Created on Cognitive Class Labs

Drslrm6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bağımlı İki Örneklem T TEsti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pazarlama biriminin talep ettiği eğitim sonucu satışları arttı mı artmadı mı problemi\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "oncesi = pd.DataFrame([123,119,119,116,123,123,121,120,117,118,121,121,123,119,\n",
    "            121,118,124,121,125,115,115,119,118,121,117,117,120,120,\n",
    "            121,117,118,117,123,118,124,121,115,118,125,115])\n",
    "\n",
    "sonrasi = pd.DataFrame([118,127,122,132,129,123,129,132,128,130,128,138,140,130,\n",
    "             134,134,124,140,134,129,129,138,134,124,122,126,133,127,\n",
    "             130,130,130,132,117,130,125,129,133,120,127,123])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'BIRLIKTE' Veri Seti: \n",
      "\n",
      "     oncesi  sonrasi\n",
      "0     123      118\n",
      "1     119      127\n",
      "2     119      122\n",
      "3     116      132\n",
      "4     123      129 \n",
      "\n",
      "\n"
     ]
    },
    {
     "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>SKOR</th>\n",
       "      <th>ONCESI_SONRASI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>119</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>119</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>123</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>117</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>118</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>123</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>119</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>118</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>124</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>125</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>115</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>115</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>119</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>118</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>117</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>117</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>120</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>120</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>121</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>117</td>\n",
       "      <td>ONCESI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>128</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>138</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>140</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>134</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>134</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>124</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>140</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>134</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>129</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>129</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>138</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>134</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>124</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>122</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>126</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>133</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>127</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>130</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>130</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>130</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>132</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>117</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>130</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>125</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>129</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>133</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>120</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>127</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>123</td>\n",
       "      <td>SONRASI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SKOR ONCESI_SONRASI\n",
       "0    123         ONCESI\n",
       "1    119         ONCESI\n",
       "2    119         ONCESI\n",
       "3    116         ONCESI\n",
       "4    123         ONCESI\n",
       "5    123         ONCESI\n",
       "6    121         ONCESI\n",
       "7    120         ONCESI\n",
       "8    117         ONCESI\n",
       "9    118         ONCESI\n",
       "10   121         ONCESI\n",
       "11   121         ONCESI\n",
       "12   123         ONCESI\n",
       "13   119         ONCESI\n",
       "14   121         ONCESI\n",
       "15   118         ONCESI\n",
       "16   124         ONCESI\n",
       "17   121         ONCESI\n",
       "18   125         ONCESI\n",
       "19   115         ONCESI\n",
       "20   115         ONCESI\n",
       "21   119         ONCESI\n",
       "22   118         ONCESI\n",
       "23   121         ONCESI\n",
       "24   117         ONCESI\n",
       "25   117         ONCESI\n",
       "26   120         ONCESI\n",
       "27   120         ONCESI\n",
       "28   121         ONCESI\n",
       "29   117         ONCESI\n",
       "..   ...            ...\n",
       "10   128        SONRASI\n",
       "11   138        SONRASI\n",
       "12   140        SONRASI\n",
       "13   130        SONRASI\n",
       "14   134        SONRASI\n",
       "15   134        SONRASI\n",
       "16   124        SONRASI\n",
       "17   140        SONRASI\n",
       "18   134        SONRASI\n",
       "19   129        SONRASI\n",
       "20   129        SONRASI\n",
       "21   138        SONRASI\n",
       "22   134        SONRASI\n",
       "23   124        SONRASI\n",
       "24   122        SONRASI\n",
       "25   126        SONRASI\n",
       "26   133        SONRASI\n",
       "27   127        SONRASI\n",
       "28   130        SONRASI\n",
       "29   130        SONRASI\n",
       "30   130        SONRASI\n",
       "31   132        SONRASI\n",
       "32   117        SONRASI\n",
       "33   130        SONRASI\n",
       "34   125        SONRASI\n",
       "35   129        SONRASI\n",
       "36   133        SONRASI\n",
       "37   120        SONRASI\n",
       "38   127        SONRASI\n",
       "39   123        SONRASI\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#BIRINCI VERI SETI\n",
    "BIRLIKTE = pd.concat([oncesi, sonrasi], axis = 1)\n",
    "BIRLIKTE.columns = [\"oncesi\",\"sonrasi\"]\n",
    "\n",
    "print(\"'BIRLIKTE' Veri Seti: \\n\\n \", BIRLIKTE.head(), \"\\n\\n\")\n",
    "\n",
    "\n",
    "#IKINCI VERI SETI\n",
    "\n",
    "GRUP_ONCESI = np.arange(len(oncesi))\n",
    "GRUP_ONCESI = pd.DataFrame(GRUP_ONCESI)\n",
    "GRUP_ONCESI[:] = \"ONCESI\"\n",
    "A = pd.concat([oncesi, GRUP_ONCESI], axis = 1)\n",
    "GRUP_SONRASI = np.arange(len(sonrasi))\n",
    "GRUP_SONRASI = pd.DataFrame(GRUP_SONRASI)\n",
    "GRUP_SONRASI[:] = \"SONRASI\"\n",
    "B = pd.concat([sonrasi, GRUP_SONRASI], axis = 1)\n",
    "\n",
    "#TUM VERIYI BIR ARAYA GETIRME\n",
    "AB = pd.concat([A,B])\n",
    "AB.columns = [\"SKOR\",\"ONCESI_SONRASI\"]\n",
    "AB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "import seaborn as sns\n",
    "sns.boxplot(x = \"ONCESI_SONRASI\", y = \"SKOR\", data = AB);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 454.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "(sns\n",
    "  .FacetGrid(AB, \n",
    "             hue='ONCESI_SONRASI', \n",
    "             height=5, \n",
    "             xlim=(100, 200))\n",
    "  .map(sns.kdeplot, 'SKOR', shade=True)\n",
    " .add_legend()\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#varsayımlar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9543654918670654, 0.10722342133522034)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(BIRLIKTE.oncesi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9780087471008301, 0.6159457564353943)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(BIRLIKTE.sonrasi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import levene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=8.31303288672351, pvalue=0.0050844511807370246)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levene(BIRLIKTE.oncesi,BIRLIKTE.sonrasi )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-9.281533480429937, pvalue=2.0235251764440722e-11)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.ttest_rel(BIRLIKTE.oncesi,BIRLIKTE.sonrasi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pazarlama departmanı haklı çıkmış bilimsel olarak eğitim işe yaramış"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonparametrik Bağımlı İki Örneklem Testi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WilcoxonResult(statistic=15.0, pvalue=2.491492033374464e-07)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.wilcoxon(BIRLIKTE.oncesi, BIRLIKTE.sonrasi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# İki Örneklem Oran Testi"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Yesil Buton: 300 tiklanma 1000 goruntulenme Kirmizi Buton: 250 tiklanma 1100 goruntulenme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.proportion import proportions_ztest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "basari_sayisi = np.array([300, 250])\n",
    "gozlem_sayisi = np.array([1000,1100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.7857863233209255, 0.0001532232957772221)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportions_ztest(basari_sayisi, gozlem_sayisi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "300/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22727272727272727"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "250/1100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Varyans Analizi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "internet sitesi ana sayfa içeriği üç farklı türe göre şekillendirildiğinde hangisi daha avantajlı?"
   ]
  },
  {
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GRUP_A</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.133333</td>\n",
       "      <td>2.224214</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.75</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRUP_B</th>\n",
       "      <td>30.0</td>\n",
       "      <td>31.700000</td>\n",
       "      <td>2.937862</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>31.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRUP_C</th>\n",
       "      <td>30.0</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>2.808239</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean       std   min    25%   50%    75%   max\n",
       "GRUP_A   30.0  30.133333  2.224214  25.0  28.25  30.0  31.75  34.0\n",
       "GRUP_B   30.0  31.700000  2.937862  26.0  30.00  31.0  34.00  38.0\n",
       "GRUP_C   30.0  38.100000  2.808239  33.0  36.00  38.0  40.00  43.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.DataFrame([28,33,30,29,28,29,27,31,30,32,28,33,25,29,27,31,31,30,31,34,30,32,31,34,28,32,31,28,33,29])\n",
    "\n",
    "B = pd.DataFrame([31,32,30,30,33,32,34,27,36,30,31,30,38,29,30,34,34,31,35,35,33,30,28,29,26,37,31,28,34,33])\n",
    "\n",
    "C = pd.DataFrame([40,33,38,41,42,43,38,35,39,39,36,34,35,40,38,36,39,36,33,35,38,35,40,40,39,38,38,43,40,42])\n",
    "\n",
    "dfs = [A, B, C]\n",
    "\n",
    "ABC = pd.concat(dfs, axis = 1)\n",
    "ABC.columns = [\"GRUP_A\",\"GRUP_B\",\"GRUP_C\"]\n",
    "ABC.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#varsayimlar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9697431921958923, 0.5321715474128723)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(ABC[\"GRUP_A\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9789854884147644, 0.7979801297187805)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(ABC[\"GRUP_B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9579201340675354, 0.273820161819458)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(ABC[\"GRUP_C\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=1.0267403645055275, pvalue=0.36247110117417064)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.levene(ABC[\"GRUP_A\"], ABC[\"GRUP_B\"],ABC[\"GRUP_C\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "varsayımlar sağlandı doğrulandı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=74.69278140730431, pvalue=1.3079050746811477e-19)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "f_oneway(ABC[\"GRUP_A\"], ABC[\"GRUP_B\"],ABC[\"GRUP_C\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "içerik stratejileri karşılaştırıldığında %95 oranında strateji farklılığı olduğu bilimsel olarak tespit edilmiştir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonparametrik Varyans Analizi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=54.19819735523783, pvalue=1.7022015426175926e-12)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import kruskal\n",
    "kruskal(ABC[\"GRUP_A\"], ABC[\"GRUP_B\"],ABC[\"GRUP_C\"])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%95 ornında gruplar arasında farklılıklar vardır."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Korelasyon Analizi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "tips = sns.load_dataset('tips')\n",
    "df = tips.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_bill\"] = df[\"total_bill\"] - df[\"tip\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.98</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.68</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.51</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.37</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.98</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       15.98  1.01  Female     No  Sun  Dinner     2\n",
       "1        8.68  1.66    Male     No  Sun  Dinner     3\n",
       "2       17.51  3.50    Male     No  Sun  Dinner     3\n",
       "3       20.37  3.31    Male     No  Sun  Dinner     2\n",
       "4       20.98  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28c1c3320>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot.scatter(\"tip\",\"total_bill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fb28c19e5c0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = \"total_bill\",y = \"tip\", data = df, kind = \"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.897811233997345, 8.20057563521992e-12)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro(df[\"tip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(shapiro(df[\"total_bill\"])[1], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5766634471096374"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"tip\"].corr(df[\"total_bill\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.593691939408997"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"tip\"].corr(df[\"total_bill\"], method = \"spearman\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5766634471096381, 5.018290084948595e-23)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(df[\"total_bill\"], df[\"tip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(pearsonr(df[\"total_bill\"], df[\"tip\"])[1], 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonparametrik Korelasyon Testi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.593691939408997, pvalue=1.2452285137560276e-24)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.spearmanr(df[\"total_bill\"], df[\"tip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KendalltauResult(correlation=0.4400790074919885, pvalue=7.131027725873721e-24)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kendalltau(df[\"total_bill\"], df[\"tip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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