part 03/03. 분산분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 일원 분산 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 핸들링을 위한 패키지\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 통계를 위해 사용하는 패키지\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "# 시각화를 위한 패키지\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 그래프를 실제로 그리기 위한 설정\n",
    "%matplotlib inline\n",
    "\n",
    "# 경고 메시지 무시\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine</th>\n",
       "      <th>type</th>\n",
       "      <th>defRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.007828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>296</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>4</td>\n",
       "      <td>0.007573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     machine  type   defRate\n",
       "0          1     3  0.001999\n",
       "1          2     1  0.005974\n",
       "2          3     4  0.007828\n",
       "3          4     1  0.006121\n",
       "4          5     1  0.005887\n",
       "..       ...   ...       ...\n",
       "295      296     2  0.002640\n",
       "296      297     4  0.007573\n",
       "297      298     2  0.003870\n",
       "298      299     1  0.005815\n",
       "299      300     1  0.006372\n",
       "\n",
       "[300 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# csv 파일을 불러와서 df_f_oneway 데이터 프레임에 담습니다.\n",
    "df_f_oneway = pd.read_csv('data/anova/factory.csv')\n",
    "df_f_oneway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   machine  300 non-null    int64  \n",
      " 1   type     300 non-null    int64  \n",
      " 2   defRate  300 non-null    float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 7.2 KB\n"
     ]
    }
   ],
   "source": [
    "# 데이터 셋에 대한 정보 확인\n",
    "df_f_oneway.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "machine    0\n",
       "type       0\n",
       "defRate    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치가 있는지 확인\n",
    "df_f_oneway.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    78\n",
       "2    77\n",
       "1    75\n",
       "4    70\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 업체별 데이터 갯수 확인\n",
    "df_f_oneway['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine</th>\n",
       "      <th>type</th>\n",
       "      <th>defRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>150.500000</td>\n",
       "      <td>2.476667</td>\n",
       "      <td>0.004636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>86.746758</td>\n",
       "      <td>1.104622</td>\n",
       "      <td>0.002418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>75.750000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.002357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>150.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.003865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>225.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.006799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.008727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          machine        type     defRate\n",
       "count  300.000000  300.000000  300.000000\n",
       "mean   150.500000    2.476667    0.004636\n",
       "std     86.746758    1.104622    0.002418\n",
       "min      1.000000    1.000000    0.000704\n",
       "25%     75.750000    1.750000    0.002357\n",
       "50%    150.500000    2.000000    0.003865\n",
       "75%    225.250000    3.000000    0.006799\n",
       "max    300.000000    4.000000    0.008727"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 기술통계량 확인\n",
    "df_f_oneway.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "      <th>missing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.006029</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>0.005724</td>\n",
       "      <td>0.005986</td>\n",
       "      <td>0.006344</td>\n",
       "      <td>0.007295</td>\n",
       "      <td>0.005986</td>\n",
       "      <td>0.284333</td>\n",
       "      <td>-0.245943</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77.0</td>\n",
       "      <td>0.002996</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.002640</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.003368</td>\n",
       "      <td>0.004074</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.203093</td>\n",
       "      <td>-0.770529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.0</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.001689</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.001986</td>\n",
       "      <td>-0.369573</td>\n",
       "      <td>0.398895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.007946</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.006821</td>\n",
       "      <td>0.007650</td>\n",
       "      <td>0.007940</td>\n",
       "      <td>0.008278</td>\n",
       "      <td>0.008727</td>\n",
       "      <td>0.007940</td>\n",
       "      <td>-0.389347</td>\n",
       "      <td>-0.232689</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count      mean       std       min       25%       50%       75%  \\\n",
       "type                                                                      \n",
       "1      75.0  0.006029  0.000460  0.005062  0.005724  0.005986  0.006344   \n",
       "2      77.0  0.002996  0.000522  0.002097  0.002640  0.002976  0.003368   \n",
       "3      78.0  0.001946  0.000454  0.000704  0.001689  0.001986  0.002248   \n",
       "4      70.0  0.007946  0.000420  0.006821  0.007650  0.007940  0.008278   \n",
       "\n",
       "           max    median      skew  kurtosis  missing  \n",
       "type                                                   \n",
       "1     0.007295  0.005986  0.284333 -0.245943        0  \n",
       "2     0.004074  0.002976  0.203093 -0.770529        0  \n",
       "3     0.002917  0.001986 -0.369573  0.398895        0  \n",
       "4     0.008727  0.007940 -0.389347 -0.232689        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 왜도 계산 \n",
    "def skew(x):\n",
    "    return stats.skew(x)\n",
    "\n",
    "# 첨도 계산 \n",
    "def kurtosis(x):\n",
    "    return stats.kurtosis(x)\n",
    "\n",
    "# 기술통계를 살펴보면 평균 불량률의 경우 3번 업체가 가장 낮은 것으로 확인\n",
    "df_desc_stat = df_f_oneway.groupby(\"type\")['defRate'].describe()\n",
    "\n",
    "skew_results = []\n",
    "kurtosis_results = []\n",
    "null_results = []\n",
    "\n",
    "for i in range(1,5):\n",
    "    skew_results.append(skew(df_f_oneway[df_f_oneway['type'] == i]['defRate']))\n",
    "    kurtosis_results.append(kurtosis(df_f_oneway[df_f_oneway['type'] == i]['defRate']))\n",
    "    null_results.append(df_f_oneway[df_f_oneway['type'] == i]['defRate'].isnull().sum())\n",
    "\n",
    "df_desc_stat['median'] = df_f_oneway.groupby(\"type\")['defRate'].median()\n",
    "df_desc_stat['skew'] = skew_results\n",
    "df_desc_stat['kurtosis'] = kurtosis_results\n",
    "df_desc_stat['missing'] = null_results\n",
    "\n",
    "df_desc_stat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot을 통해서 type별로 유의미한 차이가 있을지 확인\n",
    "# 상자의 중심은 중앙값, 윗단과 아랫단은 각각 75%, 25%의 사분의점\n",
    "sns.boxplot(x = \"type\", y = \"defRate\", data = df_f_oneway)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# violinplot도 boxplot과 함께 type별로 유의미한 차이가 있을지 확인할 때 사용. \n",
    "# boxplot과 다르게 밀도를 추정 할 수 있음. \n",
    "sns.violinplot(x = \"type\", y = \"defRate\",data = df_f_oneway)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boxplot, violinplot을 보면 type간의 유의미한 차이가 있을것 같이 형상을 보임."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램 \n",
    "\n",
    "type1 = np.array(df_f_oneway[df_f_oneway['type'] == 1]['defRate'])\n",
    "type2 = np.array(df_f_oneway[df_f_oneway['type'] == 2]['defRate'])\n",
    "type3 = np.array(df_f_oneway[df_f_oneway['type'] == 3]['defRate'])\n",
    "type4 = np.array(df_f_oneway[df_f_oneway['type'] == 4]['defRate'])\n",
    "\n",
    "figure, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)\n",
    "figure.set_size_inches(16,12)\n",
    "\n",
    "sns.distplot(type1, norm_hist=False, kde=False, label=\"type1\", rug=True, color = 'blue', ax=ax1)\n",
    "sns.distplot(type2, norm_hist=False, kde=False, label=\"type2\", rug=True, color = 'red', ax=ax2)\n",
    "sns.distplot(type3, norm_hist=False, kde=False, label=\"type3\", rug=True, color = 'green', ax=ax3)\n",
    "sns.distplot(type4, norm_hist=False, kde=False, label=\"type4\", rug=True, color = 'orange', ax=ax4)\n",
    "\n",
    "ax1.set(ylabel='defRate', title = \"type1\")\n",
    "ax2.set(ylabel='defRate', title = \"type2\")\n",
    "ax3.set(ylabel='defRate', title = \"type3\")\n",
    "ax4.set(ylabel='defRate', title = \"type4\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)\n",
    "figure.set_size_inches(16,12)\n",
    "\n",
    "sns.distplot(type1, label=\"type1\", color = 'blue', ax=ax1)\n",
    "sns.distplot(type2, label=\"type2\", color = 'red',ax=ax2)\n",
    "sns.distplot(type3, label=\"type3\", color = 'green', ax=ax3)\n",
    "sns.distplot(type4, label=\"type4\", rug=True, color = 'orange', ax=ax4)\n",
    "\n",
    "ax1.set(ylabel='defRate', title = \"type1\")\n",
    "ax2.set(ylabel='defRate', title = \"type2\")\n",
    "ax3.set(ylabel='defRate', title = \"type3\")\n",
    "ax4.set(ylabel='defRate', title = \"type4\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 한번에 보기\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "ax5 = sns.distplot(type1, hist=True, norm_hist=False, kde=False, label=\"type1\", color = 'blue')\n",
    "ax5 = sns.distplot(type2, hist=True, norm_hist=False, kde=False, label=\"type2\", color = 'red')\n",
    "ax5 = sns.distplot(type3, hist=True, norm_hist=False, kde=False, label=\"type3\", color = 'green')\n",
    "ax5 = sns.distplot(type4, hist=True, norm_hist=False, kde=False, label=\"type4\", color = 'orange')\n",
    "ax5.set(xlabel=\"defRate\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 한번에 보기\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "ax5 = sns.distplot(type1, kde=False, fit=stats.gamma, label=\"type1\",color = 'blue')\n",
    "ax5 = sns.distplot(type2, kde=False, fit=stats.gamma, label=\"type2\", color = 'red')\n",
    "ax5 = sns.distplot(type3, kde=False, fit=stats.gamma, label=\"type3\", color = 'green')\n",
    "ax5 = sns.distplot(type4, kde=False, fit=stats.gamma, label=\"type4\", color = 'yellow')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>defRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.001999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.005974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.007828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.006121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.005887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type   defRate\n",
       "0     3  0.001999\n",
       "1     1  0.005974\n",
       "2     4  0.007828\n",
       "3     1  0.006121\n",
       "4     1  0.005887"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANOVA 일원분산 분석 - machine 컬럼 삭제 \n",
    "df_one_anova = df_f_oneway.drop('machine',axis=1)\n",
    "df_one_anova.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 참고사항\n",
    "# %-formatting 방식\n",
    "# print('F-Value : %.4f  P-value : %.4f' % oneway_anova_result)\n",
    "\n",
    "# format() 형식\n",
    "# print(\"F-Value : {:.4f} P-value : {:.4f}\".format(oneway_anova_result[0], oneway_anova_result[1]))\n",
    "\n",
    "# f-string (Python 3.6 이상 사용 가능)\n",
    "# print(f'F-Value : {oneway_anova_result[0]:.4f} P-value : {oneway_anova_result[1]:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Value : 2583.2183 P-value : 0.000\n",
      "p-value < 0.05 이므로 귀무가설이 기각.\n",
      "F_onewayResult(statistic=2583.218336850084, pvalue=7.222458556350768e-212)\n"
     ]
    }
   ],
   "source": [
    "# scipy.stats 으로 ANOVA 분석\n",
    "type1 = df_one_anova[df_one_anova['type'] == 1]['defRate']\n",
    "type2 = df_one_anova[df_one_anova['type'] == 2]['defRate']\n",
    "type3 = df_one_anova[df_one_anova['type'] == 3]['defRate']\n",
    "type4 = df_one_anova[df_one_anova['type'] == 4]['defRate']\n",
    "\n",
    "oneway_anova_result = stats.f_oneway(type1, type2, type3, type4)\n",
    "oneway_anova_result\n",
    "\n",
    "print(f'F-Value : {oneway_anova_result[0]:.4f} P-value : {oneway_anova_result[1]:.3f}')\n",
    "if oneway_anova_result[1] < 0.05:\n",
    "    print(\"p-value < 0.05 이므로 귀무가설이 기각.\")\n",
    "\n",
    "print(oneway_anova_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>defRate</td>     <th>  R-squared:         </th> <td>   0.963</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.963</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2583.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 26 Apr 2021</td> <th>  Prob (F-statistic):</th> <td>7.22e-212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:33:28</td>     <th>  Log-Likelihood:    </th> <td>  1877.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   300</td>      <th>  AIC:               </th> <td>  -3747.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   296</td>      <th>  BIC:               </th> <td>  -3732.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>    0.0060</td> <td> 5.38e-05</td> <td>  112.003</td> <td> 0.000</td> <td>    0.006</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(type)[T.2]</th> <td>   -0.0030</td> <td> 7.56e-05</td> <td>  -40.105</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(type)[T.3]</th> <td>   -0.0041</td> <td> 7.54e-05</td> <td>  -54.161</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(type)[T.4]</th> <td>    0.0019</td> <td> 7.75e-05</td> <td>   24.745</td> <td> 0.000</td> <td>    0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.584</td> <th>  Durbin-Watson:     </th> <td>   2.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.747</td> <th>  Jarque-Bera (JB):  </th> <td>   0.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.014</td> <th>  Prob(JB):          </th> <td>   0.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.766</td> <th>  Cond. No.          </th> <td>    4.79</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                defRate   R-squared:                       0.963\n",
       "Model:                            OLS   Adj. R-squared:                  0.963\n",
       "Method:                 Least Squares   F-statistic:                     2583.\n",
       "Date:                Mon, 26 Apr 2021   Prob (F-statistic):          7.22e-212\n",
       "Time:                        13:33:28   Log-Likelihood:                 1877.6\n",
       "No. Observations:                 300   AIC:                            -3747.\n",
       "Df Residuals:                     296   BIC:                            -3732.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept        0.0060   5.38e-05    112.003      0.000       0.006       0.006\n",
       "C(type)[T.2]    -0.0030   7.56e-05    -40.105      0.000      -0.003      -0.003\n",
       "C(type)[T.3]    -0.0041   7.54e-05    -54.161      0.000      -0.004      -0.004\n",
       "C(type)[T.4]     0.0019   7.75e-05     24.745      0.000       0.002       0.002\n",
       "==============================================================================\n",
       "Omnibus:                        0.584   Durbin-Watson:                   2.088\n",
       "Prob(Omnibus):                  0.747   Jarque-Bera (JB):                0.693\n",
       "Skew:                          -0.014   Prob(JB):                        0.707\n",
       "Kurtosis:                       2.766   Cond. No.                         4.79\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statsmodels 패키지를 사용하여 일원분산 분석 실시\n",
    "df_model = pd.DataFrame(df_f_oneway, columns=['defRate', 'type'])\n",
    "\n",
    "results = ols('defRate ~ C(type)', data = df_model).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>df</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(type)</th>\n",
       "      <td>0.001684</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2583.218337</td>\n",
       "      <td>7.222459e-212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>0.000064</td>\n",
       "      <td>296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sum_sq     df            F         PR(>F)\n",
       "C(type)   0.001684    3.0  2583.218337  7.222459e-212\n",
       "Residual  0.000064  296.0          NaN            NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum_sq: 편차 제곱합\n",
    "# df: 자유도\n",
    "# F: F통계량\n",
    "# PR(>F): P-value\n",
    "\n",
    "one_anova_table = sm.stats.anova_lm(results, typ=2)\n",
    "one_anova_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "levene result(F) : 1.731 \n",
      "p-value : 0.161\n",
      "levene result(F) : 1.749 \n",
      "p-value : 0.157\n",
      "fligner Result(F) : 5.074 \n",
      "p-value : 0.166\n",
      "bartlett Result(F) : 3.608 \n",
      "p-value : 0.307\n"
     ]
    }
   ],
   "source": [
    "# 기본 가정 중 등분산성을 검정\n",
    "# 등분산 검정 레빈(levene)\n",
    "levene = stats.levene(type1, type2, type3, type4)\n",
    "print('levene result(F) : %.3f \\np-value : %.3f' % (levene))\n",
    "\n",
    "# 분산의 동질성 검정 기준 - default : 'median'. 'mean' 으로 변경 할 경우 다음과 같이 mean 으로 설정\n",
    "levene = stats.levene(type1, type2, type3, type4, center = 'mean')\n",
    "print('levene result(F) : %.3f \\np-value : %.3f' % (levene))\n",
    "\n",
    "# 등분산 검정 플리그너(fligner) \n",
    "fligner = stats.fligner(type1, type2, type3, type4)\n",
    "print('fligner Result(F) : %.3f \\np-value : %.3f' % (fligner))\n",
    "\n",
    "# 등분산 검정 바틀렛(bartlett)\n",
    "bartlett = stats.bartlett(type1, type2, type3, type4)\n",
    "print('bartlett Result(F) : %.3f \\np-value : %.3f' % (bartlett))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th> <th>p-adj</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>2</td>    <td>-0.003</td>  <td>0.001</td> <td>-0.0032</td> <td>-0.0028</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>3</td>    <td>-0.0041</td> <td>0.001</td> <td>-0.0043</td> <td>-0.0039</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>1</td>      <td>4</td>    <td>0.0019</td>  <td>0.001</td> <td>0.0017</td>  <td>0.0021</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>3</td>    <td>-0.0011</td> <td>0.001</td> <td>-0.0012</td> <td>-0.0009</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>2</td>      <td>4</td>     <td>0.005</td>  <td>0.001</td> <td>0.0048</td>  <td>0.0051</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>3</td>      <td>4</td>     <td>0.006</td>  <td>0.001</td> <td>0.0058</td>  <td>0.0062</td>   <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reject: True인 경우, type1과 type2의 불량률의 차이가 없다는 귀무가설을 기각\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "\n",
    "posthoc = pairwise_tukeyhsd(df_f_oneway['defRate'], df_f_oneway['type'], alpha=0.05)\n",
    "posthoc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean\n",
       "type          \n",
       "1     0.006029\n",
       "2     0.002996\n",
       "3     0.001946\n",
       "4     0.007946"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desc_stat[['mean']]"
   ]
  }
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