FUNCIONARIOS","2015","1205734","0.1811379040754","0"
-"NUM_COMPUTADOR","2014","NUM_SALAS","2015","1205734","3.69326132601989","0"
-"NUM_COMPUTADOR","2014","NUM_SALAS_UTILIZADAS","2015","1205734","3.76712324765005","0"
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diff --git a/Testes_R/Result_KS/.ipynb_checkpoints/KS_acumulado-checkpoint.csv b/Testes_R/Result_KS/.ipynb_checkpoints/KS_acumulado-checkpoint.csv
deleted file mode 100644
index 6e93d08273e139c8537f1e11b9cf44857a286264..0000000000000000000000000000000000000000
--- a/Testes_R/Result_KS/.ipynb_checkpoints/KS_acumulado-checkpoint.csv
+++ /dev/null
@@ -1,906 +0,0 @@
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diff --git a/Testes_R/Result_KS/KS_acumulado.csv b/Testes_R/Result_KS/KS_acumulado.csv
deleted file mode 100644
index d15f3833758091ced89a1661035d11bd09754598..0000000000000000000000000000000000000000
--- a/Testes_R/Result_KS/KS_acumulado.csv
+++ /dev/null
@@ -1,906 +0,0 @@
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diff --git a/Testes_R/Result_T/.ipynb_checkpoints/T_acumulado-checkpoint.csv b/Testes_R/Result_T/.ipynb_checkpoints/T_acumulado-checkpoint.csv
deleted file mode 100644
index bc958522695788173717094fa18f48433d1ee505..0000000000000000000000000000000000000000
--- a/Testes_R/Result_T/.ipynb_checkpoints/T_acumulado-checkpoint.csv
+++ /dev/null
@@ -1,694 +0,0 @@
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diff --git a/Testes_R/Result_T/T_acumulado.csv b/Testes_R/Result_T/T_acumulado.csv
deleted file mode 100644
index 9533e117464bdcd38193c3b436552a0187e46b77..0000000000000000000000000000000000000000
--- a/Testes_R/Result_T/T_acumulado.csv
+++ /dev/null
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-"NUM_TV","2020","NUM_SOM","2021","1364008","-71.293791720119","0"
-"NUM_TV","2020","NUM_TV","2021","1364008","-59.9088524108","0"
diff --git a/Testes_R/TESTE_COHEND.ipynb b/Testes_R/TESTE_COHEND.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ebb93aaf39290506d89dfd459cb3be801ecb4bb0
--- /dev/null
+++ b/Testes_R/TESTE_COHEND.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b977bf15-ee73-4730-8651-f8a3caffb393",
+ "metadata": {},
+ "source": [
+ "# Analise estatistica com Teste CohenD\n",
+ "Teste de effect size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36e8ccea-ef95-4da0-bca9-45bd2fbe5530",
+ "metadata": {},
+ "source": [
+ "## Instalação dos pacotes\n",
+ "Para fazer o jupyter-lab processar R após a instaçação, dentro do ambiente virtual do python e dentro seu client com sudo `sudo R`, instale os pacotes com o comando a seguir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "57a4780e-976b-4832-b04a-4680e62ccb5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))\n",
+ "#devtools::install_github('IRkernel/IRkernel')\n",
+ "#IRkernel::installspec()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"dataframe\">\n",
+ "<caption>A data.frame: 6 × 10</caption>\n",
+ "<thead>\n",
+ "\t<tr><th></th><th scope=col>ANO_CENSO</th><th scope=col>NUM_COMPUTADOR</th><th scope=col>NUM_COMPUTADOR_ADM</th><th scope=col>NUM_COMPUTADPR_ALUNO</th><th scope=col>NUM_DVD</th><th scope=col>NUM_FUNCIONARIOS</th><th scope=col>NUM_SALAS</th><th scope=col>NUM_SALAS_UTILIZADAS</th><th scope=col>NUM_SOM</th><th scope=col>NUM_TV</th></tr>\n",
+ "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n",
+ "</thead>\n",
+ "<tbody>\n",
+ "\t<tr><th scope=row>1</th><td>2013</td><td>165</td><td>35</td><td>130</td><td>1</td><td>42</td><td> 8</td><td> 8</td><td>1</td><td>3</td></tr>\n",
+ "\t<tr><th scope=row>2</th><td>2013</td><td> 15</td><td> 4</td><td> 11</td><td>5</td><td>16</td><td> 5</td><td> 5</td><td>7</td><td>6</td></tr>\n",
+ "\t<tr><th scope=row>3</th><td>2013</td><td> 25</td><td> 5</td><td> 20</td><td>2</td><td>40</td><td> 8</td><td> 8</td><td>3</td><td>3</td></tr>\n",
+ "\t<tr><th scope=row>4</th><td>2013</td><td> 15</td><td> 8</td><td> 7</td><td>2</td><td>45</td><td>10</td><td>10</td><td>2</td><td>4</td></tr>\n",
+ "\t<tr><th scope=row>5</th><td>2013</td><td> 16</td><td> 8</td><td> 8</td><td>2</td><td>47</td><td>12</td><td>12</td><td>1</td><td>6</td></tr>\n",
+ "\t<tr><th scope=row>6</th><td>2013</td><td> 10</td><td> 7</td><td> 3</td><td>1</td><td>25</td><td> 4</td><td> 4</td><td>0</td><td>1</td></tr>\n",
+ "</tbody>\n",
+ "</table>\n"
+ ],
+ "text/latex": [
+ "A data.frame: 6 × 10\n",
+ "\\begin{tabular}{r|llllllllll}\n",
+ " & ANO\\_CENSO & NUM\\_COMPUTADOR & NUM\\_COMPUTADOR\\_ADM & NUM\\_COMPUTADPR\\_ALUNO & NUM\\_DVD & NUM\\_FUNCIONARIOS & NUM\\_SALAS & NUM\\_SALAS\\_UTILIZADAS & NUM\\_SOM & NUM\\_TV\\\\\n",
+ " & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
+ "\\hline\n",
+ "\t1 & 2013 & 165 & 35 & 130 & 1 & 42 & 8 & 8 & 1 & 3\\\\\n",
+ "\t2 & 2013 & 15 & 4 & 11 & 5 & 16 & 5 & 5 & 7 & 6\\\\\n",
+ "\t3 & 2013 & 25 & 5 & 20 & 2 & 40 & 8 & 8 & 3 & 3\\\\\n",
+ "\t4 & 2013 & 15 & 8 & 7 & 2 & 45 & 10 & 10 & 2 & 4\\\\\n",
+ "\t5 & 2013 & 16 & 8 & 8 & 2 & 47 & 12 & 12 & 1 & 6\\\\\n",
+ "\t6 & 2013 & 10 & 7 & 3 & 1 & 25 & 4 & 4 & 0 & 1\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "A data.frame: 6 × 10\n",
+ "\n",
+ "| <!--/--> | ANO_CENSO <int> | NUM_COMPUTADOR <int> | NUM_COMPUTADOR_ADM <int> | NUM_COMPUTADPR_ALUNO <int> | NUM_DVD <int> | NUM_FUNCIONARIOS <int> | NUM_SALAS <int> | NUM_SALAS_UTILIZADAS <int> | NUM_SOM <int> | NUM_TV <int> |\n",
+ "|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| 1 | 2013 | 165 | 35 | 130 | 1 | 42 | 8 | 8 | 1 | 3 |\n",
+ "| 2 | 2013 | 15 | 4 | 11 | 5 | 16 | 5 | 5 | 7 | 6 |\n",
+ "| 3 | 2013 | 25 | 5 | 20 | 2 | 40 | 8 | 8 | 3 | 3 |\n",
+ "| 4 | 2013 | 15 | 8 | 7 | 2 | 45 | 10 | 10 | 2 | 4 |\n",
+ "| 5 | 2013 | 16 | 8 | 8 | 2 | 47 | 12 | 12 | 1 | 6 |\n",
+ "| 6 | 2013 | 10 | 7 | 3 | 1 | 25 | 4 | 4 | 0 | 1 |\n",
+ "\n"
+ ],
+ "text/plain": [
+ " ANO_CENSO NUM_COMPUTADOR NUM_COMPUTADOR_ADM NUM_COMPUTADPR_ALUNO NUM_DVD\n",
+ "1 2013 165 35 130 1 \n",
+ "2 2013 15 4 11 5 \n",
+ "3 2013 25 5 20 2 \n",
+ "4 2013 15 8 7 2 \n",
+ "5 2013 16 8 8 2 \n",
+ "6 2013 10 7 3 1 \n",
+ " NUM_FUNCIONARIOS NUM_SALAS NUM_SALAS_UTILIZADAS NUM_SOM NUM_TV\n",
+ "1 42 8 8 1 3 \n",
+ "2 16 5 5 7 6 \n",
+ "3 40 8 8 3 3 \n",
+ "4 45 10 10 2 4 \n",
+ "5 47 12 12 1 6 \n",
+ "6 25 4 4 0 1 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Carrega CSV\n",
+ "options(warn = -1)\n",
+ "library(effsize)\n",
+ "df = read.csv(\"../dados/escola-integers-ks.csv\", sep=\"|\")\n",
+ "head(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c7eca10-6c0b-427d-be50-c4bf3d224f2d",
+ "metadata": {},
+ "source": [
+ "## Limpeza de Outliers\n",
+ "Para limpar dados que se diferenciam drasticamente do resto do conjunto de dados é utilizado a remoção dos outliers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Function to remove outliers using the IQR method\n",
+ "remove_outliers <- function(data) {\n",
+ " # Calculate the interquartile range (IQR)\n",
+ " Q1 <- quantile(data, 0.25)\n",
+ " Q3 <- quantile(data, 0.75)\n",
+ " IQR <- Q3 - Q1\n",
+ " \n",
+ " # Define the lower and upper bounds\n",
+ " lower_bound <- Q1 - 1.5 * IQR\n",
+ " upper_bound <- Q3 + 1.5 * IQR\n",
+ " \n",
+ " # Remove outliers\n",
+ " cleaned_data <- data[data >= lower_bound & data <= upper_bound]\n",
+ " \n",
+ " # Return the cleaned data\n",
+ " return(cleaned_data)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ca6c624-62c0-4fce-b3cd-c47d20c0b85e",
+ "metadata": {},
+ "source": [
+ "## Plot de Grafico\n",
+ "Funções para plot de gráficos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "4c578156-afa1-4432-acb3-d6a143c0b349",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_ecdf <- function(x, x2){\n",
+ " plot(ecdf(x),\n",
+ " xlim = range(c(x, x2)), \n",
+ " col = \"blue\")\n",
+ " plot(ecdf(x2), \n",
+ " add = TRUE, \n",
+ " lty = \"dashed\",\n",
+ " col = \"red\")\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69c80406-3d20-408d-8fce-e4a7aea65dd4",
+ "metadata": {},
+ "source": [
+ "## Teste Cohen D\n",
+ "Utilizando testes de effect size é possivel determinar a magnitude da relação entre duas amostras\n",
+ "\n",
+ "Primeiro é selecionado duas amostras que foram agrupadas pelos anos do censo. \n",
+ "Em seguida é feito uma limpeza dos dados fazendo a retirada dos nulos, outliers e os ordenando. \n",
+ "Finalmente é executado o teste."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "a9629868-ef70-4073-856b-fa3cee413898",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1] -0.03455845\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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KMR7AAAqXB2llWr5M03xdU1+WCFCrJhg9SooV1bAFLFGjsAQOq8vGTGDJk0SQ4f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\nhCHmHljmmQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "Plot with title “ecdf(x)”"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 420,
+ "width": 420
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Dados agrupados por ano\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "# Amostras\n",
+ "X <- df[\"NUM_SALAS\"]\n",
+ "Xi <- X[ANO == 2013]\n",
+ "\n",
+ "Y <- df[\"NUM_SALAS\"]\n",
+ "Yi <- Y[ANO == 2014]\n",
+ "\n",
+ "# Limpeza dos dados\n",
+ "data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ "data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ "# Executa teste Cohen D\n",
+ "#print(cohen.d(data1, data2))\n",
+ "result = cohen.d(data1, data2)\n",
+ "print(result$estimate)\n",
+ "\n",
+ "# Plotagem dos dados\n",
+ "plot_ecdf(data1, data2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6c8c14d-5101-4201-b172-28e5b17016df",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos subsequentes\n",
+ "Execução do teste T entre uma coluna de um ano contra todas as colunas dos anos subsequentes para todos os anos do csv."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9aa86d14-0e60-4945-9d5f-d058b3c45654",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0));\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra de um ano\n",
+ " X <- df[col1]\n",
+ " Xi <- X[ANO == ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " Yi <- Y[ANO == ano+1]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ " data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ "\n",
+ " # Teste Cohen D\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_subsequente.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d52a6568-693a-4d59-b050-2f8d6899433e",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos acumulados\n",
+ "O mesmo teste anterior, porém com o acumulo da amostra conforme o passar dos anos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd76fa5b-a42c-434a-b3df-83f12f80a262",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
+ "ano_start = min(df$ANO_CENSO)\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO_COLUMN <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra acumulada dos anos\n",
+ " X <- df[col1]\n",
+ " Xi <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " Yi <- Y[ANO_COLUMN == ano+1]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- sort(remove_outliers(na.omit(Xi)))\n",
+ " data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ "\n",
+ " # Teste Cohend\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_acumulado.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "4.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Testes_R/TESTE_F.ipynb b/Testes_R/TESTE_F.ipynb
index b6cb3a68b695d4cfc0b00b8c73f20e4fa5dc7cfd..192a07ef31219531a89fc1e59b685240c5b39a96 100644
--- a/Testes_R/TESTE_F.ipynb
+++ b/Testes_R/TESTE_F.ipynb
@@ -32,7 +32,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 1,
"id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
"metadata": {},
"outputs": [
@@ -122,7 +122,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 2,
"id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
"metadata": {},
"outputs": [],
@@ -157,7 +157,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 3,
"id": "4c578156-afa1-4432-acb3-d6a143c0b349",
"metadata": {},
"outputs": [],
@@ -262,7 +262,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 4,
"id": "9aa86d14-0e60-4945-9d5f-d058b3c45654",
"metadata": {},
"outputs": [],
diff --git a/analise_resultados_muriki.ipynb b/analise_resultados_muriki.ipynb
index aa21f88ce25ea1e9e88c3efaf14169a07e3f0e3f..2ad62a19ea61027fb525d7fad2bda3433df5397a 100644
--- a/analise_resultados_muriki.ipynb
+++ b/analise_resultados_muriki.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 1,
"id": "e13d5b4a-95a3-404c-bf01-7b4b48b4d121",
"metadata": {},
"outputs": [],
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 4,
"id": "3ee220b5-c632-415e-8a0e-76e85f31b5f1",
"metadata": {
"scrolled": true
@@ -45,8 +45,8 @@
" <th>ano_coluna1</th>\n",
" <th>coluna2</th>\n",
" <th>ano_coluna2</th>\n",
- " <th>tamanho_amostra</th>\n",
- " <th>estatistica_ks</th>\n",
+ " <th>tamanho_amostra1</th>\n",
+ " <th>estatistica_f</th>\n",
" <th>p_valor</th>\n",
" </tr>\n",
" </thead>\n",
@@ -58,8 +58,8 @@
" <td>NUM_SALAS</td>\n",
" <td>2008</td>\n",
" <td>191233</td>\n",
- " <td>0.014694</td>\n",
- " <td>2.337802e-18</td>\n",
+ " <td>0.007688</td>\n",
+ " <td>9.073760e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
@@ -68,28 +68,18 @@
" <td>NUM_SALAS_UTILIZADAS</td>\n",
" <td>2008</td>\n",
" <td>189299</td>\n",
- " <td>0.015293</td>\n",
- " <td>1.183242e-19</td>\n",
+ " <td>0.009017</td>\n",
+ " <td>4.824552e-02</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>41</th>\n",
- " <td>NUM_SALAS</td>\n",
- " <td>2007</td>\n",
- " <td>NUM_SALAS_UTILIZADAS</td>\n",
- " <td>2008</td>\n",
- " <td>191125</td>\n",
- " <td>0.015560</td>\n",
- " <td>1.596687e-20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>NUM_COMPUTADOR_ADM</td>\n",
+ " <th>24</th>\n",
+ " <td>NUM_COMPUTADPR_ALUNO</td>\n",
" <td>2007</td>\n",
- " <td>NUM_COMPUTADOR_ADM</td>\n",
+ " <td>NUM_COMPUTADPR_ALUNO</td>\n",
" <td>2008</td>\n",
- " <td>74158</td>\n",
- " <td>0.015575</td>\n",
- " <td>3.079822e-08</td>\n",
+ " <td>44225</td>\n",
+ " <td>0.009145</td>\n",
+ " <td>3.072432e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
@@ -98,8 +88,18 @@
" <td>NUM_FUNCIONARIOS</td>\n",
" <td>2008</td>\n",
" <td>187765</td>\n",
- " <td>0.022613</td>\n",
- " <td>3.996180e-42</td>\n",
+ " <td>0.047947</td>\n",
+ " <td>1.264120e-26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>NUM_COMPUTADOR_ADM</td>\n",
+ " <td>2007</td>\n",
+ " <td>NUM_COMPUTADOR_ADM</td>\n",
+ " <td>2008</td>\n",
+ " <td>74158</td>\n",
+ " <td>0.066157</td>\n",
+ " <td>1.129997e-22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -109,34 +109,48 @@
" coluna1 ano_coluna1 coluna2 ano_coluna2 \\\n",
"40 NUM_SALAS 2007 NUM_SALAS 2008 \n",
"48 NUM_SALAS_UTILIZADAS 2007 NUM_SALAS_UTILIZADAS 2008 \n",
- "41 NUM_SALAS 2007 NUM_SALAS_UTILIZADAS 2008 \n",
- "16 NUM_COMPUTADOR_ADM 2007 NUM_COMPUTADOR_ADM 2008 \n",
+ "24 NUM_COMPUTADPR_ALUNO 2007 NUM_COMPUTADPR_ALUNO 2008 \n",
"32 NUM_FUNCIONARIOS 2007 NUM_FUNCIONARIOS 2008 \n",
+ "16 NUM_COMPUTADOR_ADM 2007 NUM_COMPUTADOR_ADM 2008 \n",
"\n",
- " tamanho_amostra estatistica_ks p_valor \n",
- "40 191233 0.014694 2.337802e-18 \n",
- "48 189299 0.015293 1.183242e-19 \n",
- "41 191125 0.015560 1.596687e-20 \n",
- "16 74158 0.015575 3.079822e-08 \n",
- "32 187765 0.022613 3.996180e-42 "
+ " tamanho_amostra1 estatistica_f p_valor \n",
+ "40 191233 0.007688 9.073760e-02 \n",
+ "48 189299 0.009017 4.824552e-02 \n",
+ "24 44225 0.009145 3.072432e-01 \n",
+ "32 187765 0.047947 1.264120e-26 \n",
+ "16 74158 0.066157 1.129997e-22 "
]
},
- "execution_count": 40,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importa CSV, ordena por ano e metrica\n",
- "df = pd.read_csv('Testes_R/Result_KS/KS_subsequente.csv', sep=',')\n",
- "df = df.sort_values(by=['ano_coluna1', 'estatistica_ks'])\n",
- "#df = df[~df['coluna1'].str.contains('ANO_CENSO')]\n",
+ "df = pd.read_csv('Testes_R/Result_F/F_subsequente.csv', sep=',')\n",
+ "\n",
+ "# Coluna contendo a estatistica a ser analisada\n",
+ "stat_column = 'estatistica_f'\n",
+ "\n",
+ "# Tira absoluto pro teste F\n",
+ "df[stat_column] = (df[stat_column] - 1).abs()\n",
+ "\n",
+ "# Tira absoluto pro teste Cohen\n",
+ "#df[stat_column] = df[stat_column].abs()\n",
+ "\n",
+ "# Ordena para buscar os mais proximos a 0\n",
+ "df = df.sort_values(by=['ano_coluna1', stat_column])\n",
+ "\n",
+ "# Remove coluna ANO_CENSO\n",
+ "df = df[~df['coluna1'].str.contains('ANO_CENSO') & ~df['coluna2'].str.contains('ANO_CENSO')]\n",
+ "\n",
"df.head()"
]
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 5,
"id": "ae5ebf5b-a2b3-4d87-af13-62fc187b3ecc",
"metadata": {},
"outputs": [
@@ -152,16 +166,14 @@
"+----------------------+----------------------+\n",
"| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
+ "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
+ "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
"ACURACIA: 1.0\n",
"\n",
"\n",
@@ -173,21 +185,19 @@
"+----------------------+----------------------+\n",
"| BASE | 2009 |\n",
"+======================+======================+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
+ "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.7142857142857143\n",
+ "ACURACIA: 1.0\n",
"\n",
"\n",
"\n",
@@ -198,21 +208,19 @@
"+----------------------+----------------------+\n",
"| BASE | 2010 |\n",
"+======================+======================+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
+ "+----------------------+----------------------+\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
- "+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.7142857142857143\n",
+ "ACURACIA: 1.0\n",
"\n",
"\n",
"\n",
@@ -223,21 +231,19 @@
"+----------------------+----------------------+\n",
"| BASE | 2011 |\n",
"+======================+======================+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
+ "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.7142857142857143\n",
+ "ACURACIA: 1.0\n",
"\n",
"\n",
"\n",
@@ -248,21 +254,19 @@
"+----------------------+----------------------+\n",
"| BASE | 2012 |\n",
"+======================+======================+\n",
- "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
- "+----------------------+----------------------+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
+ "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
+ "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.7142857142857143\n",
+ "ACURACIA: 1.0\n",
"\n",
"\n",
"\n",
@@ -277,51 +281,47 @@
"+----------------------+----------------------+\n",
"| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS | NUM_SALAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 1.0\n",
+ "ACURACIA: 0.6666666666666666\n",
"\n",
"\n",
+ "COLUNA SEM NOVOS DADOS: NUM_COMPUTADOR\n",
"\n",
"\n",
+ "NOVAS COLUNAS: NUM_COMPUTADOR\n",
+ "NOVAS COLUNAS: NUM_SOM\n",
"NOVAS COLUNAS: NUM_TV\n",
"NOVAS COLUNAS: NUM_DVD\n",
- "NOVAS COLUNAS: NUM_SOM\n",
"\n",
"\n",
"ANO: 2013\n",
"+----------------------+----------------------+\n",
"| BASE | 2014 |\n",
"+======================+======================+\n",
- "| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_DVD | NUM_DVD |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
+ "| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "| NUM_SOM | NUM_TV |\n",
"+----------------------+----------------------+\n",
- "| NUM_SOM | NUM_SOM |\n",
+ "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_TV | NUM_TV |\n",
+ "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
+ "| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
+ "| NUM_TV | NUM_SOM |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
- "ACURACIA: 0.8\n",
+ "ACURACIA: 0.7777777777777778\n",
"\n",
"\n",
"\n",
@@ -332,27 +332,25 @@
"+----------------------+----------------------+\n",
"| BASE | 2015 |\n",
"+======================+======================+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
+ "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| NUM_TV | NUM_COMPUTADOR_ADM |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR_ADM | NUM_TV |\n",
+ "| NUM_SOM | NUM_TV |\n",
"+----------------------+----------------------+\n",
- "| NUM_DVD | NUM_SOM |\n",
- "+----------------------+----------------------+\n",
- "| NUM_SOM | NUM_DVD |\n",
+ "| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_TV | NUM_SOM |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.2\n",
+ "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
+ "+----------------------+----------------------+\n",
+ "ACURACIA: 0.5555555555555556\n",
"\n",
"\n",
"\n",
@@ -363,26 +361,24 @@
"+----------------------+----------------------+\n",
"| BASE | 2016 |\n",
"+======================+======================+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_TV | NUM_TV |\n",
- "+----------------------+----------------------+\n",
"| NUM_SOM | NUM_SOM |\n",
"+----------------------+----------------------+\n",
- "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
"| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS | NUM_SALAS |\n",
+ "| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
+ "+----------------------+----------------------+\n",
+ "| NUM_TV | NUM_TV |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
+ "+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
"ACURACIA: 1.0\n",
"\n",
"\n",
@@ -398,27 +394,25 @@
"+----------------------+----------------------+\n",
"| NUM_SOM | NUM_SOM |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
"| NUM_FUNCIONARIOS | NUM_FUNCIONARIOS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
+ "| NUM_SALAS | NUM_SALAS |\n",
"+----------------------+----------------------+\n",
"| NUM_COMPUTADOR | NUM_COMPUTADOR |\n",
"+----------------------+----------------------+\n",
- "| NUM_DVD | NUM_DVD |\n",
- "+----------------------+----------------------+\n",
- "| NUM_COMPUTADOR_ADM | NUM_COMPUTADOR_ADM |\n",
+ "| NUM_COMPUTADPR_ALUNO | NUM_COMPUTADPR_ALUNO |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
- "ACURACIA: 0.8\n",
+ "ACURACIA: 0.8888888888888888\n",
"\n",
"\n",
+ "COLUNA SEM NOVOS DADOS: NUM_COMPUTADOR_ADM\n",
"\n",
"\n",
+ "NOVAS COLUNAS: NUM_COMPUTADOR_ADM\n",
"\n",
"\n",
"ANO: 2017\n",
@@ -431,38 +425,34 @@
"+----------------------+----------------------+\n",
"| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
"ACURACIA: 1.0\n",
"\n",
"\n",
"COLUNA SEM NOVOS DADOS: NUM_COMPUTADOR\n",
- "COLUNA SEM NOVOS DADOS: NUM_COMPUTADPR_ALUNO\n",
- "COLUNA SEM NOVOS DADOS: NUM_COMPUTADOR_ADM\n",
"COLUNA SEM NOVOS DADOS: NUM_SOM\n",
+ "COLUNA SEM NOVOS DADOS: NUM_COMPUTADOR_ADM\n",
"COLUNA SEM NOVOS DADOS: NUM_TV\n",
+ "COLUNA SEM NOVOS DADOS: NUM_COMPUTADPR_ALUNO\n",
"COLUNA SEM NOVOS DADOS: NUM_DVD\n",
"\n",
"\n",
"\n",
"\n",
"ANO: 2018\n",
- "+----------------------+----------------------+\n",
- "| BASE | 2019 |\n",
- "+======================+======================+\n",
- "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SOM |\n",
- "+----------------------+----------------------+\n",
- "| NUM_FUNCIONARIOS | NUM_TV |\n",
- "+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
- "ACURACIA: 0.2857142857142857\n",
+ "+-----------+----------------------+\n",
+ "| BASE | 2019 |\n",
+ "+===========+======================+\n",
+ "| NUM_SALAS | NUM_SALAS_UTILIZADAS |\n",
+ "+-----------+----------------------+\n",
+ "ACURACIA: 0.6666666666666666\n",
"\n",
"\n",
+ "COLUNA SEM NOVOS DADOS: NUM_SALAS_UTILIZADAS\n",
+ "COLUNA SEM NOVOS DADOS: NUM_FUNCIONARIOS\n",
"\n",
"\n",
+ "NOVAS COLUNAS: NUM_SOM\n",
+ "NOVAS COLUNAS: NUM_TV\n",
"NOVAS COLUNAS: NUM_DVD\n",
"\n",
"\n",
@@ -470,16 +460,14 @@
"+----------------------+----------------------+\n",
"| BASE | 2020 |\n",
"+======================+======================+\n",
+ "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
+ "+----------------------+----------------------+\n",
"| NUM_SOM | NUM_SOM |\n",
"+----------------------+----------------------+\n",
"| NUM_TV | NUM_TV |\n",
"+----------------------+----------------------+\n",
- "| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
- "+----------------------+----------------------+\n",
"| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
- "+----------------------+----------------------+\n",
"ACURACIA: 1.0\n",
"\n",
"\n",
@@ -493,13 +481,11 @@
"+======================+======================+\n",
"| NUM_SALAS_UTILIZADAS | NUM_SALAS_UTILIZADAS |\n",
"+----------------------+----------------------+\n",
- "| NUM_SOM | NUM_SOM |\n",
+ "| NUM_DVD | NUM_DVD |\n",
"+----------------------+----------------------+\n",
"| NUM_TV | NUM_TV |\n",
"+----------------------+----------------------+\n",
- "| NUM_DVD | NUM_DVD |\n",
- "+----------------------+----------------------+\n",
- "| ANO_CENSO | ANO_CENSO |\n",
+ "| NUM_SOM | NUM_SOM |\n",
"+----------------------+----------------------+\n",
"ACURACIA: 1.0\n",
"\n",
@@ -536,7 +522,7 @@
" if row['coluna1'] in prev_col or row['coluna2'] in next_col:\n",
" continue\n",
" # Testa treshold\n",
- " if row['estatistica_ks'] > threshold:\n",
+ " if row[stat_column] > threshold:\n",
" break\n",
"\n",
" # Adiciona nas listas\n",