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Limites de controle Gráficos Xbarra e S - python notebook

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 "nbformat": 4,
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 "metadata": {
 "colab": {
 "name": "CarlosEduardo04.ipynb",
 "provenance": [],
 "toc_visible": true
 },
 "kernelspec": {
 "name": "python3",
 "display_name": "Python 3"
 },
 "language_info": {
 "name": "python"
 }
 },
 "cells": [
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "FKsB5UTJjvyh"
 },
 "source": [
 "#a) leia o arquivo;"
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "5WNdKQ-OH3Yt"
 },
 "source": [
 "#Pacotes\n",
 "import pandas as pd\n",
 "import matplotlib.pyplot as plt\n",
 "import math\n",
 "import scipy.stats as st\n",
 "import numpy as np"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "27bJXluRjpdm",
 "outputId": "a6cf1a60-68b5-4476-b874-2549ecfff93b"
 },
 "source": [
 "#montando e conectando ao drive\n",
 "from google.colab import drive\n",
 "drive.mount('/content/drive')"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "stream",
 "text": [
 "Mounted at /content/drive\n"
 ],
 "name": "stdout"
 }
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "hzPeeXmOkIAQ"
 },
 "source": [
 "#importando o pandas para manipular os dados como dataframes\n",
 "\n",
 "df = pd.read_excel(\"/content/drive/MyDrive/2021.2/tec controle qualidade/T4_XS.xlsx\")"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "jJeM7OMJkKUU"
 },
 "source": [
 "#importando a tabela de parâmetros gráficos\n",
 "parametros_graf = pd.read_excel(\"/content/drive/MyDrive/2021.2/tec controle qualidade/TEP00120 - TABELAS.xlsx\", \n",
 " header = 2,\n",
 " sheet_name = \"Parâmetros gráficos\")\n",
 "#importando a Distruição W, segunda aba do mesmo arquivo onde consta os parâmetros gráficos\n",
 "dist_w = pd.read_excel(\"/content/drive/MyDrive/2021.2/tec controle qualidade/TEP00120 - TABELAS.xlsx\", \n",
 " header = 1,\n",
 " sheet_name = \"Distribuição W\")\n",
 "#preencher os NaN com zeros\n",
 "dist_w.fillna(0, inplace=True)"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "nzcKhxXDZmZc"
 },
 "source": [
 "##Rotina para pesquisar parametros gráficos na tabela"
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "NPfDpSE1XiIx"
 },
 "source": [
 "#Rotina para atribuir uma var com os parâmetro gráficos\n",
 "\n",
 "def chamar_parametro(parametro):\n",
 " \"\"\" atribuir o valor do parametro gráfico para determinado n a uma var\"\"\"\n",
 " #usamos n-2 porque o index começa no zero e a coluna n no dois \n",
 " var_grafica = parametros_graf[parametro].loc[n-2]\n",
 " return var_grafica"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "g5d80ZwSkOAy"
 },
 "source": [
 "##Criando as Colunas \"Média\" e \"S\""
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "ZqrTglOSkZS9"
 },
 "source": [
 "#definindo var n com o tamanho das amostras n, sendo a contagem das colunas sem a primeira col \"Amostra\"\n",
 "n = len(df.iloc[:,1:].columns)\n",
 "#criando uma lista com o nome das colunas que contém os dados coletados, sem a amostra\n",
 "colunas = [\"Medida_\"+ str(n) for n in range(1,len(df.columns)) ]\n",
 "\n",
 "def df_mean_S(df):\n",
 " \"\"\"retorna uma cópia do df com as colunas Média e Amplitude\"\"\"\n",
 " df1 = df.copy()\n",
 " #criando uma coluna com a média das amostras linha a linha\n",
 " df1['Média'] = df[colunas].mean(axis=1)\n",
 " #criando uma coluna com a amplitude de cada linha\n",
 " df1['S'] = df[colunas].std(axis=1)\n",
 " return df1"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "-w4AziRjx53F"
 },
 "source": [
 "df1= df_mean_S(df)"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/",
 "height": 224
 },
 "id": "gWkA7XhXx-R-",
 "outputId": "4e9a2d73-fcbe-4586-8f53-5565412c39cd"
 },
 "source": [
 "df1.head()"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "execute_result",
 "data": {
 "text/html": [
 "<div>\n",
 "<style scoped>\n",
 " .dataframe tbody tr th:only-of-type {\n",
 " vertical-align: middle;\n",
 " }\n",
 "\n",
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 " .dataframe thead th {\n",
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 " }\n",
 "</style>\n",
 "<table border=\"1\" class=\"dataframe\">\n",
 " <thead>\n",
 " <tr style=\"text-align: right;\">\n",
 " <th></th>\n",
 " <th>Amostra</th>\n",
 " <th>Medida_1</th>\n",
 " <th>Medida_2</th>\n",
 " <th>Medida_3</th>\n",
 " <th>Medida_4</th>\n",
 " <th>Medida_5</th>\n",
 " <th>Medida_6</th>\n",
 " <th>Medida_7</th>\n",
 " <th>Medida_8</th>\n",
 " <th>Medida_9</th>\n",
 " <th>Medida_10</th>\n",
 " <th>Medida_11</th>\n",
 " <th>Medida_12</th>\n",
 " <th>Medida_13</th>\n",
 " <th>Medida_14</th>\n",
 " <th>Média</th>\n",
 " <th>S</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th>0</th>\n",
 " <td>1</td>\n",
 " <td>76.523</td>\n",
 " <td>78.655</td>\n",
 " <td>78.143</td>\n",
 " <td>78.558</td>\n",
 " <td>79.625</td>\n",
 " <td>76.916</td>\n",
 " <td>77.527</td>\n",
 " <td>80.040</td>\n",
 " <td>77.712</td>\n",
 " <td>78.736</td>\n",
 " <td>75.400</td>\n",
 " <td>76.241</td>\n",
 " <td>79.183</td>\n",
 " <td>74.663</td>\n",
 " <td>77.708714</td>\n",
 " <td>1.592514</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>1</th>\n",
 " <td>2</td>\n",
 " <td>74.841</td>\n",
 " <td>79.495</td>\n",
 " <td>78.634</td>\n",
 " <td>77.433</td>\n",
 " <td>77.663</td>\n",
 " <td>77.370</td>\n",
 " <td>78.072</td>\n",
 " <td>80.079</td>\n",
 " <td>73.722</td>\n",
" <td>74.050</td>\n",
 " <td>80.456</td>\n",
 " <td>78.264</td>\n",
 " <td>80.223</td>\n",
 " <td>76.219</td>\n",
 " <td>77.608643</td>\n",
 " <td>2.214472</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>2</th>\n",
 " <td>3</td>\n",
 " <td>76.388</td>\n",
 " <td>76.546</td>\n",
 " <td>77.358</td>\n",
 " <td>77.715</td>\n",
 " <td>79.597</td>\n",
 " <td>76.115</td>\n",
 " <td>79.731</td>\n",
 " <td>77.722</td>\n",
 " <td>77.803</td>\n",
 " <td>77.760</td>\n",
 " <td>77.628</td>\n",
 " <td>79.231</td>\n",
 " <td>78.122</td>\n",
 " <td>77.764</td>\n",
 " <td>77.820000</td>\n",
 " <td>1.100715</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>4</td>\n",
 " <td>77.009</td>\n",
 " <td>77.793</td>\n",
 " <td>77.869</td>\n",
 " <td>78.889</td>\n",
 " <td>79.672</td>\n",
 " <td>75.304</td>\n",
 " <td>77.834</td>\n",
 " <td>77.910</td>\n",
 " <td>78.204</td>\n",
 " <td>77.758</td>\n",
 " <td>78.015</td>\n",
 " <td>79.692</td>\n",
 " <td>77.437</td>\n",
 " <td>78.297</td>\n",
 " <td>77.977357</td>\n",
 " <td>1.084625</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>4</th>\n",
 " <td>5</td>\n",
 " <td>77.991</td>\n",
 " <td>80.150</td>\n",
 " <td>77.126</td>\n",
 " <td>78.936</td>\n",
 " <td>79.914</td>\n",
 " <td>76.145</td>\n",
 " <td>78.556</td>\n",
 " <td>78.685</td>\n",
 " <td>78.088</td>\n",
 " <td>75.878</td>\n",
 " <td>76.066</td>\n",
 " <td>73.772</td>\n",
 " <td>77.032</td>\n",
 " <td>76.712</td>\n",
 " <td>77.503643</td>\n",
 " <td>1.741332</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "</div>"
 ],
 "text/plain": [
 " Amostra Medida_1 Medida_2 ... Medida_14 Média S\n",
 "0 1 76.523 78.655 ... 74.663 77.708714 1.592514\n",
 "1 2 74.841 79.495 ... 76.219 77.608643 2.214472\n",
 "2 3 76.388 76.546 ... 77.764 77.820000 1.100715\n",
 "3 4 77.009 77.793 ... 78.297 77.977357 1.084625\n",
 "4 5 77.991 80.150 ... 76.712 77.503643 1.741332\n",
 "\n",
 "[5 rows x 17 columns]"
 ]
 },
 "metadata": {
 "tags": []
 },
 "execution_count": 12
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "7QG58yO_jx-V"
 },
 "source": [
 "#b) calcule LM, LIC e LSC dos gráficos S e Xbarra;"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "v9msBZfupspq"
 },
 "source": [
 "##Gráfico S"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "M_5jvWbSnxmX"
 },
 "source": [
 "S^2 é um estimador não enviesado para sigma^2 mas S não é um estimador não enviesado para sigma. \n",
 "\n",
 "S é um estimador não enviesado para c4*sigma."
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "kY6AykYmpmyD"
 },
 "source": [
 "![image.png](data:image/png;base64,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)"
]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "UlNSTqglt8L5"
 },
 "source": [
 "Para os limites de controle, temos:\n",
 "\n",
 "\n",
 "![image.png](data:image/png;base64,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)"
]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "3kb_0ZeQjzne"
 },
 "source": [
 "def calcula_limites_s(df):\n",
 " \"\"\"#criando uma função para definir os limites de controle de S\"\"\"\n",
 "\n",
 " #salvando os parametros gráficos \"B3\" e \"B4\" em vars com o mesmo nome. \n",
 " B3 = chamar_parametro(\"B3\")\n",
 " B4 = chamar_parametro(\"B4\")\n",
 "\n",
 " #definindo LMs = Sbarra\n",
 " LMs = df[\"S\"].mean(axis=0)\n",
 " #Definindo LSCs = B3*Sbarra\n",
 " LSCs = B4*LMs\n",
 " #Definindo LICx = B4*Sbarra\n",
 " LICs = B3*LMs\n",
 "\n",
 " limites = [LMs, LSCs, LICs]\n",
 " return limites"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "YVo-GKG6xq9p",
 "outputId": "328beaa9-9a07-40a1-feb8-411bb72577f0"
 },
 "source": [
 "limites_s = calcula_limites_s(df1)\n",
 "print(f'LM = {limites_s[0]} \\nLSC = {limites_s[1]} \\nLIC = {limites_s[2]}')"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "stream",
 "text": [
 "LM = 2.1434290735069883 \n",
 "LSC = 3.4166259431701396 \n",
 "LIC = 0.8702322038438373\n"
 ],
 "name": "stdout"
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "TB1TVB7TuY0t"
 },
 "source": [
 "##Gráfico Xbarra"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "Cnw1ZH7iwGfl"
 },
 "source": [
 "Usando o estimador para sigma, manipulamos os limites como no exemplo abaixo:\n",
 "\n",
 "![image.png](data:image/png;base64,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)"
]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "OrbNNXrsudOD"
 },
 "source": [
 "Assim, chegamos aos seguintes limites de Xbarra:\n",
 "\n",
 "\n",
 "![image.png](data:image/png;base64,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)"
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "MDNlc7AjucIK"
 },
 "source": [
 "def calcula_limites_xbarra(df):\n",
 " \"\"\"#criando uma função para definir os limites de controle de Xbarra\"\"\"\n",
 "\n",
 " #salvando o parametro gráfico \"A3\" em uma var com o mesmo nome. \n",
 " A3 = chamar_parametro(\"A3\")\n",
 " Sbarra = df[\"S\"].mean(axis=0)\n",
 "\n",
 " #definindo LMx = Xbarra-barra\n",
 "
LMx = df[\"Média\"].mean(axis=0)\n",
 " #Definindo LSCx = Xbarra-barra + A3*Sbarra\n",
 " LSCx = LMx + A3*Sbarra\n",
 " #Definindo LICx = Xbarra-barra - A3*Sbarra\n",
 " LICx = LMx - A3*Sbarra\n",
 "\n",
 " limites = [LMx, LSCx, LICx]\n",
 " #print(f'LM = {LMx} \\nLSC = {LSCx} \\nLIC = {LICx}')\n",
 " return limites"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "G1CKGququcM9",
 "outputId": "c31bd565-449c-4005-d979-f7c54f21be6a"
 },
 "source": [
 "limites_xbarra = calcula_limites_xbarra(df1)\n",
 "print(f'LM = {limites_xbarra[0]} \\nLSC = {limites_xbarra[1]} \\nLIC = {limites_xbarra[2]}')"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "stream",
 "text": [
 "LM = 77.36227405247814 \n",
 "LSC = 79.11345560553335 \n",
 "LIC = 75.61109249942294\n"
 ],
 "name": "stdout"
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "oYXfQVqQjz_S"
 },
 "source": [
 "#c) plote os gráficos de controle de S e Xbarra com os pontos dados"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "nKCxyBNUy9c2"
 },
 "source": [
 "##Plot Xbarra"
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "6b-ru7hIj1l1"
 },
 "source": [
 "def plot_xbarra(df1):\n",
 " limites_xbarra = calcula_limites_xbarra(df1)\n",
 " plt.plot(df1['Amostra'], df1['Média'])\n",
 " plt.hlines(limites_xbarra[0], xmin=0, xmax = len(df1), colors ='g')\n",
 " plt.hlines(limites_xbarra[1], xmin=0, xmax = len(df1), colors = 'y')\n",
 " plt.hlines(limites_xbarra[2], xmin=0, xmax = len(df1), colors = 'r')\n",
 "\n",
 " plt.title(\"Gráfico Xbarra\")\n",
 " plt.xlabel(\"Amostra\")\n",
 " plt.legend(('Xbarra','LM', 'LSC', 'LIC'),loc='best')\n",
 " plt.show()"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/",
 "height": 295
 },
 "id": "T4_ocKhRzG3a",
 "outputId": "9a3c1c8c-a40c-45be-bdd3-2c1d26b036a2"
 },
 "source": [
 "plot_xbarra(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "display_data",
 "data": {
 "image/png": 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\n",
"text/plain": [
 "<Figure size 432x288 with 1 Axes>"
 ]
 },
 "metadata": {
 "tags": [],
 "needs_background": "light"
 }
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "01wBjzd2zPlR"
 },
 "source": [
 "O gráfico de Xbarra mostra uma amostra fora dos limites de controle entre a amostra 30 e 35. Precisamos removê-la e recalcular os limites."
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "QAT9cNlPzqrc"
 },
 "source": [
 "def elimina_outlier_xbarra(df1):\n",
 " \"\"\"elimina uma amostra fora dos limites de controle de xbarra, se houver alguma\"\"\"\n",
 "\n",
 " #recalcula os limites de controle\n",
 " limites_xbarra = calcula_limites_xbarra(df1)\n",
 "\n",
 "\n",
 " #identificando a linha com maior valor na coluna \n",
 " amostra_ruim = df1[df1['Média'] == df1['Média'].max(axis=0)]\n",
 " #checando se o ponto com maior valor está acima do limite de controle\n",
 " if amostra_ruim['Média'].values[0] > float(limites_xbarra[1]):\n",
 " #cria cópia do df para evitar perder dado por erro de script\n",
 " df2 = df1.copy()\n",
 " df2.drop(df2.index[amostra_ruim[\"Amostra\"]-1], inplace=True) \n",
 " print('há pontos acima dos limites de controle')\n",
 " else:\n",
 " print('não há pontos acima dos limites de controle') \n",
 " df2 = df1.copy() \n",
 "\n",
 " #identificando a linha com menor valor na coluna \n",
 " amostra_ruim = df1[df1['Média'] == df1['Média'].min(axis=0)]\n",
 " #checando se o ponto com maior valor está abaixo do limite de controle\n",
 " if amostra_ruim['Média'].values[0] < float(limites_xbarra[2]):\n",
 " #cria cópia do df para evitar perder dado por erro de script\n",
 " df2 = df1.copy()\n",
 " print(f'há pontos abaixo dos limites de controle, amostra: {amostra_ruim[\"Amostra\"].values} eliminada')\n",
 " df2.drop(df2.index[amostra_ruim[\"Amostra\"]-1], inplace=True) \n",
 " else:\n",
 " print('não há pontos abaixo dos limites de controle') \n",
 " df2 = df1.copy()\n",
 "\n",
 " return df2 "
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "FNnJGnx_1Op9",
 "outputId": "e5ce9422-4ab5-4124-ea10-da0ecfd18acb"
 },
 "source": [
 "df1 = elimina_outlier_xbarra(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "stream",
 "text": [
 "não há pontos acima dos limites de controle\n",
 "há pontos abaixo dos limites de controle, amostra: [33] eliminada\n"
 ],
 "name": "stdout"
 }
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/",
 "height": 295
 },
 "id": "mxA0o-Wt2_64",
 "outputId": "32e71234-2dfe-4b4e-b1da-f1d456097d91"
 },
 "source": [
 "plot_xbarra(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "display_data",
 "data": {
 "image/png": 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\n",
"text/plain": [
 "<Figure size 432x288 with 1 Axes>"
 ]
 },
 "metadata": {
 "tags": [],
 "needs_background": "light"
 }
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "jDXXr2RU3H0Y"
 },
 "source": [
 "##Plot S"
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "IxhrEAX83VfR"
 },
 "source": [
 "def plot_s(df1):\n",
 " #Plotar\n",
 " limites_s = calcula_limites_s(df1)\n",
 " plt.plot(df1['Amostra'], df1['S'])\n",
 " plt.hlines(limites_s[0], xmin=0, xmax = len(df1), colors ='g')\n",
 " plt.hlines(limites_s[1], xmin=0, xmax = len(df1), colors = 'y')\n",
 " plt.hlines(limites_s[2], xmin=0, xmax = len(df1), colors = 'r')\n",
 "\n",
 " plt.title(\"Gráfico S\")\n",
 " plt.xlabel(\"Amostra\")\n",
 " plt.ylabel(\"S\")\n",
 " plt.legend(('S','LM', 'LSC', 'LIC'),loc='best')\n",
 " plt.show()"
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/",
 "height": 295
 },
 "id": "zp3IHyku3buO",
 "outputId": "4d09e3b0-19a5-485d-8665-c95af971ab97"
 },
 "source": [
 "plot_s(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "display_data",
 "data": {
 "image/png": 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\n",
"text/plain": [
 "<Figure size 432x288 with 1 Axes>"
 ]
 },
 "metadata": {
 "tags": [],
 "needs_background": "light"
 }
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "jwIOhuqW3mdo"
 },
 "source": [
 "Há uma amostra fora dos limites de controle do gráfico S também. Vamos removê-la."
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "id": "sYqNvuL03tha"
 },
 "source": [
 "def elimina_outlier_s(df1):\n",
 " \"\"\"elimina uma amostra fora dos limites de controle de s, se houver alguma\"\"\"\n",
 "\n",
 " #recalcula os limites de controle\n",
 " limites_s = calcula_limites_s(df1)\n",
 "\n",
 "\n",
 " #identificando a linha com maior valor na coluna \n",
 " amostra_ruim = df1[df1['S'] == df1['S'].max(axis=0)]\n",
 " #checando se o ponto com maior valor está acima do limite de controle\n",
 " if amostra_ruim['S'].values[0] > float(limites_s[1]):\n",
 " #cria cópia do df para evitar perder dado por erro de script\n",
 " df2 = df1.copy()\n",
 " print(f'há pontos acima dos limites de controle, amostra: {amostra_ruim[\"Amostra\"].values} eliminada')\n",
 " df2.drop(df2.index[amostra_ruim[\"Amostra\"]-1], inplace=True) \n",
 " return df2\n",
 " else:\n",
 " print('não há pontos acima dos limites de controle') \n",
 " df2 = df1.copy() \n",
 "\n",
 " #identificando a linha com menor valor na coluna \n",
 " amostra_ruim = df1[df1['S'] == df1['S'].min(axis=0)]\n",
 " #checando se o ponto com maior valor está abaixo do limite de controle\n",
 " if amostra_ruim['S'].values[0] < float(limites_s[2]):\n",
 " #cria cópia do df para evitar perder dado por erro de script\n",
 " df2 = df1.copy()\n",
 " print(f'há pontos abaixo dos limites de controle, amostra: {amostra_ruim[\"Amostra\"].values} eliminada')\n",
 " df2.drop(df2.index[amostra_ruim[\"Amostra\"]-1], inplace=True) \n",
 " return df2\n",
 " else:\n",
 " print('não há pontos abaixo dos limites de controle') \n",
 " df2 = df1.copy()\n",
 " return df2\n",
 "\n",
 " "
 ],
 "execution_count": null,
 "outputs": []
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "FokkAyC04TJR",
 "outputId": "1d3a656a-4890-4c75-9c8f-fa57d94681ff"
 },
 "source": [
 "df1 = elimina_outlier_s(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "stream",
 "text": [
 "há pontos acima dos limites de controle, amostra: [18] eliminada\n"
 ],
 "name": "stdout"
 }
 ]
 },
 {
 "cell_type": "code",
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/",
 "height": 295
 },
 "id": "BthL0PA87AkO",
 "outputId": "68aba1e0-6761-43cb-d71b-344f2d0b22e5"
 },
 "source": [
 "plot_s(df1)"
 ],
 "execution_count": null,
 "outputs": [
 {
 "output_type": "display_data",
 "data": {
 "image/png": 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\n",
"text/plain": [
 "<Figure size 432x288 with 1 Axes>"
 ]
 },
 "metadata": {
 "tags": [],
 "needs_background": "light"
 }
 }
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "CqMB9SMmj2DE"
 },
 "source": [
 "#d) plote a curva do poder de detecção do gráfico de Xbarra"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "GJPNK1aJ7XNd"
 },
 "source": [
 "A curva do poder de detecção Pd depende de n. Vamos plotar algumas curvas de Pd para diferentes valores de n."
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {
 "id": "rhmR3Z1V-aPH"
 },
 "source": [
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)"

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