diff --git a/Testes_R/Result_COHEND/COHEND_acumulado.csv b/Testes_R/Resultados_outliers/COHEND_acumulado.csv
similarity index 100%
rename from Testes_R/Result_COHEND/COHEND_acumulado.csv
rename to Testes_R/Resultados_outliers/COHEND_acumulado.csv
diff --git a/Testes_R/Result_COHEND/COHEND_subsequente.csv b/Testes_R/Resultados_outliers/COHEND_subsequente.csv
similarity index 100%
rename from Testes_R/Result_COHEND/COHEND_subsequente.csv
rename to Testes_R/Resultados_outliers/COHEND_subsequente.csv
diff --git a/Testes_R/Result_F/F_acumulado.csv b/Testes_R/Resultados_outliers/F_acumulado.csv
similarity index 100%
rename from Testes_R/Result_F/F_acumulado.csv
rename to Testes_R/Resultados_outliers/F_acumulado.csv
diff --git a/Testes_R/Result_F/F_subsequente.csv b/Testes_R/Resultados_outliers/F_subsequente.csv
similarity index 100%
rename from Testes_R/Result_F/F_subsequente.csv
rename to Testes_R/Resultados_outliers/F_subsequente.csv
diff --git a/Testes_R/Result_KS/KS_acumulado.csv b/Testes_R/Resultados_outliers/KS_acumulado.csv
similarity index 100%
rename from Testes_R/Result_KS/KS_acumulado.csv
rename to Testes_R/Resultados_outliers/KS_acumulado.csv
diff --git a/Testes_R/Result_KS/KS_subsequente.csv b/Testes_R/Resultados_outliers/KS_subsequente.csv
similarity index 100%
rename from Testes_R/Result_KS/KS_subsequente.csv
rename to Testes_R/Resultados_outliers/KS_subsequente.csv
diff --git a/Testes_R/Result_T/T_acumulado.csv b/Testes_R/Resultados_outliers/T_acumulado.csv
similarity index 100%
rename from Testes_R/Result_T/T_acumulado.csv
rename to Testes_R/Resultados_outliers/T_acumulado.csv
diff --git a/Testes_R/Result_T/T_subsequente.csv b/Testes_R/Resultados_outliers/T_subsequente.csv
similarity index 100%
rename from Testes_R/Result_T/T_subsequente.csv
rename to Testes_R/Resultados_outliers/T_subsequente.csv
diff --git a/Testes_R/TESTE_COHEND.ipynb b/Testes_R/TESTE_COHEND.ipynb
index 8cf1389016ab4df43ca386034e04f3567ffa7e00..49a4cf3ec59f01cbd140924f2049d20101f6786d 100644
--- a/Testes_R/TESTE_COHEND.ipynb
+++ b/Testes_R/TESTE_COHEND.ipynb
@@ -252,14 +252,18 @@
     "\n",
     "# Amostras\n",
     "X <- df[\"NUM_SALAS\"]\n",
-    "Xi <- X[ANO == 2013]\n",
+    "data1 <- X[ANO == 2013]\n",
     "\n",
     "Y <- df[\"NUM_SALAS\"]\n",
-    "Yi <- Y[ANO == 2014]\n",
+    "data2 <- Y[ANO == 2014]\n",
+    "\n",
+    "# Remove zeros\n",
+    "data1 <- data1[data1 != 0]\n",
+    "data2 <- data2[data2 != 0]\n",
     "\n",
     "# Limpeza dos dados\n",
-    "data1 <- sort(remove_outliers(na.omit(Xi)))\n",
-    "data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+    "data1 <- remove_outliers(na.omit(data1))\n",
+    "data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "# Executa teste Cohen D\n",
     "print(cohen.d(data1, data2))\n",
@@ -301,20 +305,20 @@
     "\n",
     "            # Amostra de um ano\n",
     "            X <- df[col1]\n",
-    "            Xi <- X[ANO == ano]\n",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
     "\n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
+    "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
     "                next\n",
@@ -368,20 +372,20 @@
     "\n",
     "            # Amostra acumulada dos anos\n",
     "            X <- df[col1]\n",
-    "            Xi <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]\n",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO_COLUMN == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
     "\n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
+    "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
     "                next\n",
diff --git a/Testes_R/TESTE_F.ipynb b/Testes_R/TESTE_F.ipynb
index 45aa4d286984c2668d19bd8698ac5ced3a1a4c3b..9218de687dbf5497063b804af4d79fbdd7f7cb32 100644
--- a/Testes_R/TESTE_F.ipynb
+++ b/Testes_R/TESTE_F.ipynb
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
    "metadata": {},
    "outputs": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
    "metadata": {},
    "outputs": [],
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "4c578156-afa1-4432-acb3-d6a143c0b349",
    "metadata": {},
    "outputs": [],
@@ -210,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "a9629868-ef70-4073-856b-fa3cee413898",
    "metadata": {
     "scrolled": true
@@ -224,19 +224,19 @@
       "\tF test to compare two variances\n",
       "\n",
       "data:  data1 and data2\n",
-      "F = 1.0022, num df = 185050, denom df = 182511, p-value = 0.6373\n",
+      "F = NaN, num df = 42182, denom df = 40253, p-value = NA\n",
       "alternative hypothesis: true ratio of variances is not equal to 1\n",
       "95 percent confidence interval:\n",
-      " 0.9930793 1.0114080\n",
+      " NaN NaN\n",
       "sample estimates:\n",
       "ratio of variances \n",
-      "          1.002202 \n",
+      "               NaN \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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KMR7AAAqXB2llWr5M03xdU1+WCFCrJhg9SooV1bAFLFGjsAQOq8vGTGDJk0SQ4f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\ntwg78b/9iZGtHBuf69x56GvLtySn5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aX5jTvt3LHhimPOLBg3atTXeZvv3H2z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\ngg==",
       "text/plain": [
        "Plot with title “ecdf(x)”"
       ]
@@ -255,15 +255,19 @@
     "ANO <- df[\"ANO_CENSO\"]\n",
     "\n",
     "# Amostras\n",
-    "X <- df[\"NUM_SALAS\"]\n",
-    "Xi <- X[ANO == 2013]\n",
+    "X <- df[\"NUM_FAX\"]\n",
+    "data1 <- X[ANO == 2013]\n",
+    "\n",
+    "Y <- df[\"NUM_FAX\"]\n",
+    "data2 <- Y[ANO == 2014]\n",
     "\n",
-    "Y <- df[\"NUM_SALAS\"]\n",
-    "Yi <- Y[ANO == 2014]\n",
+    "# Remove zeros\n",
+    "data1 <- data1[data1 != 0]\n",
+    "data2 <- data2[data2 != 0]\n",
     "\n",
     "# Limpeza dos dados\n",
-    "data1 <- sort(remove_outliers(na.omit(Xi)))\n",
-    "data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+    "data1 <- remove_outliers(na.omit(data1))\n",
+    "data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "# Executa teste F\n",
     "print(var.test(data1, data2))\n",
@@ -283,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "9aa86d14-0e60-4945-9d5f-d058b3c45654",
    "metadata": {},
    "outputs": [],
@@ -292,28 +296,32 @@
     "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_f\", \"p_valor\")\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$ANO_CENSO))){\n",
-    "    for(col1 in names(df)) {\n",
-    "        for(col2 in names(df)) {\n",
+    "    for(col1 in atributos) {\n",
+    "        for(col2 in atributos) {\n",
     "\n",
     "            # Amostra de um ano\n",
     "            X <- df[col1]\n",
-    "            Xi <- X[ANO == ano]\n",
+    "            data1 <- X[ANO == ano]\n",
     "\n",
     "            # Amostra do ano seguinte\n",
     "            Y <- df[col2]\n",
-    "            Yi <- Y[ANO == ano+1]\n",
-    "\n",
-    "            # Remove NaN, outliers e ordena\n",
-    "            data1 <- sort(remove_outliers(na.omit(Xi)))\n",
-    "            data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+    "            data2 <- Y[ANO == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
+    "            \n",
+    "            # Remove NaN\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
@@ -345,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "bd76fa5b-a42c-434a-b3df-83f12f80a262",
    "metadata": {},
    "outputs": [],
@@ -355,28 +363,32 @@
     "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
     "ano_start = min(df$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 names(df)) {\n",
-    "        for(col2 in names(df)) {\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",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO_COLUMN == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
+    "            \n",
+    "            # Remove NaN\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
diff --git a/Testes_R/TESTE_KS.ipynb b/Testes_R/TESTE_KS.ipynb
index 8efe2db92d3c6d8e995775a52fb99c4010cc6e1a..04a4f5eec4a9001cc787df5b01e8c18e12a8a960 100644
--- a/Testes_R/TESTE_KS.ipynb
+++ b/Testes_R/TESTE_KS.ipynb
@@ -298,14 +298,18 @@
     "\n",
     "# Amostras\n",
     "X <- df[\"NUM_SALAS\"]\n",
-    "Xi <- X[ANO == 2013]\n",
+    "data1 <- X[ANO == 2013]\n",
     "\n",
     "Y <- df[\"NUM_SALAS\"]\n",
-    "Yi <- Y[ANO == 2014]\n",
+    "data2 <- Y[ANO == 2014]\n",
+    "\n",
+    "# Remove zeros\n",
+    "data1 <- data1[data1 != 0]\n",
+    "data2 <- data2[data2 != 0]\n",
     "\n",
     "# Limpeza dos dados\n",
-    "data1 <- sort(remove_outliers(na.omit(Xi)))\n",
-    "data2 <- sort(remove_outliers(na.omit(Yi)))\n",
+    "data1 <- remove_outliers(na.omit(data1))\n",
+    "data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "# Sub amostras (testar duas amostras de tamanhos iguais para o teste ks)\n",
     "tam = min(length(data1), length(data2))\n",
@@ -338,36 +342,39 @@
     "# Colunas do csv de saida\n",
     "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra\", \"estatistica_ks\", \"p_valor\")\n",
     "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
-    "            \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 names(df)) {\n",
-    "        for(col2 in names(df)) {\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",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
+    "            \n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
     "                next\n",
     "            }\n",
-    "            #cat(col1, \"|\", ano, \"|\", col2, \"|\", ano+1, \"|\", sep = \"\")\n",
-    "\n",
+    "            \n",
     "            # Distribui as amostras para o tamanho da menor amostra\n",
     "            tam = min(length(data1), length(data2))\n",
     "            x_amostra = mono_get_sample(data1, tam)\n",
@@ -408,29 +415,33 @@
     "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra\", \"estatistica_ks\", \"p_valor\")\n",
     "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
     "ano_start = min(df$ANO_CENSO)\n",
-    "            \n",
+    "\n",
+    "# Remove ANO_CENSO das iteracoes\n",
+    "atributos = names(df)\n",
+    "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+    "\n",
     "# Separa os anos em amostras\n",
     "ANO_COLUMN <- df[\"ANO_CENSO\"]\n",
     "\n",
     "for(ano in sort(unique(df$ANO_CENSO))){\n",
-    "    for(col1 in names(df)) {\n",
-    "        for(col2 in names(df)) {\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",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO_COLUMN == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
+    "            \n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
     "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
diff --git a/Testes_R/TESTE_T.ipynb b/Testes_R/TESTE_T.ipynb
index 266f1e70080a6fb737514d05d78ff248acd58428..d266e4bf599af365ba4bc9a507f17680103e6c9c 100644
--- a/Testes_R/TESTE_T.ipynb
+++ b/Testes_R/TESTE_T.ipynb
@@ -255,19 +255,19 @@
     "\n",
     "# Amostras\n",
     "X <- df[\"NUM_DVD\"]\n",
-    "Xi <- X[ANO == 2013]\n",
+    "data1 <- X[ANO == 2013]\n",
     "\n",
     "Y <- df[\"NUM_DVD\"]\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",
+    "data2 <- Y[ANO == 2014]\n",
     "\n",
     "# Remove zeros\n",
     "data1 <- data1[data1 != 0]\n",
     "data2 <- data2[data2 != 0]\n",
     "\n",
+    "# Limpeza dos dados\n",
+    "data1 <- remove_outliers(na.omit(data1))\n",
+    "data2 <- remove_outliers(na.omit(data2))\n",
+    "\n",
     "# Executa teste KS\n",
     "print(t.test(data1, data2))\n",
     "\n",
@@ -308,20 +308,20 @@
     "\n",
     "            # Amostra de um ano\n",
     "            X <- df[col1]\n",
-    "            Xi <- X[ANO == ano]\n",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
     "\n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
+    "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
     "                next\n",
@@ -375,20 +375,20 @@
     "\n",
     "            # Amostra acumulada dos anos\n",
     "            X <- df[col1]\n",
-    "            Xi <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]\n",
+    "            data1 <- 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",
+    "            data2 <- Y[ANO_COLUMN == ano+1]\n",
     "\n",
     "            # Remove zeros\n",
     "            data1 <- data1[data1 != 0]\n",
     "            data2 <- data2[data2 != 0]\n",
     "\n",
+    "            # Remove NaN, outliers e ordena\n",
+    "            data1 <- remove_outliers(na.omit(data1))\n",
+    "            data2 <- remove_outliers(na.omit(data2))\n",
+    "\n",
     "            # Pula casos em que não há dados nas amostras\n",
     "            if(length(data1) == 0 || length(data2) == 0){\n",
     "                next\n",