2_correlation_lecture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "data_dir = './data/'\n",
    "import numpy as np\n",
    "\n",
    "#######################################\n",
    "# 相関\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## 身長と体重の相間\n",
    "HW_data = pd.read_csv(data_dir + 'height_weight.csv')\n",
    "plt.scatter(HW_data['Ht'], HW_data['Wt']) # 分布をみる\n",
    "\n",
    "r,p = scipy.stats.pearsonr(HW_data['Ht'],HW_data['Wt'])\n",
    "print(r)\n",
    "print(p)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.7809062893282465\n",
      "9.638647211605826e-43\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 636,
     "status": "ok",
     "timestamp": 1565663667110,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "SvzIoBNbz7wu",
    "outputId": "795389a8-e046-4494-ef8d-379c4385aadd"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "## アイリスデータ解析\n",
    "# iris data 読み込み\n",
    "iris=pd.read_csv(data_dir+'iris.csv')\n",
    "\n",
    "# sepal length と petal length の相関係数 r と有意確率 p を求める\n",
    "plt.scatter(iris.iloc[:,0], iris.iloc[:,2]) # 分布を見る\n",
    "r,p = scipy.stats.pearsonr(iris.iloc[:,0], iris.iloc[:,2])\n",
    "print(r)\n",
    "print(p)\n",
    "\n",
    "# 相関マップ\n",
    "R = np.corrcoef([iris.iloc[:,0], iris.iloc[:,1],\n",
    "                 iris.iloc[:,2], iris.iloc[:,3]])\n",
    "print(R)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.8717541573048713\n",
      "1.0384540627941062e-47\n",
      "[[ 1.         -0.10936925  0.87175416  0.81795363]\n",
      " [-0.10936925  1.         -0.4205161  -0.35654409]\n",
      " [ 0.87175416 -0.4205161   1.          0.9627571 ]\n",
      " [ 0.81795363 -0.35654409  0.9627571   1.        ]]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 736,
     "status": "ok",
     "timestamp": 1565663746063,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "V-2p6-js1IFt",
    "outputId": "f147044d-2a5b-4ab4-9651-db4baf30660c"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# 相関行列のヒートマップを描く\n",
    "import seaborn as sns\n",
    "sns.heatmap(R)\n",
    "# グラフを表示する\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 974,
     "status": "ok",
     "timestamp": 1565663764819,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "MbAafe9-1PgG",
    "outputId": "ed2b7adb-30a9-46a8-f8dc-c8f13f2f6ee7"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# もう少しわかりやすく相関行列のヒートマップを描く\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5) # フォントの大きさを調整\n",
    "cr = ['SepalLength', 'SepalWidth', \n",
    "      'PetalLength','PetalWidth'] # 横軸と縦軸の意味\n",
    "sns.heatmap(R, \n",
    "            annot=True, # マップの実際の値を表示する\n",
    "            annot_kws={'size': 15}, # 値のフォントの大きさ\n",
    "            xticklabels=cr, # 横軸の意味\n",
    "            yticklabels=cr) # 縦軸の意味\n",
    "# グラフを表示する\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1029,
     "status": "ok",
     "timestamp": 1565663785843,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "GlzsBY7P1V5I",
    "outputId": "6ef7e98e-4357-4216-e2b2-602c7ae53946"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# sepal length と sepal width の相関係数 r と有意確率 p を求める\n",
    "plt.scatter(iris.iloc[:,0], iris.iloc[:,1]) # 分布を見る\n",
    "r,p = scipy.stats.pearsonr(iris.iloc[:,0], iris.iloc[:,1])\n",
    "print(r)\n",
    "print(p)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "-0.10936924995064937\n",
      "0.1827652152713699\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 691,
     "status": "ok",
     "timestamp": 1565663800833,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "_H4QOVvm1YJL",
    "outputId": "c67c69b2-4c7d-4584-cf85-2e6b598edf68"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# class ごとに　sepal length と sepal width の相関係数 r と有意確率 p を求める\n",
    "# classごとにデータを分ける\n",
    "irisSetosa = iris[iris.iloc[:,4]=='Iris-setosa']\n",
    "irisVersicolor = iris[iris.iloc[:,4]=='Iris-versicolor']\n",
    "irisVirginica = iris[iris.iloc[:,4]=='Iris-virginica']\n",
    "\n",
    "# setosa\n",
    "plt.scatter(irisSetosa.iloc[:,0], irisSetosa.iloc[:,1]) # 分布を見る\n",
    "plt.show()\n",
    "r,p = scipy.stats.pearsonr(irisSetosa.iloc[:,0], irisSetosa.iloc[:,1])\n",
    "print(r)\n",
    "print(p)\n",
    "\n",
    "# versicolor\n",
    "plt.scatter(irisVersicolor.iloc[:,0], irisVersicolor.iloc[:,1]) # 分布を見る\n",
    "plt.show()\n",
    "r,p = scipy.stats.pearsonr(irisVersicolor.iloc[:,0], irisVersicolor.iloc[:,1])\n",
    "print(r)\n",
    "print(p)\n",
    "\n",
    "# virginica\n",
    "plt.scatter(irisVirginica.iloc[:,0], irisVirginica.iloc[:,1]) # 分布を見る\n",
    "plt.show()\n",
    "r,p = scipy.stats.pearsonr(irisVirginica.iloc[:,0], irisVirginica.iloc[:,1])\n",
    "print(r)\n",
    "print(p)\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.7467803732639269\n",
      "4.751986580148901e-10\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.5259107172828247\n",
      "8.771860011973821e-05\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.45722781639411314\n",
      "0.000843462472370877\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 976,
     "status": "ok",
     "timestamp": 1565663873020,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "8MIk0Dzb1kB6",
    "outputId": "8e7aaac9-977e-481b-d9c5-0bb6e553d4ae"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "#######################################\n",
    "# 回帰\n",
    "from statsmodels import api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "ais = pd.read_csv(data_dir+'ais.csv')\n",
    "X = ais.Ht\n",
    "X = sm.add_constant(X) #定数項を導入\n",
    "Y = ais.Wt\n",
    "model=sm.OLS(Y,X) #線形モデルを作成\n",
    "result = model.fit() #線形モデルの係数を決定\n",
    "result.summary()\n",
    "\n",
    "plt.scatter(x=ais.Ht,y=ais.Wt) and plt.plot(ais.Ht, \n",
    "model.fit().predict(X),c=\"red\")\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f081f951780>]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1556,
     "status": "ok",
     "timestamp": 1565663918506,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "Z6Tf1FVL11U9",
    "outputId": "bc4d2ce8-0575-46f2-9ada-f1d36aaab165"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# 未知データ\n",
    "unknown = [185, 167, 215, 177]\n",
    "X2 = sm.add_constant(pd.DataFrame(unknown))\n",
    "Wt2 = model.fit().predict(X2) ##線形モデルの係数を決定＋データX2に対して予測\n",
    "plt.scatter(x=ais.Ht,y=ais.Wt) and plt.plot(ais.Ht, \n",
    "model.fit().predict(X),c=\"red\") and plt.scatter(x=unknown,y=Wt2, c='orange')\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f081f92bb70>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 818,
     "status": "ok",
     "timestamp": 1565663937808,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "17v8tLIu1678",
    "outputId": "2ca7f31b-815c-4e3c-e0bc-b1ccbf69f29e"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "##################\n",
    "# 重回帰\n",
    "# 例１：身長と体重からLBMを予測\n",
    "X = ais[['Ht', 'Wt']]\n",
    "X = sm.add_constant(X)\n",
    "Y = ais.LBM\n",
    "model=sm.OLS(Y,X)#線形モデルを作成\n",
    "result = model.fit()#線形モデルの係数を決定\n",
    "result.summary()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>LBM</td>       <th>  R-squared:         </th> <td>   0.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   737.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 13 Aug 2019</td> <th>  Prob (F-statistic):</th> <td>9.85e-93</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:41:12</td>     <th>  Log-Likelihood:    </th> <td> -590.29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   202</td>      <th>  AIC:               </th> <td>   1187.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   199</td>      <th>  BIC:               </th> <td>   1196.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>const</th> <td>  -36.6586</td> <td>    7.519</td> <td>   -4.875</td> <td> 0.000</td> <td>  -51.486</td> <td>  -21.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ht</th>    <td>    0.2587</td> <td>    0.053</td> <td>    4.922</td> <td> 0.000</td> <td>    0.155</td> <td>    0.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wt</th>    <td>    0.7325</td> <td>    0.037</td> <td>   19.941</td> <td> 0.000</td> <td>    0.660</td> <td>    0.805</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>22.720</td> <th>  Durbin-Watson:     </th> <td>   0.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  26.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.862</td> <th>  Prob(JB):          </th> <td>1.54e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.458</td> <th>  Cond. No.          </th> <td>4.61e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.61e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    LBM   R-squared:                       0.881\n",
       "Model:                            OLS   Adj. R-squared:                  0.880\n",
       "Method:                 Least Squares   F-statistic:                     737.1\n",
       "Date:                Tue, 13 Aug 2019   Prob (F-statistic):           9.85e-93\n",
       "Time:                        02:41:12   Log-Likelihood:                -590.29\n",
       "No. Observations:                 202   AIC:                             1187.\n",
       "Df Residuals:                     199   BIC:                             1196.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        -36.6586      7.519     -4.875      0.000     -51.486     -21.831\n",
       "Ht             0.2587      0.053      4.922      0.000       0.155       0.362\n",
       "Wt             0.7325      0.037     19.941      0.000       0.660       0.805\n",
       "==============================================================================\n",
       "Omnibus:                       22.720   Durbin-Watson:                   0.692\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               26.767\n",
       "Skew:                          -0.862   Prob(JB):                     1.54e-06\n",
       "Kurtosis:                       3.458   Cond. No.                     4.61e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.61e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 14
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 534
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1164,
     "status": "ok",
     "timestamp": 1565664073420,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "9ntC-8n92aON",
    "outputId": "2d4d679d-8c53-4443-8d64-3b7c44f70cb2"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# 実際の可視化\n",
    "plt.scatter(x=X.Ht, y=X.Wt, c=Y,\n",
    "            cmap='rainbow') and plt.colorbar()\n",
    "plt.show()\n",
    "# 予測の可視化\n",
    "H = X.Ht\n",
    "plt.scatter(x=X.Ht, y=X.Wt, c=model.fit().predict(X),\n",
    "            cmap='rainbow')  and plt.colorbar()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1551,
     "status": "ok",
     "timestamp": 1565664096260,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "PDojU1bw2e9A",
    "outputId": "8d98771c-25ca-4226-e62d-ad890ddab133"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "# 例２：\n",
    "# ワインの品質データ\n",
    "from statsmodels import api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "wine_data = pd.read_csv(data_dir+\"winequality-red.csv\", sep=\";\")\n",
    "X = wine_data.drop(\"quality\", axis=1)\n",
    "X = sm.add_constant(X)\n",
    "Y = wine_data['quality']#Yとしてqualityを取り出す\n",
    "model = sm.OLS(Y, X)#線形モデルの定義\n",
    "result = model.fit()#線形モデルの係数を決定\n",
    "result.summary()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>quality</td>     <th>  R-squared:         </th> <td>   0.361</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.356</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   81.35</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 13 Aug 2019</td> <th>  Prob (F-statistic):</th> <td>1.79e-145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>02:41:53</td>     <th>  Log-Likelihood:    </th> <td> -1569.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1599</td>      <th>  AIC:               </th> <td>   3162.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1587</td>      <th>  BIC:               </th> <td>   3227.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</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>const</th>                <td>   21.9652</td> <td>   21.195</td> <td>    1.036</td> <td> 0.300</td> <td>  -19.607</td> <td>   63.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>fixed acidity</th>        <td>    0.0250</td> <td>    0.026</td> <td>    0.963</td> <td> 0.336</td> <td>   -0.026</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>volatile acidity</th>     <td>   -1.0836</td> <td>    0.121</td> <td>   -8.948</td> <td> 0.000</td> <td>   -1.321</td> <td>   -0.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>citric acid</th>          <td>   -0.1826</td> <td>    0.147</td> <td>   -1.240</td> <td> 0.215</td> <td>   -0.471</td> <td>    0.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>residual sugar</th>       <td>    0.0163</td> <td>    0.015</td> <td>    1.089</td> <td> 0.276</td> <td>   -0.013</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>chlorides</th>            <td>   -1.8742</td> <td>    0.419</td> <td>   -4.470</td> <td> 0.000</td> <td>   -2.697</td> <td>   -1.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>free sulfur dioxide</th>  <td>    0.0044</td> <td>    0.002</td> <td>    2.009</td> <td> 0.045</td> <td>    0.000</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total sulfur dioxide</th> <td>   -0.0033</td> <td>    0.001</td> <td>   -4.480</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>density</th>              <td>  -17.8812</td> <td>   21.633</td> <td>   -0.827</td> <td> 0.409</td> <td>  -60.314</td> <td>   24.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pH</th>                   <td>   -0.4137</td> <td>    0.192</td> <td>   -2.159</td> <td> 0.031</td> <td>   -0.789</td> <td>   -0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sulphates</th>            <td>    0.9163</td> <td>    0.114</td> <td>    8.014</td> <td> 0.000</td> <td>    0.692</td> <td>    1.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alcohol</th>              <td>    0.2762</td> <td>    0.026</td> <td>   10.429</td> <td> 0.000</td> <td>    0.224</td> <td>    0.328</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>27.376</td> <th>  Durbin-Watson:     </th> <td>   1.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  40.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.168</td> <th>  Prob(JB):          </th> <td>1.27e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.708</td> <th>  Cond. No.          </th> <td>1.13e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.13e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                quality   R-squared:                       0.361\n",
       "Model:                            OLS   Adj. R-squared:                  0.356\n",
       "Method:                 Least Squares   F-statistic:                     81.35\n",
       "Date:                Tue, 13 Aug 2019   Prob (F-statistic):          1.79e-145\n",
       "Time:                        02:41:53   Log-Likelihood:                -1569.1\n",
       "No. Observations:                1599   AIC:                             3162.\n",
       "Df Residuals:                    1587   BIC:                             3227.\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "const                   21.9652     21.195      1.036      0.300     -19.607      63.538\n",
       "fixed acidity            0.0250      0.026      0.963      0.336      -0.026       0.076\n",
       "volatile acidity        -1.0836      0.121     -8.948      0.000      -1.321      -0.846\n",
       "citric acid             -0.1826      0.147     -1.240      0.215      -0.471       0.106\n",
       "residual sugar           0.0163      0.015      1.089      0.276      -0.013       0.046\n",
       "chlorides               -1.8742      0.419     -4.470      0.000      -2.697      -1.052\n",
       "free sulfur dioxide      0.0044      0.002      2.009      0.045       0.000       0.009\n",
       "total sulfur dioxide    -0.0033      0.001     -4.480      0.000      -0.005      -0.002\n",
       "density                -17.8812     21.633     -0.827      0.409     -60.314      24.551\n",
       "pH                      -0.4137      0.192     -2.159      0.031      -0.789      -0.038\n",
       "sulphates                0.9163      0.114      8.014      0.000       0.692       1.141\n",
       "alcohol                  0.2762      0.026     10.429      0.000       0.224       0.328\n",
       "==============================================================================\n",
       "Omnibus:                       27.376   Durbin-Watson:                   1.757\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               40.965\n",
       "Skew:                          -0.168   Prob(JB):                     1.27e-09\n",
       "Kurtosis:                       3.708   Cond. No.                     1.13e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.13e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 16
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 689,
     "status": "ok",
     "timestamp": 1565664114079,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "P--pQwsR2mWJ",
    "outputId": "0b5e78ff-461c-45cc-9fc4-0f1b61b8a9d0"
   }
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "correlation.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}