Homework/Day_38_HW.ipynb

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    "## 作業\n",
    "在鐵達尼資料集中，今天我們專注觀察變數之間的相關性，以 Titanic_train.csv 中，首先將有遺失值的數值刪除，並回答下列問題。\n",
    "* Q1: 透過數值法計算  Age 和  Survived  是否有相關性?\n",
    "* Q2:透過數值法計算  Sex 和 Survived  是否有相關性?\n",
    "* Q3: 透過數值法計算  Age 和  Fare 是否有相關性?   \n",
    "  \n",
    "提示:    \n",
    "1.產稱一個新的變數 Survived_cate ，資料型態傳換成類別型態；      \n",
    "2.把題目中的 Survived 用 Survived_cate 來做分析；       \n",
    "3.首先觀察一下這些變數的資料型態後，再來想要以哪一種判斷倆倆的相關性。     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install researchpy\n",
    "#!pip install pingouin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
     "elapsed": 2242,
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     "timestamp": 1601990565808,
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   "source": [
    "# import library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics\n",
    "import pingouin as pg\n",
    "import researchpy   \n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M6YWLLdqE8GI"
   },
   "source": [
    "## 讀入資料"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <th>3</th>\n",
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       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train = pd.read_csv( 'Titanic_train.csv' )\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  ColumnName  MissingCount  Percentage(%)\n",
       "0   Embarked             2           0.22\n",
       "1        Age           177          19.87\n",
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   ],
   "source": [
    "# 檢查訓練資料集中是否有遺失值 ?\n",
    "def Missing_Counts( Data ) : \n",
    "    missing = Data.isnull().sum()  \n",
    "    missing = missing[ missing>0 ]\n",
    "    missing.sort_values( inplace=True ) \n",
    "    \n",
    "    Missing_Count = pd.DataFrame( { 'ColumnName':missing.index, 'MissingCount':missing.values } ) \n",
    "    Missing_Count[ 'Percentage(%)' ] = Missing_Count['MissingCount'].apply( lambda x:round(x/Data.shape[0]*100,2) )\n",
    "    return  Missing_Count\n",
    "\n",
    "Missing_Counts(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cITJCaELE8GK"
   },
   "source": [
    "### 這邊我們做一個調整，把 Survived 變成離散型變數 Survived_cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked Survived_cate  \n",
       "0        0         A/5 21171   7.2500   NaN        S             0  \n",
       "1        0          PC 17599  71.2833   C85        C             1  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S             1  \n",
       "3        0            113803  53.1000  C123        S             1  \n",
       "4        0            373450   8.0500   NaN        S             0  \n",
       "..     ...               ...      ...   ...      ...           ...  \n",
       "886      0            211536  13.0000   NaN        S             0  \n",
       "887      0            112053  30.0000   B42        S             1  \n",
       "888      2        W./C. 6607  23.4500   NaN        S             0  \n",
       "889      0            111369  30.0000  C148        C             1  \n",
       "890      0            370376   7.7500   NaN        Q             0  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Survived_cate'] = df_train['Survived'].astype('object')\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YN-BISG4E8GM"
   },
   "source": [
    "###  Q1: 透過數值法計算  Age 和  Survived  是否有相關性?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R3AArKqKcbjuTc4d3a3Db4PCRI8SKq6b9unioGkicxqGUSt++fXuZX2pR4jezus7KObk5n1uDe5a6u7vp6e4eC+HpMIFyxFfEkSNHHKhMqfxkjOHAvn0sL5v9EOPi0hheDxw4cMCGyjJHg3uWUltDWiU103+xCLHiag7k8C5lSmXayZMnGRganvUwCUCRFxaXxtmfYxtOaXDP0qFDh0BkRj1ugFhJDc1NTYTDYZsrUyo/pXrH09kR8GKWl0c4fOgQsZg918sEDe5Z2rdvPyZUBV7/jF5vldYSj8dpbGy0uTKl8tOhQ4cI+oR5aZ54M5Vl5THCkWhOranQ4J6FeDzO/gP7iZbUzvwapXOB3Ls5opRbDh06yJLSKJ5pbiw1maXJnnsu7RSowT0Lzc3NhEdHiZfWzfgaxh+CYLkGt1JpiMViNDU1sbQs/RNvplJXbBHyS04dZabBPQupFVezCW6AaEktu3bvwZjZTW1SKt8dP36caDSW9onu6fAILC6JcDSHZndpcM/Cnj17kEApJs1TbyYTL6tnoL8vJze7USqTUveCFpXZM76dsqgsRnNzE/G4vdd1igb3DBlj2LV7D5GSubO+Vqy0Hkj8IlBKTa6xsZEiLzSE7A3YxaWJG5SnTp2y9bpO0eCeoVOnTtHb0028rH7W1zLBcqQoxK5du2yoTKn81dTUxIKSuG03JlMWliZ+ETQ3N9t7YYdocM9QKmRjZQ2zv5gIkZI6duzcpePcSk3CGENz01EWlNh3YzJlXvKXQVOO7NapwT1Du3fvRopCmGD6p95cTLy8nu6uzrFDh5VS5+vp6aGvf5AFpfaPQ/s9UF9itMedz4wxbN+xg0hpXdpnTE4lnuy563CJUhNL7Xu/sMSZFY4LQhGONWuPO2+dPn06sbGUHcMkSVawAikqZufOnbZdU6l8kgpuJ3rcAAtK4rSdaWdkZMSR69tJg3sGUr1iO25MjtFxbqUu6tixY5QVCeWz3Mp1MqlfCLmwP74G9wzs2rULKQpN+8SbqcTLG+jqPKvj3EpNoLmpiQUlEbtGJ98jNQST6tlnMw3uaTLGsGPnLiIl9o1vp6R68DrOrdT5LMui+VizY+PbALXFFgFvbsws0eCepra2Nrq7OomX2zhMkmQF5yD+Ynbvzr0z8JRyUmtrK+FwxLHxbUgsfZ9fEqepKft36tTgnqZUqNo6vp0iQqRkLjt3aXArNV7qeD879yiZyOLSKEePHMn6+0wa3NO0e/duxB/ECs5x5Prxsno62s/Q0dHhyPWVykVHjhzB60nM/HDSkrIYQ8MjWX+fSYN7mnbt3p3Yn8ShOyTxssROg6mdB5VScPjwIRaWWvgcTqzU3tzZvsWrBvc0dHV1caatbSxcnWCFqhBfkY5zK5UUi8U4sH8/y8udP95vQUmcgBf279/veFuzocE9DXbtv31R4iFaUstu3SlQKSCx8dNoOMIKm86YvBifB5aVRdm7J7s7Thrc07B3717E68MKzeBE92mIl9bRcuIEAwMDjrajVC5I/fW5Yk5mDvNdMSdKY1Mzg4ODGWlvJjS4p2HPnj3EQjXgcfZti5fWYYzJ+j/XlMqEbdu2UV9iqAlaGWnvisoolmVl9XoKDe40DQ8P09TURMzJYZKkeEkNiOg5lKrgRSIRdu3ayRWVoxlrc3lFjIBP2LZtW8banC4N7jQdOnQIy7LGTmV3lNePFapm714NblXYdu/eTTgc4coq+/fgnozPA6vmhNny1ptZO59bgztNqd5vRoIbiJXO5cDBA8RimRnXUyobvfrqqwR9wuWVmQtugGtqIrR3nOXw4cMZbTddGtxp2rt3LyZUBb5ARtqLl9YRjUTGVowpVWhisRhvvPYqV1WNUuTNbNsfqI3gFXjttdcy23CaNLjTEI/H2bdvP9GS2sy1mRxL13FuVah27NhBb/8A186NZLztUr9hVWWUl17cjGVl5qbodEwZ3CJSJyL/LiK/SH69SkQecb607HHs2DFGRoadnb99AVMUgmC5nvyuCtbPf/5zSovgqprMBzfARxpGae84m5WHm6TT4/4e8CtgXvLrI8BXnCooG40tvHFwxeREUgtxsvUGiVJO6evr4803Xuf6uSP4XRoX+EBNhJA/8Qsk26TzltQYY54ELABjTAxwdqeXLLNnzx4kUIopKs1ou/Gyevr7+jh58mRG21XKbb/4xS+IxuJ8dJ7zy9wnU+SF6+tGeO21V+np6XGtjomkE9xDIlINGAARWQv0OVpVFjHGsHPnLkc3lppMrDSxdawOl6hCEovFeHrjU1w2J8YiB/ffTset80eJRmM899xzrtZxoXSC+6vAs8AlIvIm8ATwpXQbEBGviOwUkU3Jr6tEZLOIHE1+rJxR5Rly6tQpent7nNl/ewomWI4UhbJ6BZdSdnvzzTfpONvJbQvdP7S3ocRidXWU//rZ00SjmZ2SeDFTBrcxZgfwMeB64IvA5caY6XQBvwwcHPf114GXjDErgJeSX2et1I2JWLl9J7qnTYRIaR3bd+zQcW5VEIwx/PCHP6A2ZPiASzclL/TxBSN09/TywgsvuF3KmHRmlfwmsAG4FFgJ3Cki60RkypUoIrIAWA/827iH7wIeT37+OHD3dIvOpO3bt0OgBBMod6X9eFkDPd3dtLS0uNK+Upm0fft2Dh8+wh0Lh/BkdmRyUldURVlaHucH3//PrFkQl85QySMkgvczyf/9K4nhkzdF5LNTvPb/Af6Y5I3NpDpjTBtA8uOEvwBE5Asisk1Etp09ezaNMu1nWRY7duwkWtqQ8fHtlFh5YjLPjh07XGlfqUz6zyeeoDIIH25w76bkhUTgzsXDtLad4ZVXXnG7HCC94LaA9xlj7jHG3AOsAsLAdcD/PdmLROQOoMMYs30mhRljvmOMWWOMWVNbm7mFL+MdPXqUgYH+sfCcrkDLVrzDXXiHuyg+9HMCLVunfQ0TKINgOe++++6MalAqV+zYsYPde/Zw+8Ih16YATuYDNREWlFo88fj3sqLXnc7bs8QY0z7u6w5gpTGmG7jYaP0NwAYROQ78GLhZRL4PtItIA0DyY9Yervj2228DEK+YP6PXe4a7kXgUiUfxDZzBM9w9/YuIECmbx/btO7Lq5ohSdjLG8O//9m9UBuGmeZnbCTBdHoFPLhni5KnTvPjii26Xk1Zwvy4im0TkYRF5GHgGeE1ESoDeyV5kjPnvxpgFxpglwAPAy8aYB0nMUHk4+bTU9bLS1q1vY0pqMP5iV+uIVSwgHB7VcyhV3nrnnXfYf+AAdy0ezPi+JOlaUxthcZnF49/9D9c7UekE96PAd4Grkv97BzDGmCFjzE0zaPOvgVtF5Chwa/LrrNPb28vBgweIVCxwuxTi5Q3g8fLWW2+5XYpStrMsi3/91+9QGzJ8NIvGti8kAvcuHaStvcP11ZTpTAc0QBOJYZFPAus4f3rflIwxvzbG3JH8vMsYs84YsyL5cQbjB87bsmULxhhicxa5XQp4/cTK5vHa62/otECVd1555RUaG5u4Z8mg46e4z9bq6iiXzonx+Pe+y8iIe/PMJ32bRGSliPwPETkI/BNwEhBjzE3GmH/KWIUuef311yFQihWqdrsUAGKVi+hoP0NTU5PbpShlm1gsxn/8+7+xsMxibV12zNu+GBG4b9kQ3T29PPXUU67VcbHfb4dI9K7vNMZ82Bjz/1Ige5QMDQ3xzjvvEpmzyLVpgBeKJWt59dVX3S5FKds8//zznG5t496lg1kzb3sqK+fEuLomwo9++EP6+tzZ/eNiwX0PcAZ4RUT+VUTWATny1s7OW2+9RSwWJVq51O1Sxhh/MfGyel5+5RUdLlF5YXR0lMe/911WzIlxVXVuzZi6d9kwIyMj/OhHP3Kl/UmD2xjzM2PM/cBlwK+Bx4A6EfkXEbktQ/W54sWXXkoMk2TomLJ0RSuXcvrUKT0VR+WFjRs30t3Ty28tG8qWP2zTtrA0zvX1YZ7euBE3Fgimc3NyyBjzg+TNxQXALrJ8f5HZ6O3t5d133iFcuSxrhklSolVLweNl8+bNbpei1KwMDg7y4x/9cOxmn92+fyTEiQEvJwa8fGtHOd8/ErK9jd9cOkw8FuX73/++7deeyrTu4Rpjuo0x/58x5manCnLbyy+/jGVZxKovcbuU9/IFiJYv4IXNm7Ni9ZZSM/XUU08xMDjEPUuHHLl+y6CPkbiHkbiHQ71+WgZ9trdRW2zx0YZRnt+0iTNnzth+/YvJ8sk3mWWM4blNm7BKarBC2bnbbKxmOX29vWOrOpXKNQMDAzz5kx9zTU2EpeW5Pd9hw5IRMHGeeOKJjLarwT3OkSNHONbcTKRmhdulTCpWsRApCvH888+7XYpSM/Kzn/2M4ZFRPrl02O1SZq06aHFjwyi/+tUvaW9vn/oFNtHgHmfTpk2Ix0e0apnbpUzO42G06hK2bt3qyk0RpWZjZGSEp376JFdVR1hUltu97ZTbF42AZfHkk09mrE0N7qShoSFeeGEzkaql4Au4Xc5FRWsvxbIsNm3a5HYpSk3L888/T//AIHcucf90G7vUFFtcXzfKpueepbd30u2bbKXBnfTCCy8QDo8Sqb3M7VKmZILlxCoW8Oxzm/QmpcoZ8XicjU/9lBVzYqyoyK9/t7cvHiUciWZsCFODm8RNyY1PP41VUotV6s7e39MVmXsZPd1dvPHGG26XolRa3n77bdrOtPPxBfnT206ZXxLn8qrE2ZSZ6ExpcAPbtm3j1MmThOe+z+1S0havWADBcn76U/f2S1BqOp5+eiOVQbLmLEm73Tp/hLOdXbz55puOt6XBDTy1cSNSFCJWlT1L3KckHkZrL2P//n0cPnzY7WqUuqiOjg62b9/Ox+qHs34HwJm6qiZKZRB++YtfON5Wnr6F6WtpaeHtrVsZrVkJnizdwX0S0ZqViNfv6i5lSqXjxRdfxBi4oT5799ueLY/A9XNHePudd+jp6XG2LUevngM2btwIHi/RHBomGeMrIly9gpdffpnOzk63q1FqUi/86pesqIhRF7KmfnIOu6E+jGVZvPzyy462U9DB3d/fzy9+8UuiVctcP55spiJ1q4jH4/zXf/2X26UoNaFTp05x/EQLH5ybv73tlAWlceaXWrzx+uuOtlPQwf3cc88RiYSJ1F3udikzZoLlROcs4r+eeYZwOP9/MFTu2bJlCwBX5+lNyQtdXR1mz949DAwMONZGwQZ3LBZj49NPEy9vwApVuV3OrETrLmdwYIAXXnjB7VKUeo8tW95ifqnF3OL8HiZJubomQjxusW3bNsfaKNjgfu211+ju6iKcw73tlHhZPaakmp8+9ZQesqCySiwWY/++fVw+p3D+GlxWFiPgE3bv3u1YGwUb3E89tRGC5cQrFrpdyuyJMDp3FS0nTrBz5063q1FqTGNjI+FIlJUO7LmdrbweWF4eYd/ePY61UZDBfeTIEQ4c2M9o7fuy7rCEmYpVLUX8QZ7auNHtUpQas2/fPgBWVOTW0WSztaI8SnPzMYaHndkBsSCD+5lnnkG8fqI1y90uxT4eH6M1K9ny1lt0dHS4XY1SADQ3N1MeECoDhTWEt7gshmUMJ06ccOT6BRfcg4ODbN78IuHK7N8FcLqitZdijNG9ulXWOHHiOA3FhdXbBpgXSmxZq8Ftk5deeolIJEy09lK3S7GdCZSN7RoYj+fHXscqt7UcP868UOGMb6fMLbbwehIrs51QcMH985//AhOqwiqpcbsUR0RrVtLT3cX27dvdLkUVuHA4zMDQMNXBwpgGOJ7XA5VBHFvRXFDBffz4cQ4fPkS4enne3JS8UGzOQsQf5BcZ2OhGqYvp7u4GoKKo8IIboMIfG3sP7FZQwf3iiy+CSHae4G4Xj5dw5RLeeOMNx+5oK5WO1EZLcwo2uC26O505XrBggtsYw8uvvEK8rCFz+5LEIwSDQe69916CwSDEM7PkN1a1jGg0ytatWzPSnlITGR0dBSDgzfyMkpGYnPezNxLL/F/YAa8Zew/sVjDB3dTUROvp00Qrl2SsTYlFuOOOO/j93/991q9fj8QyE9zx0rlIUYhXXnklI+0pNZFoNDGbxI39t4djct7P3rALwe3zmLH3wPZrO3LVLJTa6CZWuThjbRpfEZs2beFS+zYAABxgSURBVBqbomd8ocw0LB7CFYt45913iUQiFBUVZaZdpcY5F9yZ73GHfOa8n706X+Zr8AlEHTrGrGB63Fu2bMUqqcns9q3eIkZHR9m4cWPiTyZv5gI0NmcB4dFR9u7dm7E2lRrP7/cDELMy39st9pnzfvaKXQjumDn3HtitIIJ7YGCAgwcPEK1Y4HYpGRMvawCPl3fffdftUlSBCgQSC9wiBbqkIBIXAgFnOmsFEdz79+/HGJMIs0Lh9RMvqWGXgzuUKXUxwWAQgNF4fk69nUo4LgSDzvyFXxDBvW/fPhAP8TxddDOZWEkdR48c0QMWlCsqKysB6IsURMy8R1/US2VVtSPXLoh39ODBg5hQFXidGW/KVlZpLfF4nMbGRrdLUQWoqipxQEkhB3dNjTOdxYJ4R5uajxELznG7jIyLFyd6PMePH3e3EFWQ/H4/cyrK6RotiJg5T8yC3lGortYe94wMDAzQ29ONVVx4wW0CZYjXp8GtXLNo0WLaRgpm1vGY9hEvloFFixY5cv28D+729nYArECZy5W4QAQTKBt7D5TKtMVLlnB62E+hnajXOuQFYMmSJY5cP++Du7e3FyCz87ezSMwboLu7x+0yVIFaunQpQxFDTzjvo+Y8LYNePCIsXOjM0Yh5/26OBbcv6HIl7jC+Yrp6nNmhTKmprFq1CoDG/sIaLjna52fZsqUUF+t0wBmxrMTOZEby/j91YiIYq8D+TlVZY/ny5QSK/BzpLZzgjlvQNODniitXO9ZGAaVZoYaXydetx1UO8Pl8rFp1OYf68uuYwIs5PuAjHIMrr7zSsTbyPrhTGyyJVaDrbq04RX7dZEq557q1a2kZ8NBdINMCd3X58YiwZs0ax9rI+3eytrYWAIkMulyJO3zRIerr69wuQxWwtWvXAolAKwS7u4OsWrWKiooKx9rI++Cur68HwBMuzOD2RIaoq9PgVu5ZvHgxDfV1bDub/8MlZ0c8HO/3cP0NNzjaTt4Hd2VlJSWlZXiHu9wuJeMkPICJjjo2l1SpdIgIt9x6G/t7/PSG8/uGy5b2xC+nm2++2dF28j64PR4PH7j6KvyDZ9wuJeO8/W0AXH311S5XogrdLbfcgjGwtT1/e93GwFvtxaxefeXYX/pOyfvgBrjqqqtgdAAZ7XO7lIzy9bdSVl6uPW7lusWLF3PZpSt59Uxx3q6iPNrno3VIuO22jzveVkEE90c+8hFEBH/nUbdLyZxYGH/vCW6+6SZE5wOqLHD3J3+T04MeDvbk55zuzaeClJaEWLduneNtORbcIrJQRF4RkYMisl9Evpx8vEpENovI0eTHSqdqSJk7dy5r164l2NUIyQU5+c7f1QhWnDvuuMPtUpQC4KabbqKivIwXTuXf9hPdYQ/bzga4ff0djq2WHM/JHncM+Jox5n3AWuBREVkFfB14yRizAngp+bXjNmzYgIkM4+sqgL2prTjBjoNceullrFixwu1qlAISR5ltuOtudnYWcTq5CVO++FVLEIPwyU9+MiPtORbcxpg2Y8yO5OcDwEFgPnAX8HjyaY8DdztVw3hr167l0ssuo7htJ8SdOXn5QlaoCuP1Y7x+YmX1WKGqjLTr7zgEo/38t//2SEbaUypd99xzD0WBIp47nj+97v6I8HJrMetuuYWGhswcj5iRMW4RWQJcDbwN1Blj2iAR7sDcSV7zBRHZJiLbzp49a0cNPPp7vwfhIYra9836eukIL1pLPFRNPFTNyGW3E1601vlGY2GKz+zmA9dcw7XXXut8e0pNw5w5c7jrrrvZ0hGgfTg/brH96mSQiAUPPvhgxtp0/J0TkVJgI/AVY0x/uq8zxnzHGLPGGLMmtfpxtlavXs1HPvpRgm278Yzk51anwRNbkXiU3/vd33W7FKUmdP/991Pk97PxWMjtUmatNyy8cLqEG2+8icWLF2esXUeDW0T8JEL7B8aYp5MPt4tIQ/L7DUCHkzVc6KuPPUZpaSmhY69Bnu1f4us+hr+7iYcffojly5e7XY5SE6qurua+37qfre0Bmvtze6z7Z8dCxIzwyCOZHZZ0claJAP8OHDTG/P24bz0LPJz8/GHgGadqmEhlZSV//Ed/iAx1ETi9PZNNO0rCA4RatrDy0kv5zGc+43Y5Sl3Upz71KSrKy/hJU2nOzutuHfLwaluQDRvuYsGCBRlt28ke9w3AZ4GbRWRX8n+3A38N3CoiR4Fbk19n1Ec+8hE2bNhA0Zl9+LqaM928/eIxSppeJuj38D/+7M/w+fJznqzKHyUlJTz8ud/mYI+P7Z25t3ulMfCDo6UEg0EeeuihjLfv2E+4MeYNYLKVH87PUJ/Cl770JZqamzlw8A0GgxVYJc6cxuw4Ywgefx0Z7uYv/vqvM/6bX6mZ2rBhA889+ww/bDzBlVVdBGwcNVlUGuPEQOKCi8viLCq1dybZjk4/e7v9PProI1RWOr4U5T3y47buDPj9fv7yG9+gas4cSppeRHJ098Ci1p34u4/xhc9/nuuuu87tcpRKm8/n48tfeYzOEXj+hL3TAx9cOczisjiLy+L8yQf6eXDlsG3XjsThh01lLF60MGPzti9UsMENUFVVxbe//TcUewwljZshFna7pGnxdxwi0LqL9evX86lPfcrtcpSatquuuoqbb76ZTS0h2nJkeuCzx4s5Oyx85bGvujYsmRvvlIMuueQSvvWtv8IX7qek8SWwMrM4Z7Z8PScItmzhuuvW8thjj+l+JCpnPfroowSCxTx+uCzrb1SeHvLy/MkQt956q6u7bhZ8cENi29M/+ZM/wTPYTnHTK1m/n4m3v41Q86+59NJL+Yu/+HO9GalyWnV1NV/44u9woMfHW2ey90alZeB7h0sJhUp49NFHXa1Fgztp3bp1PPaVr+DrPUnw2Gtk669+z+BZShpfZNGihfztt7+dkQ1tlHLanXfeyar3XcYPm8oYiGTnX4+vtQU43Ovjd37395gzZ46rtWhwj3PXXXfx+c9/Hn93M4GWLVkX3p6RHkobNzO3tpq//7u/o7y83O2SlLKFx+PhD//ojxmOe/hxY/atqOyLCD9uKuX9q1dz++23u12OBveFPv3pT/PAAw9Q1HGIotM73C5njIQHKD36AhWlIf7h7/+e6uocnb6o1CSWLVvGAw98itfPBDnQnV3Dfz84UkLUePnaH/5hVtxP0uC+gIjwxS9+kfXr1xNo243/TGY2pLpoTdFhSo/8ipBP+Pu/+9/MmzfP7ZKUcsRDDz1EQ30d3ztaTjRLbjXt6/aztSPAZx78LIsWLXK7HECDe0Iiwle/+lU+9rGPETz5Dr6uJveKiUcpObqZgInw7W//DcuWLXOvFqUcFggE+MpjX+XMkPBzm+d2z0QkDo8fKWP+vAY+/elPu13OGA3uSXi9Xv70T/+U97///RQffx1vf2vmi7AsQk0v4x3p4Rvf+J9cfvnlma9BqQy77rrruPHGG3n2RIiOEXcj6vmWYtqHhce++jWKirJnxosG90UUFRXxzW9+k0ULF1LS9DKe4QxuBWsMwRNv4u07zde+9jVdFakKyqOPPorXX8RPGktcq6Fr1MPzLSFuvPFG1qxZ41odE9HgnkJZWRl/++1vU1FWQknTSxlbXenvOIC/8yif/exnWb9+fUbaVCpb1NbW8unPPMi7Z4s45NLhwj9tCmE8Pn7nd37HlfYvRoM7DXV1dfzlN76BNzpEcfOrjk8T9A6cIXjyHa6//np++7d/29G2lMpW999/P3NrqvlBYxlWhmfmNvX7eKs9wP33P0B9fX1mG0+DBnearrzySv7gD/4AX98pilp3OdaOREcoaX6FefPmJVZzevT/IlWYAoEAj3z+C5wY8LAjw1u//uxYiPKy0qy6ITmepsI0bNiwgVtuuYVA2y48Q532N2AMweNv4rVi/NU3v0lpaan9bSiVQ9atW8f8eQ08c7wkY+vhmvp87Onyc/8DnyIUyr7FQKDBPS0iwpe//GWqqqqSR5/ZuyGVr6sRX28LX/ziF1i6dKmt11YqF/l8Ph56+HOcGPCws9OfkTafOVFMeVmpa1u2pkODe5rKysr471//OjLSS1HbHtuuK9ERQiffYfXq93PPPffYdl2lct26deuoqa7ipdPOz+s+O+Jhd2cRd3/yN7O2tw0a3DNy7bXXctNNNxNs32fbAQxFrbsQK8rXvvZVHddWahyfz8f6O+5kX7efsw7P6361NYCIZP1MLk2IGfriF7+AV8SWA4c9I70UnT3Ehg0bWLx4sQ3VKZVf1q9fj4jwWlvAsTYsA6+1h/jgdR+krq7OsXbsoME9Q/X19dx33734u5qQ0f5ZXauobTeBQIDPfe5z9hSnVJ6ZO3cuq1evZmdX0LE2jg346B2FW2651bE27KLBPQv33nsvXq+XovYDM76GRIbxdx/jjvXrXd/jV6lstvZDH6JlwEN32JnY2t3pxyPCtdde68j17aTBPQvV1dXcfPPNBLqOQiwyo2v4Ow4iGL0hqdQUUts+7O1yZnbJ3p4Al73vMioqKhy5vp00uGfp7rvvxsSj+Hpbpv9iYwj0NLPm2mt1q1alprBkyRJCxUFODHptv7ZloGXQyxVXXGn7tZ2gwT1Lq1atoqa2Fn/3sWm/1jPUCaMD3HzTTQ5UplR+ERGWLl3KqUH7e9ztIx6icXJm/YQG9yyJCDffdBO+gdMQj07rtb6e43i9Xj784Q87VJ1S+WXJ0mWcHrY/uFuHEr14De4C8sEPfhAsC+9g+7Re5x88w/ve9z7Kysocqkyp/FJTU8NAxNi+6VRfJBGFuXIkoAa3DS6//HI8Xi/e/jPpvygewTPUydVXX+1cYUrlmdT+PcMxe899HIomrpcrnSgNbhsUFxezcsUKfEMdab/GO9QJxrB69WoHK1MqvzgV3CNxDz6vl0DAuQU+dtLgtsmKFSvwjfSkvVe3Z7h77HVKqelx6pz1bDjBPR0a3DZZvnw5JhZGIkNpPd873E3FnEpddKPUNMTjcQC8NuerVwyxeByTqb1jZ0mD2yaLFi0CwDPal9bzveE+li1d4lxBSuWhaDQxc8vrsTdgU78IUr8Ysp0Gt03mz58PgCec3r4l3sjg2GuUUukZHEzsxhny2Rvcqeulrp/tNLhtUlNTg8/vxxMemPrJ8SgmMqKrJZWapoGBAQI+wW9zcpX6LQD6+2e3YVymaHDbxOPxUFNTm9YYtyeS+K2e7VtHKpVt+vr6KPXbPw6dumZfX3pDnW7T4LZRXd1cPNHhKZ8nkcRzamtrnS5JqbzS29tLud/+cejyImvs+rlAg9tGtTU1+NIK7kSvvKamxumSlMorPd1dlDkR3MketwZ3AaqursZEhqecy+2Jjow9XymVvp6eHsqT49F2SvW4e3p6bL+2EzS4bVRdXQ1WHOIX35tbosMUh0I5s0pLqWwxNDREiQNj3D4PBLw6q6QgpXrQU41zS3SEqirtbSs1HbFYjOGRUdunAqaE/KLBXYiqqqqARDBfjCc6zNxaHd9WajpGRhI/V8VeZ4K72GcxPDz1PapsoMFto1SPOzVrZDK++KiObys1TZaVGIf2OLSdiGdcG9lOg9tGqVkiFx0qMQYTHtKpgEplm9zYXwrQ4LZVKBSiOBS6aI9bYqNgxXUqoFLT5PEk4sruQxRSLKO7Axas2tpaJDL5DQ5dfKPUzJSUlODxeBiMOhOug1EP5eXljlzbbhrcNmuor8cXnXzZuy53V2pmPB4P5WWl9Eftjy3LwEDY5Mw2yxrcNmtoaBgL54lIOPG9+vr6TJWkVN6orq6mO2x/bPVGPBjOzQzLdhrcNquvr8dEwxALT/h9T3iAYLA4Z/4kUyqbLF12CaeHi2y/7qlBPeW9oJ3bl3vi7V094X7mz5+fMzdBlMomy5Yto2vk3OG+djmZDO5ly5bZel2naHDbbCy4JzkJxxcZYOHCBZksSam8kTqjtanfZ+t1m/t9zK2tyZm/hDW4bZbqTU8Y3FYcRvvHjjlTSk3PFVdcgd/nZX+337Zrxi3Y3xtgzbUftO2aTnMluEXkEyJyWEQaReTrbtTglEAgwNy6urHgtkJVWKHEDQ9PuB+M0eBWaoaKi4u54sor2ddj3wZtxwZ8DEdhzZo1tl3TaRkPbhHxAv8H+A1gFfApEVmV6TqctHTJEnzJ4A4vWkt40VoAPCOJxxYuXOhabUrluuuuW8vJQQ/tw/bE1/azRXg8Hq655hpbrpcJbvS4Pwg0GmOajTER4MfAXS7U4ZglS5Ygo31gzt/3wDPSg4iwePFilypTKvfddNNNAGxtv3ive1FpjEWlsYs+xzKw9Wwx165ZQ0VFhW01Os2N4J4PnBz39ankY+cRkS+IyDYR2Xb27NmMFWeHJUuWgBXHM3r+waOekR7m1tURDAbdKUypPFBXV8fq1VeypaP4omeWPLhymAdXXnzDt6N9PrpG4JZbb7W5Sme5EdwTzeN5z9tvjPmOMWaNMWZNri0PT80F9Yycf5qGP9zHJTky3UipbHbbbR+ndUhonOXsktdaAwQDAW644QabKssMN4L7FDB+kHcB0OpCHY5ZvHhxYmbJyLjz66w4jPTmzDxRpbLZzTffTKg4yMunZ36TcigqbD0b5NbbbiMUCtlYnfPcCO53gRUislREioAHgGddqMMxwWCQ+oZ55/W4PaN9YEzOrMxSKpuFQiFuve3jvNMRZGCGi3FePxMgGocNGzbYXJ3zMh7cxpgY8PvAr4CDwJPGmP2ZrsNplyxbij98rsedCnENbqXscddddxG14NXW6fe6LQMvnQ6x6n2XjS3qySWuzOM2xvzcGLPSGHOJMeav3KjBaUuWLIGRvsQQCeAZ6cXj8bBgga6aVMoOy5Yt4+qrruKl1hLi0zy4ZneXn/Zh4Z5773OmOIfpykmHLFmyBIwZW4jjGemlYd48iors3yBHqUJ1z7330jUC2zun93O1+VQxNdVVfOxjH3OoMmdpcDsktToyFdz+SD9LlyxxsSKl8s+HPvQhGurmsvlUcdqvOT3kZV+3n7vu/iQ+n717nmSKBrdDUkMintRCnJE+XTGplM28Xi93/+Y9HO71cWLAm9ZrNp8K4vf7uPPOOx2uzjka3A4JhUJUVlXjGe1PHJ5gLA1upRxw++23EwgUsfnU1AvbhmPCm+3FrFt3S86cdjMRDW4HLVwwH09kYGxv7nnz5rlckVL5p6ysjNtu+zhbO4JT7tP91pkiwjHD3XffnaHqnKHB7aB58+bhiwxqcCvlsDvuuINIHLa0T36T0hh4pTXEiuXLueyyyzJYnf00uB1UV1eHCQ/hGe3H4/VSXV3tdklK5aVLL72UFcuX80rb5Csgmwd8nBz0cGcOLri5kAa3g+bOnQuAZ+gsVVVVeL3p3TxRSk3f+jvu4OSAZ+wYsgu9eaaIIr+fdevWZbgy+2lwOygV3N6hjrHPlVLOuPHGG/F4PGyZYLvXuAXvnC3mQ9dfT0lJiQvV2UuD20FVVYmTb8QYanSYRClHzZkzh2uu+QBvdwTfs93rwV4//WHyorcNGtyOGj/dqLKy0sVKlCoMN954E2dHhFND5w+X7Oj0EwgUcd1117lUmb00uB00/kSNXJ4zqlSuSAXz7q5zhwkbA3u6g1xzzTUEAvadVekmDW4HjV9OW1pa6mIlShWGmpoali+/hN1d5wL6zLCHjmHhuuvWuliZvTS4M6SsrMztEpQqCNdcs4amfh+RxMacHOr1Jx/PncOAp6LBnSH5cCdbqVxwxRVXELPg+EDiL94jfT7mVJQzf/57jrbNWRrcGVJcnP7uZUqpmbviiiuAxEHAAI39Aa64cjUiMzspJxtpcDssdeJNeXm5y5UoVRgqKyuZW1tDy6CPkRi0DwsrV650uyxb5eZmtDnkW9/6Fk1NTTl5PJJSuWrpsks4daCD00OJiMu3Q7q1x+2whoYGPvzhD+fVn2lKZbtly5bROuShJbn8Pd/OetXgVkrlnQULFhC3EjNKvF4P9fX1bpdkKw1upVTeSQX14R4/c2tr8m6DNw1upVTeSQV3T8RDfUP+TANM0ZuTSqm8M2/ePB599FG6u7u54YYb3C7HdhrcSqm8IyLcd999bpfhGB0qUUqpHKPBrZRSOUaDWymlcowGt1JK5RgNbqWUyjEa3EoplWM0uJVSKsdocCulVI7R4FZKqRyjwa2UUjlGg1sppXKMBrdSSuUYMca4XcOUROQscMLtOnJYDdDpdhGqIOm/vdnpNMZ84sIHcyK41eyIyDZjzBq361CFR//tOUOHSpRSKsdocCulVI7R4C4M33G7AFWw9N+eA3SMWymlcoz2uJVSKsdocCulVI7R4M5jIvIJETksIo0i8nW361GFQ0T+Q0Q6RGSf27XkIw3uPCUiXuD/AL8BrAI+JSKr3K1KFZDvAe9ZOKLsocGdvz4INBpjmo0xEeDHwF0u16QKhDHmNaDb7TrylQZ3/poPnBz39ankY0qpHKfBnb9kgsd07qdSeUCDO3+dAhaO+3oB0OpSLUopG2lw5693gRUislREioAHgGddrkkpZQMN7jxljIkBvw/8CjgIPGmM2e9uVapQiMiPgC3ApSJySkQecbumfKJL3pVSKsdoj1sppXKMBrdSSuUYDW6llMoxGtxKKZVjNLiVUirHaHArpVSO0eBWWUtE/lRE9ovIHhHZJSLX2XDNDXZtcSsig3ZcJ3mtu3X3RpUun9sFKDUREfkQcAfwAWNMWERqgKI0X+tLLkB6D2PMs2TnCtK7gU3AAbcLUdlPe9wqWzUAncaYMIAxptMY0yoix5MhjoisEZFfJz//CxH5joi8ADwhIm+LyOWpi4nIr0XkGhH5nIj8k4hUJK/lSX4/JCInRcQvIpeIyC9FZLuIvC4ilyWfs1REtojIuyLyl1P9B4jIH4vIXhHZLSJ/nXzs88nX7xaRjcl2rwc2AH+b/MvikslqUAo0uFX2egFYKCJHROSfReRjabzmGuAuY8ynSew//lsAItIAzDPGbE890RjTB+wGUte9E/iVMSZK4mTyLxljrgH+EPjn5HP+EfgXY8y1wJmLFSIiv0GiF32dMeb9wLeT33raGHNt8rGDwCPGmLdI/BXwR8aYq4wxTRepQSkdKlHZyRgzKCLXAB8BbgJ+ksbY9LPGmJHk508Cm4E/JxHgP53g+T8B7gdeIbEJ1z+LSClwPfBTkbGdcQPJjzcA9yQ//0/gby5Syy3Ad40xw8n/ntShAleIyDeBOUApib1kzjNFDUppcKvsZYyJA78Gfi0ie4GHgRjn/lIMXvCSoXGvPS0iXSKymkQ4f3GCJp4F/peIVJHorb8MlAC9xpirJisrzfJlkud+D7jbGLNbRD4H3DjBczxT1KAKnA6VqKwkIpeKyIpxD10FnACOkwhZONf7ncyPgT8GKowxey/8pjFmEHiHxBDIJmNM3BjTDxwTkfuSdYiIvD/5kjdJ9MwBPjNF2y8A/5eIhJLXqUo+Xga0iYj/gmsMJL/HFDUopcGtslYp8LiIHBCRPSQOPP4L4H8C/ygirwPxKa7xFImgffIiz/kJ8GDyY8pngEdEZDewn3NndX4ZeFRE3gUqLtawMeaXJHr020RkF4lxaoA/A94mMYxzaNxLfgz8kYjsFJFLLlKDUrqtq1JK5RrtcSulVI7Rm5NKzYKIXElihsl4YWPMrFd5KjUZHSpRSqkco0MlSimVYzS4lVIqx2hwK6VUjtHgVkqpHPP/A15dGjXOrpI2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot( x='Survived_cate', y='Age', hue='Survived_cate', data=df_train, kind='violin' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Survived_cate</td>\n",
       "      <td>897.187582</td>\n",
       "      <td>1</td>\n",
       "      <td>897.187582</td>\n",
       "      <td>4.271195</td>\n",
       "      <td>0.039125</td>\n",
       "      <td>0.005963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Within</td>\n",
       "      <td>149559.448362</td>\n",
       "      <td>712</td>\n",
       "      <td>210.055405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Source             SS   DF          MS         F     p-unc       np2\n",
       "0  Survived_cate     897.187582    1  897.187582  4.271195  0.039125  0.005963\n",
       "1         Within  149559.448362  712  210.055405       NaN       NaN       NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_train[['Age','Survived_cate']]\n",
    "aov = pg.anova( dv='Age', between='Survived_cate', data=data, detailed=True )\n",
    "aov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "組間變異數 =  897.1875816713222\n",
      "組內變異數 =  149559.44836244633\n",
      "總變異數 =  150456.63594411765\n"
     ]
    }
   ],
   "source": [
    "print( '組間變異數 = ', aov.SS[0] )\n",
    "print( '組內變異數 = ', aov.SS[1] )\n",
    "print( '總變異數 = ', aov.SS[0]+aov.SS[1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0059630974469252"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算 𝜼 ^𝟐\n",
    "\n",
    "etaSq = aov.SS[0] / (aov.SS[0] + aov.SS[1])\n",
    "etaSq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Negligible'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def judgment_etaSq(etaSq):\n",
    "    if etaSq < .01:\n",
    "        qual = 'Negligible'\n",
    "    elif etaSq < .06:\n",
    "        qual = 'Small'\n",
    "    elif etaSq < .14:\n",
    "        qual = 'Medium'\n",
    "    else:\n",
    "        qual = 'Large'\n",
    "    return(qual)\n",
    "\n",
    "judgment_etaSq(etaSq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ans 1: 藉由  $\\eta^2$ 推得 'Age' 與 'Survived' 無相關性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8dlu8RnNE8GN"
   },
   "source": [
    "### Q2: 透過數值法計算  Sex 和 Survived  是否有相關性?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "g= sns.countplot( x='Sex', hue='Survived_cate', data=df_train )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method: Cramer’s V Coefficient  \n",
    "\n",
    "$$\\displaystyle V=\\sqrt{\\frac{\\chi^2}{n\\times \\min(r,c)-1}}$$  \n",
    "where $r$ is the number of rows in contingency table, $c$ is the number of columns in contingency table, and $n$ is the grand total of observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Survived_cate</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived_cate    0    1\n",
       "Sex                    \n",
       "female          81  233\n",
       "male           468  109"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: 列聯表(Contingency table)\n",
    "\n",
    "contTable = pd.crosstab( df_train['Sex'], df_train['Survived_cate'] )\n",
    "contTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: 自由度 df \n",
    "\n",
    "df = min(contTable.shape[0], contTable.shape[1]) - 1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cramer's value is 0.5434\n"
     ]
    }
   ],
   "source": [
    "# Step 3: 運用 researchpy 套件計算 Cramer's V 係數\n",
    "\n",
    "# 這邊用卡方檢定獨立性，所以採用的 test 參數為卡方 \"test =\" argument。\n",
    "# 採用的變數在這個模組中，會自己根據資料集來判斷 : Cramer's Phi if it a 2x2 table, or Cramer's V is larger than 2x2.\n",
    "\n",
    "crosstab, res = researchpy.crosstab(df_train['Survived_cate'], df_train['Sex'], test='chi-square')\n",
    "print( \"Cramer's value is\", res.loc[2,'results'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'large'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def judgment_CramerV(df,V):\n",
    "    if df == 1:\n",
    "        if V < 0.10:\n",
    "            qual = 'negligible'\n",
    "        elif V < 0.30:\n",
    "            qual = 'small'\n",
    "        elif V < 0.50:\n",
    "            qual = 'medium'\n",
    "        else:\n",
    "            qual = 'large'\n",
    "    elif df == 2:\n",
    "        if V < 0.07:\n",
    "            qual = 'negligible'\n",
    "        elif V < 0.21:\n",
    "            qual = 'small'\n",
    "        elif V < 0.35:\n",
    "            qual = 'medium'\n",
    "        else:\n",
    "            qual = 'large'\n",
    "    elif df == 3:\n",
    "        if V < 0.06:\n",
    "            qual = 'negligible'\n",
    "        elif V < 0.17:\n",
    "            qual = 'small'\n",
    "        elif V < 0.29:\n",
    "            qual = 'medium'\n",
    "        else:\n",
    "            qual = 'large'\n",
    "    elif df == 4:\n",
    "        if V < 0.05:\n",
    "            qual = 'negligible'\n",
    "        elif V < 0.15:\n",
    "            qual = 'small'\n",
    "        elif V < 0.25:\n",
    "            qual = 'medium'\n",
    "        else:\n",
    "            qual = 'large'\n",
    "    else:\n",
    "        if V < 0.05:\n",
    "            qual = 'negligible'\n",
    "        elif V < 0.13:\n",
    "            qual = 'small'\n",
    "        elif V < 0.22:\n",
    "            qual = 'medium'\n",
    "        else:\n",
    "            qual = 'large'\n",
    "    return(qual)\n",
    "\n",
    "judgment_CramerV( df,res.loc[2,'results'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ans 2: 藉由 Cramer's V 推得 'Sex' 與 'Survived' 存有高度的相關性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SxLGI6sE8GN"
   },
   "source": [
    "### Q3: 透過數值法計算  Age 和  Fare 是否有相關性?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "g = sns.regplot( x='Age', y='Fare', data=df_train )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method: Pearson Correlation Coefficient "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0960666917690389\n"
     ]
    }
   ],
   "source": [
    "# Note: feature must not contain infs or NaNs!\n",
    "\n",
    "data = df_train[['Age','Fare']].dropna()\n",
    "corr, _ = stats.pearsonr( data['Age'], data['Fare'] )\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ans 3: 藉由 Pearson Correlation Coefficient 推得 'Age' 與 'Fare' 無線性相關性。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "作業.ipynb",
   "provenance": []
  },
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}