2_statistics_lecture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 773
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1568,
     "status": "ok",
     "timestamp": 1565657295344,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "j9iXH4wTLh3o",
    "outputId": "e3d82770-beee-4e78-9aee-fd528796d3f6"
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 numpy as np\n",
    "################################\n",
    "# ヒストグラムと正規分布\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data_dir = './data/'\n",
    "\n",
    "# csvを読み込み\n",
    "HW_data=pd.read_csv(data_dir+\"/height_weight.csv\")\n",
    "# ヒストグラムをプロット\n",
    "HW_data.plot(kind=\"hist\",y='Ht') # (HW_data.plot(kind=\"hist\",y=2)) でもOK.\n",
    "plt.show()\n",
    "\n",
    "# bin数を30に設定\n",
    "HW_data.plot(kind=\"hist\",y='Ht',bins=30)\n",
    "plt.show()\n",
    "\n",
    "# bin数を50に設定\n",
    "HW_data.plot(kind=\"hist\",y='Ht',bins=50)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2164,
     "status": "ok",
     "timestamp": 1565657295955,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "NB7RAUS0T88B",
    "outputId": "0f33cb82-9df8-4a62-8ef6-b8f73e0fdcc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180.1039603960396\n",
      "94.2912714439761\n",
      "9.710369274336383\n"
     ]
    },
    {
     "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": [
    "# 正規分布で表現\n",
    "# 正規分布のパラメータ（平均、分散）を求める\n",
    "Ht_mu = np.mean(HW_data['Ht']) # 平均\n",
    "Ht_var = np.var(HW_data['Ht']) # 分散：標準偏差の二乗\n",
    "Ht_std = np.std(HW_data['Ht']) # 標準偏差\n",
    "print(Ht_mu)\n",
    "print(Ht_var)\n",
    "print(Ht_std)\n",
    "\n",
    "# 正規分布とヒストグラムを可視化\n",
    "from scipy.stats import norm\n",
    "HW_data.plot(kind=\"hist\",y='Ht', density=True) # ヒストグラムをプロット\n",
    "a = np.min(HW_data['Ht'])-5 # xの下限\n",
    "b = np.max(HW_data['Ht'])+5 # xの上限\n",
    "x = np.arange(a,b,0.01) # xを0.01刻みで生成\n",
    "y = norm.pdf(x,loc=Ht_mu,scale=Ht_std) # 正規分布の確率を求める\n",
    "plt.plot(x,y,lw=3,color=\"r\") # 正規分布を線でプロット\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2400,
     "status": "ok",
     "timestamp": 1565657296203,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "iQ8SadIxVkig",
    "outputId": "18a88918-7c8f-45bd-d31a-2ca946380eb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000906628095277654\n",
      "0.1179317704803018\n"
     ]
    },
    {
     "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": [
    "################################\n",
    "# 標本平均の分布\n",
    "\n",
    "# ランダムサンプリングと標本平均\n",
    "import random\n",
    "N = 100 # 1回の標本中のサンプリング数\n",
    "K = 100 # 標本回数\n",
    "mu = 0 # 平均\n",
    "sigma = 1 # 標準偏差（分散の平方根）\n",
    "meanList = [] # 標本平均を保存するリストを初期化\n",
    "for j in range(K): # 標本取得をK回繰り返す\n",
    "    samples = [] # サンプルのリスト\n",
    "    for i in range(N): # N個の標本をサンプリング\n",
    "        a = random.gauss(mu,sigma)# 正規分布に従ったランダムサンプリング\n",
    "        samples.append(a)\n",
    "    sample_mean = np.mean(samples) # 標本平均の計算\n",
    "    meanList.append(sample_mean) # 標本平均をリストに保存\n",
    "    \n",
    "# ヒストグラムで標本平均の分布を正規化して表示\n",
    "plt.hist(meanList,range=[-0.5,0.5], bins=50, density=True)\n",
    "mu = np.mean(meanList) # 平均\n",
    "std = np.std(meanList) # 標準偏差\n",
    "print(mu)\n",
    "print(std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2393,
     "status": "ok",
     "timestamp": 1565657296206,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "-IPZ6rayVsCm",
    "outputId": "381a7716-a65b-42bd-cb84-82d8f2d4039a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.5\n",
      "17.783419243778738\n",
      "信頼区間の下限: 55.09037025988665\n",
      "信頼区間の上限: 81.90962974011336\n"
     ]
    }
   ],
   "source": [
    "################################\n",
    "# 母平均の推定\n",
    "from scipy import stats\n",
    "# data読み込み\n",
    "Grade_data=pd.read_csv(data_dir+\"grade_data1.csv\",sep=\",\")\n",
    "# 平均と標準偏差を求める\n",
    "mu = np.mean(Grade_data['English'])\n",
    "sigma = np.std(Grade_data['English'])\n",
    "print(mu)\n",
    "print(sigma)\n",
    "# 母平均の95%信頼区間の推定\n",
    "alpha = 0.95 # 信頼係数95%\n",
    "n = len(Grade_data['English']) # sample数\n",
    "t = stats.t.ppf(1-(1-alpha)/2, n-1) # t分布を用いて確率変数tを計算\n",
    "t_min = mu - t * sigma / np.sqrt(n-1) # 下限\n",
    "t_max = mu + t * sigma / np.sqrt(n-1) # 上限\n",
    "print(u\"信頼区間の下限:\",t_min)\n",
    "print(u\"信頼区間の上限:\",t_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2965,
     "status": "ok",
     "timestamp": 1565657296785,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "dXdhSMc9WASm",
    "outputId": "1304f38b-d3a0-42c9-a5fb-6bfe71c7dcde"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T+znV4eTBT6zhrs3z07JJVphn4aefXBuUGA65vYA+7XcsPrx2Nk/cuQHXqI8PPPCm7oEJobx5qotv/aGBqxdX8uWrFyZ9vGhsnFuK3WrmpYbxf8vPv9NGeYGVdUlIFfWQPQE80E6fCZayR1oHuOn+7ew/28d/fWQl/3DdRcytsNPU5dJfo1ftYiOVUEBqwTO8Br6jsZvbH9nN3HI7j35mA8W2MaXCVYsqeauxe8Kt52QyMOLh4e1NvHtpFRfNiH57fcWCCmoc+Ty2I3PKKGd6hrju4plcm6bZiir15XZ+9LHVHG8f5O9/dQBFUTh4rp8THRN9v5NhTW0pv/vipdSV27j9kT38+NVTut87Z3uGuOuxfdSX27n3lpVpLWHk5pi5clEFrxxpD3rnjHh8bD3WwbVLZyRVXtJD3AAuhMgTQuwSQhwQQhwWQnwz8Hi9EGKnEOKEEOIpIUTqdgsikCmWsi8cauVDP34Tj8/P05/dxE0rpd1rXZmdgREvfXrb/aNpwFVULXiGsqe5h0//fDc1DhuP3r4h+P+lctWiCkY8fnY0Tt1kpUfebGZwxMvdW2I36phMgo9tmMNbjd2c7MiMgQQDIx6K8ien7+7KhRX843sW86d32rh/60nd2u94VJfk86vPXsL1y2by3ReO8qWn9mu6wJ/tGeJ7Lxzl/fdvx+dX+Okn11KYp03eqIerF1fRMTgaLJm+dryTIbcvraPTopFIBj4KbFEUZQWwErhOCLER+C5wr6IoC4Be4DPpW+ZYDXyqmnn8foX/evkEn3t0HwurCvn9Fy9jxeyx3ea6Mmkb2dSts4wSrQtTpWgWDGRmDXz/2T7++uHdzCjK4/HbN1AeodNu49wycnNMvDpFGmvXqJeH3mhi86IKLp5VHPf1H14zG4tZ8PjOqc/C/X4F56iXokkITiq3X17PB1bN4gcvHudXe8/q0n4nQr7VzH0fXcU9717Ec/vP81c/eYu2/uhNRT6/wl+OtvPpn+/miu9v5X9eO8WqOQ4eu31j2q1bVTYvqsQkCDb1vHCojeJ8Cxvnlk3K+UOJG8AViZqGWAL/FGAL8Ezg8V8A70/LCgMU5VswianJwIfcXr74xD7uffk4H1w1iyfv3DjBlKcu8Mejuw6eSAY+2g+jmTUo9lBLP594aCeldiuP3zHx96KSZzFzybwyXj02NXXwx3aepnfIw93vSqxNvqIwl3cvncEze89OadkHYHDUi6LI98BkIYTgOx9cxvKaYkY8ft3a70TPddfm+Tz4iTWc6nDyvvve4O0z45vWupyj3L/1JFd8byuf/vke3mnp54ub5/P6P2zhZ7etZVlN/ItyqnDYraytK+WlhnZGvT5ePtLONUuqUqY310JC92RCCDOwF5gP3A+cAvoURfEGXnIOmBXle+8E7gSYM0d/O6vZJCixWSe9Bn6ud4g7HtnLsbYBvnb9Ym6/vD7iBtLs0nxMApr1SuXUDNwexQ0vKCVshYroXgyTyZHWAT7+0E6K8iw8fseGuCZQVy2qZOuxwzR1uSYtWwJZo3xwWxOXzS/X5AN964Za/nCwlT8cbE1rAIvHwLAsyxXlTa51UZ7FzEO3rWPb8U4u06n91sK1S2fwmy9cyu2P7OaWB3fwHx9cRo3Dxi93nOaFQ614fAqb5pbxT9cv5tqlUxMwVa5ZXMW//ekIT+85x+CIl+snwTo2Egn9RSiK4gNWCiFKgN8CkWYRRdyBUBTlQeBBgLVr1ybVhVNis0yqCmVgxMP773+TUY+Ph/56HZsXRSlvIDc3qkvyk8vA80shR5aKHnj1JK8f7+KWdbN5z7IZ5IZqwSvSt8ueKK5RL594aCf5FjNP3LExocGtVy2SF6dXj3VQX16f7iUGeWLXGbqco9y9ZZWm79s4t5S5FXYe23l6agN4oOllMjNwlYrC3KSlg1pYNKOQ3911GV94bB9/9/QBAArzcvj4xlpu3VAbUfo5FVy9RAbw7z1/lMLcHN3NTcmi6ZKuKEqfEOJVYCNQIoTICWThNUDad9hKbdZJ1YEfaumnyznKzz65NmbwVqkvt9OsuwY+XgP+3NvnOd4xyFuN3XzrD1buuBg+DxmjBW/udtHldPOjj64a180Yi9oyO3PL7bx6rJNPXTo5AXzU6+MnrzWyvr6UDRprlEIIbt1Qy7/+oYHD5/tZWj15t+mhDAzLG93JrIFPJQ67lUc+s55Hd5zGZjXz3hXV2KyZZZxaX25nXoWdU50ublpZTW5O6jT5WkhEhVIRyLwRQuQDVwNHgK3AzYGX3QY8l65Fqky2I2FjpwzGiXZ01ZUlISUM6cL0+vw0djm58/K5/PIz61lX5+C/d8u1PLN1Jy81tOOb4vFf6p1QZaE2a9ArF1Wwo7GbYbf+uvLD25u49Wc7+M7zR/jjwVbO9gxF/Z3/as852gZG+Js4ypNofGj1LHJzTFO6mTmWgWdWEEsnFrOJT11azy3r5mRc8FZRh2FMhfpEJZHfzEzgF4E6uAl4WlGUPwghGoAnhRDfBt4GHkrjOgGZgR8815fu0wRp7HSRbzEzI8EpIrVlNgZHvPQOebR7MDjboWY9AKd7hvD4FBZUFXL5ggouX1BBW//FDP+oGDF4njse2UN1cR4fXT+HW9bPprJw8gcQdLukI1tZgbafc/OiSh7e3syOxm42XxT/riac/mEP3//zMfIsZnY19QSHF5TarSybVcyKmmKW1ZSwoqYYh93Kj189xcrZJVw6X59CoMRm5cbl1Tz7dgv/eP3iuD7b6WCsBn5hZODZwic21jLq8XNVAnfn6SLuX6OiKAeBCcVDRVEagfXpWFQ0HHYrvS4PiqJMil1jY5eT+nJ7wo0B6sZcU5dLWwBXlHEZuKo9Dh3JNKM4D8rm8IEigX3Fah7dcYb/fOk4//XKCd69dAa3bpzDprllk2ZjqZaySu3aMvD19aXkW8y8eqxDVwB/avcZhtw+fvW5TcyvLOBo6yAHW/o5eLaPd1r6uW9rJ+rNSXG+hf5hD//6/qVJ/V5u3TiHX+87x3P7W7h1Q63u4+hlYCRQQpmCGrhBdGocNv7lfUundA2ZeW8SBYfNgtvnx+X2TUom1NjpYrkGeVKolHBNbeJqB9xO8AwFa+BqAJ8XvmFTVI1psJXrLp7JdRfPpLHTyeM7z/Crvef44zutzKuwc+uGWj60piYtmt1QelxuTAJKNJ5HlRNuPdbJv2i8EHt9fn6+vZlNc8uC9egVs0ukHn+jDKxDbi8N5wc4eK6fg+f6yLfmJLR/EYtVs0tYPLOIR3ec4WPr50zaRVJlYNiDEFA4Bdm/QWaTNa30MLndmKNeH+d6h5irwfthtsOGScBprRuZwSaesQBeXZw38SJVNL6dfm5FAV+/cQk7/+ld/ODDKyjMs/CtPzSw4d9f5qvPHEhruanb5cZhs+pqW75qUQVneoZo0qjYef5QG+f7R/jMZdE3QG3WHNbWlfLpy+r5fx9ZxXc+uCzpgCs3M+dwpHWA/Wcnr4SnMjDioSA3Z1Jd7gyyg6wK4JPpSHi6ewi/AvMqEtcrW3NMzHLk06RVCx42jf5ExyDzqyJovYtmwVAXeMZ3quVZzNy8poZn77qUP9x9GR9YNYvfH2jlffdt5333vcHTu88mtWkYiV6Xe0K7fKKoNUMtk28UReFnbzRRX25ni47SS7K8f9Us7FYzj03BZubA8OR2YRpkD1kVwIMZ+CQoURo7ZRljbrk23WldmV27FjykC9PvVzjZ4WR+pMxf1YLHkBJePKuY73xwOTu/9i6++b6lDLt9fPXXB9nw7y/zH88fTdlAjG6XW7dZ/uxSG/Mq7Jq6Mved6eXA2T4+fWndlGSiBbk53LRqFr8/cH7Su4GlD4oRwA0mklUBvHQSA/ipgISwXkMGDgEtuFYpYUgJpaVvmBGPnwVV+gJ48KV5Fm67pI4Xv3wFT925keU1JfzPa6doG0hseG08elxuypKYdnLVokp2NvYELUvj8bPXmyjOt0xqU0k4t22qw+tX+Nc/NEzqeQeGPZPehWmQHWRXAA+WUNLfjdnY6aKqKFfzZmltmZ3BUa+2Mo+zHUw5kO+IqEAJUqh9tJoQgg1zy/jkJrnJ1+1MzcWvJ4kMHKSc0O3zR5z+Es7ZniH+fLiNj22YWk3wohmF3LV5Pr95u4U/Hpy8hqqBEa+RgRtEJKsCeGFezqQZWjV2OTWXTwDqy2VXoqaOTGc72CvBZAoG8Igtw0mMVisLOAR2RpmorQWfX6F3KLkMfF29A5vVnJA74cPbmzEJwW2b6nSfL1XcvWU+K2qK+dqz79CeoruZeMgM3AjgBhPJqgBuMgkck2BopSgKjZ0u5mosn0CIrWyXho3MEA34iY5BygtyKbFFCI55RWAt1OULXhEI4KnIwPuHPSgKujcxQXrHSDlhR8xy0+CIh6f3nOXG5TPjmmVNBhaziR/espIRj497njk4KUO2J9ML3CC7yKoADjJopHoYajg9Ljf9wx5NEkKVGocNs0lokxKG+KCc6HAyvzLGhUPnYAe1Y7I7BRl4T6ALM9mJ31ctquRc73BwvyEST+0+i3PUy2cum5vUuVLJvIoCvnb9YrYd74w5xT0VTIUXuEH2kHUBfDIMrdSAoicDt+aYmFWSr03jHMjAFUUqUBZUxrCLLdI3Ws1mNZNnMdGVggCuZvFlGrswwwl1J4yE1+fn4e3NrK8vnVS/50T4+MZarlxYwb//6Uhap/YMjky+F7hB9pB1AdxhT7+lrCohnKejBg6yIzPhGrjfHwjgckzT4Ig3sgJFpWiWrgAuhKC8IDclJZSxNvrkMvAah40FlQVR6+AvNrTT0jccs3FnqhBC8P2bl5NnMfN3T+/H40vPBPugkZWhQjGIQPYF8EmogTd2uYJNOXqoL7PR3BXdIW8cwz2g+KCgihPtgQ3MWKWbompwtoEvMfldKGUFuXSl4O6lO3AMrUZWkbhqUQW7mnpwjU78eX72eiO1ZTauXhxlStEUU1mUx3c+sIyD5/r50Ssnxj/Ze1penJOkf1ijF/hw35gs1WDak30B3G6l1+VO6+ZRY6eTujKb7gnTtWV2nKPeYKCLSUgX5skOOS5tfswMvBoUP7i0v0nL7Va6BpMvoagqoBJb8rf1qpzwzTA54b4zvew708enLqmb9EnfWnjPspl8aHUN9209yT51DFj/OfjRanjnV0kffywDT+B37ffBLz8AP3uXrgu8QfaRdQG81GbF61cYjJCxpYrGTpcuCaFKvZb5mCFdmCc6nBTl5QQVIxHRoQVXKSuwBm1gk6Hb5aYwNyclJvZr60qxW81sDauDP/RGE4V5OXx47eykz5Fu/u/7ljCzOJ+/e2q/vJM4uxP8Xmg9kPSxg8McElGh7H0Yzu+DvjNw5HdJn9sg88m6AK5K1/rSVAf3+Pyc6RnStYGpUhdiKxuXkGn0JzucLKgqjG2+lIQWXK2BJ3v30uNyU5qC8gnITd9L5pfz2rHO4LrO9Q7xwqE2PrZ+DvYscOAryrPww79awemeIb79xyPQsk8+0XUs6WMnnIE7O+Dlb0Hd5VA6D966P+lzG2Q+WRfAS+3yDzlddfAzPUN4/YouCaFKjSM/ICVMQAsekoFLBUqc8xYFZkfrysBz8fqVYFanl2S7MMPZvKiSlr7hoJrjF282A3DbJXUpO0e62TC3jDuvmMsTu87Qd+JN+WDX8aSPO5BoDfylb0hL4ht+CBs/Dy174OyupM9vkNlkXQBXG1zS1Y3ZmISEUMViNlHjyKcpESWKswMsdnq8Vrpd7vhDW22lYM7VFcDLA1lzst2Y3Un6oISjygm3HuvAOerlyV1nuX7ZTKpL9G0iTxV/d81Cllblk9d1CMVkgb6z4NboTBnGwIg3vhd48xtw4Am49G/kwOsVH4W8YnjrvqTObZD5ZF0AT7elbLISQpWEXQmd7cHyCURpoQ9FCN1a8PJgN2ZyAbw34AWeKqpL8llUVcirxzp5evdZBke9GSkdjEdujpn7rsknDzf789YDCvScSuqYA8NxvMB9HvjjV6B4Dlz+94GFFMCaT8GR30s1jMG0JesCeLotZRs7XZTZrRQnqbBI2JUw0IV5QlWgxAvgoFsLHuzGTOLipyhKSmvgKlctqmB3cw8PvcwPKx8AACAASURBVNHE2loHK2eXpPT4k0X9yFEAftwXmDbYmVwdfGAkjg/Kjgeg8yhc/z2w2sYeX38nCBPs/ElS5zfIbLIugBfl5WA2ifQF8C5nUuUTldoyGy63j654jTOBLsyTHU5sVjPVxQmUDYqqYVBHAA90TibTjekc9eL2+VNaQgE5rd7jU2jpG+b2y7Mv+w7Ssgd/fimv+VegIKDrRPzvicHAcAwnwv5z8Op/wKLrYdF7xj9XPAuWfgD2PQIjA0mtwSBzyboALkTA0CpNKhRdEkK/D37xXmgYk24F52PGq4MHMvCTHU7mVxYkNqxA9UPRqCZx2CwIQfyLSgz0DjOOx9raUr6X+7/8feGLXLNkRkqPPam07EPMWoPPlEtfbnXSG5kyA49S/37h/8i/gev+I/LzG78A7kF4+5dJrcEgc8m6AA5SiZKOTcz+IQ/dLrf2DLztIDRtgwNPBh+qL0tASugdheHeYBdmzA7MUAqrweeGofhe2qHkmE2U2qxJ1cCDXZgpzsCtJvhQzhvc5jiU0Y07MRkdhI4jiFlrcNittFtnpyADjzKN5/iLssZ95T3gqI38zbNWw5xLYOf/GI0905SsDOAlaWqnP9UVGKOmVULYtE1+PPNmsH26xpFPTjxXQpf0ABnJK6NtYCR2B2YoSfmCW5MqoagXzmSsZCMfuAmzb4TCYe0/U8bQegBQoGYtDpuFs6Ya6D4h79B0MjgSwYnQMwzP3wPlC2HT3bEPsOkLsrHn6B90ryGluF3w6M1wbs9Ur2RakJUBvNRmTUsGrltC2PS6/DjcKzeUkNlujSOf5li+4AENeIunCIjjgRJKUAuufSpMmT05Q6t0ZeB0BMaUDbbKAJWNqEGpejUOm5VTyizwjkD/Wd2HlBl4WAnljXuhtxmu/wHkxPl/WHQ9OOrkZmcm0PA7OPkS7PvFVK9kWpAdAfzN++DxW4JfOuxWeodSXwNv7HSSYxLMKbXFf7GKzwOn34QF18qvT28PPlVXbo9dQgl0YTaNyAvGgkiT6CORTDdmYW5SKpRUORFOoOPI2Oe9zak99mTRslcGS3sZDpuVo96Z8nGdZRRfwDJiXAbefUoG8GUfhrlXxj+IyQwbPi/b+zMh693/mPx44mXNezgGE8mOAD7SDydeBI8cYVVqt9A7lHpDq8ZOF3NKbVjMGn4tLfvA44JVH5eZcWgAL5O2slHXGcjAjzptWHNMzE7U/bCgEoRZn5TQnlwJpcflJjfHhM2avA/KONoPA4Had9YG8H0waw0gk4x3RgMuijqlhM4R1QclEMAVBf7095CTB9d+O/EDrboVcoumvr2+9zQ0vy5LP4PnA//nBsmQHQG8aol04At4SzhsVnx+hYGR1G7M6JIQqvXvusuh9hJo3h7MLOrKbAy5fdE7HwMZ+IFeC3PL7eQkeuEwmaFwhu5uzMERLyMefXXZbqfswozp16KHjiMwZ6P8vKcptceeDAbbYOAczFoLSMXP6eE8FFuZbiXKBC/whmfh1F9gy9fl/3+i5BbCmtug4TnZHTpVHHgSEHBToJxz4sWpW8s0ITsCeOUS+TFwm+1IQzu9z6/Q3D2kfQOzeRvMWCZb3GsvlTav3bL7LigljFYHd7ZDfinHOkcTa+AJRa8WPNCNqbeTtXfInfoNTO8odJ+EustkptibhQG8Za/8GMjAS+3SNdNXukB3CWWcF/joILzwjzBjOaz9jPaDrf+s/Lhrihp7/H5ZPqm/Amavk++Zky9PzVqmEdkRwEvngdkavOVS66+pVKK09A7j9vqZW64hA/eMwJmdUHeF/Lr2UvkxUEaJayvrbMdvr+Rs75C+AJ5UO72+3113io2sAFliUHzyQu2ozc4SSsteWdaauRwY8+wZLpqbggzcAq99V2b5N94LZh0OjSWzYclNsPcX8mIw2Zx5C/pOw8pb5dfzr4EzO2R51EA32RHAzTlQviioVAi206cwA9clITy3C3yjMqsAKF8A9opgAJ9VIqWEUZt5nB0MWctQFGLPwYxEYTX0t2jeCFLb6fXWwXtco2lQoAQ2MCuXgKM+O0soLXuhailY5D6G6po5UFAPQ10w1KP5kEEv8DyzLD8suQlq1upf46a7YHQA3n5M/zH0sv9xsBbC4hvl1wuulRftU1snfy3TiOwI4CDr4IE3umpolUolii4JYdPrMuuqvUR+LYT8/LS0E80xm5hdaosRwNvpNTkAYs/BjERRtdw8HdXWJl2eZDt9j9Od8i5MOg7LO6yyeVBaLzO1JLTTk47fDy1vjwuuagbelVcnH9CRhasZuMPTLnsG6i5Lbp01a6FmvZQUTubvd9QJh38LS98P1sD7q2addEw8+dLkrWMakj0BvHKJlM0N9+IIZDepzMAbO+U0HE3ZZdM2qF4JeUVjj9VeJnW/ARe4ujIbTZFq4IoCzg7afMWYTYK6Mo2bp0EpoTYteHmhfkOrEY8Pl9uXklmY4+g4IpUJZouU4fncUg+eLXSfhNH+YP0bxpKMVktgopCeAB6ogZf0HJQPJJN9q2y6S14gj/0p+WMlypHfy2RDLZ+AvKuet8WQEyZJ3AAuhJgthNgqhDgihDgshPjbwOMrhRA7hBD7hRB7hBDr07rS4EbmUQpyc8gxiZTWwBs7XcytKEhcXTHqlKb5avlERc3GA1l4Xbmd05GkhG4neIY44y6gtkzKCDURbObRpgW3WXPIt5h1tdOrBmJBK9mjf0pN0017w9j/ryNgZJVNZZSwDUwY+x21KOXSv11XBi69wPM79stjVC5Nfq0X3SitZydTUrj/Mfn/qqqMVOZfIwd0t70zeWuZZiQSNbzAVxRFWQxsBO4SQiwBvgd8U1GUlcA3Al+njyo1gB+Whlb21HZjapYQnt0h5x6GB/DKJZBXEqyD15XZpZQwfJhwQEJ43GVLvAMzlKJAk4hOW1k9hlbqxmep3Qo9jfDkR6XbXTKM9Ev5nfr/WxoI4NmkRGnZC9YCeRcRoFB1zRz2Q9l86NSXgRfk5iDO75Obo/G6LhPBnAMbPyc3FdULTzpRtd8rb5UlxlDmXy0/GnJC3cQN4IqitCqKsi/w+SBwBJgFKIBaOygGtEcSLRTNgtxima0hb1FTNdTBOeqlfWCUeVoCadM2MFlgdlhWYTIF6uCBAB5tPmagiadhMF97/RugMJkAnqurBq7+vssKrFIRAbLDLxlCNzABimrAlJNdSpSWvVC9SurzA5hMgpJ8i7xLrFiouwbuyDVB6/5x2X3SrPqE3FB8axLa61Xt94qPTHyusApmrjDkhEmg6b5dCFEHrAJ2Al8Cvi+EOAv8APjHKN9zZ6DEsqezs1P/SoWAysVjWnC7hb4UbWI2qRuYWiSETdvkRow1Qtt97SUyQx1oDboSTtjIDATwdn+xdgUKQE6uVLzo0IJXFFh1yQjHtdGrw5jP7tZ8nHGo3XhqADfnQPHs7CmheEZkCSBCgHXYrfQNuWVm3nc62EmcKAPDXi62npezLmeloP6tklcEqz8pNxb7z6XuuOGEar9LZkd+zfxr5OzO4d70rWMak3AAF0IUAL8GvqQoygDweeDLiqLMBr4MPBTp+xRFeVBRlLWKoqytqKhIbrVVS6RiQVEotafOkbBRq4RwuE86z4WXT1RC9ODVJXlYzILm8AHHgQDYqRRr14Cr6NSCl9n1ZeDjjKwCTor0nxnLxvXQcUQ27xTXjD1WWp+aEorbJZ340kn7IfB7Igdwm0Ve9MoXyk7inkZNhx4Y8bDCFBjJNmt1KlY7xobPAgrsejC1xw0lXPsdCUNOmBQJBXAhhAUZvB9TFOU3gYdvA9TPfwWkdxMTZJY20g+DrThS6Eh4qtMlFYBlCZpYnX5TviHrL4/8/Izl8hb19JtSSuiwTWzmcbbjE2b6RYG20k0ohToDeIEsP/n92nb/e11uzCYhG0tcIXdTyUw/72iQd1ah9VFHXWpKKK99D35yRXpVDmodOYJCxGGzyrtEtTbepc0TZWDYw2L/Cch3QOncZFcatrhaWPxe2PtzuSGfDsK135GoWSv3jIwyii4SUaEIZHZ9RFGUH4Y8dR5Q7dC2AMk51yeCepvd3iAD+JD2IBSJxk4nNY588iwJGjQ1bZOGQjXrIj9vzoE5G8YpUSLVwAfMDqpL7OTrNYYqqtbnSFiQi9evBHXGidLtcuOwWeTUIGeH3JMwW+GczjKKoowF8FAc9fKWerhP33FVzu+Tx0nmDiEe5/bI/QhV1hmCQ92nKZsvH9DYUj844mW++6jM7lPtPQOw8S6ZEO1/PPXHjqT9joTJDPPfBSdeCnrpGyROIhn4pcAngC0ByeB+IcT1wB3AfwohDgD/DtyZxnVK1Dd6RwMOuxW/guYgFAnNY9SaX5eSqJwYDS21l0DnEXB1U1dm53T30HgpobODTqWEBXrLJyCDxnCvZimf3m7MHtfoWBu9q1Oef+YK/QF8sE2uP1welyolSmDDO62Klpa9UTcYZQ3cg2LJl9I9jRuZnuFBZow2p3YDM5TZ62VtfeePU9/YE0n7HY3510gPobaDqV3DBUAiKpQ3FEURiqIsVxRlZeDfnwKPr1EUZYWiKBsURUm/JslWKrOdjoZgq3Ky3Zh+v0JTlytxCaGrS9Y966KUT1TUOviZN6krtzHs8dERIiVUnO20eAoT9wCPRLCZR1sZRfVD0Sol7An1QXF1QkGF7Ow7/zZ4dZSzOgIbmKqEUMVRFzhhEoHX2Slb2CF9ipahHug5FbU+7bBZcPv8uNw+abOgIYD7/Ap17hOY8KcvgAshJ/b0NMLxF1J77Gja70jMf5f8eMLoytRK9nRiqlQuhvbDwUaJZKWEbQMjDHt8iW9gNgem79THMdOvXi3LLM3bg12WoWUU30A77f5ifRpwlSQDuFYlSo/LHZxsj7MD7JXSWc47Ii9qWgmXEKqoATyZwNsR4jWdLkXL+bflxygKkXGePRWLZAklwTKBc8QbsoGZpgAOsPgmqfpJpaQwlvY7EgWVUoZptNVrJgsD+BLoPEZpvqwbJ7uRqXqgzEtUQti0TW7MVK+K/bocq6yRn94+0ZXQ78c01EknJYnPwYxEsBtTWwBXSyjdLq0lFHfQxgBXp5QxqvsAesoo7Q1QMEPeWYWSWyiPnUzpQy2fpNOetmUvIKSdQgSCtsdDbpmBe4YS3rMYGPGw0nQSp60G7OWpWvFEzDmw/k44/Qac35+aY8bSfkdj/jXyb0iH6deFTPYF8Kql4BulwiPfCMlKCTVLCJteh9pNiVl61l0Gbe9QneceLyUc7sGk+JKTEMJYM49GLbjDZkUI6ArvDo2Bz6/QN+yRRlbuIWkFUFAh5X+F1fqUKJE2MIOLrEsuc+5oAFuZDK7pKqG07JUKk7ziiE+PK/MFlSiJlVH6hz2sMDUyWLY8JUuNyepPyk7SVMzNTET7HYkF10pl16m/JL+GaPzx7+Gha6fVZmn2BfDAG77EeRJITQZut5qpKkrAYW/gvJwyHk3/HU7tJYCC+dxO6UqoZuCBJh5PfsXEieNayC2QShCNGbjZJCi1WenS8LuTI+zCNOD2Svlx9jpprasFv08OgK6K4u/hqA8agumioyG99rSKEnMDE8YcCXtVLTgkrEQZ7j1PjehitDJydp9S8kvkSMBDv9YlSx1HItrvSMxaDfml6ZMTDrZJyeTZnZNr5JVmsi+AV1wECPJ6jmI1m5LexDzV6aS+wp6YiZU6fT7RAF6zTrbbn95OfWA+JhAM4PmOidIzzSQx2EGLodW4Lkw1gBcEAnjNOtkwM9ie+AJ6m2XtPFoGXlovPVL0bI76/dBxNBDA6+RmZqqHGPSflb+HmugBvDS0hGKvkHrnBLXgpvP7APCns/4dyobPyYvqrp8md5xEtN+RSLeccNdPpXdR4UzY9r1p44CYfQHckg+lcxEdDTjslpRk4AlLCJu3yTdh1bLEXm/Jlxla83bqyscGHCuBQFdSURPnAAmgUwuu1dCqJ7QLU22jV2uzNYEeLi118PAW+nAcdfKWWk8nZd9pKWGrWhIiSWzWfpxYqBPeYwTYonwLQgQycCFkFp5gBp7fsR+vYsI6axIycJC/p4tugL0Pyw5WPSSq/Y7G/GvkxbY1RbV4FfcQ7HlI/nyb/0l2UU+TxqHsC+AQHO7gsCXXTj/i8XG+fzhxCWHTNlnXNmn4tdVeAq37mV8MIx4/7QOjDHTJgFtZPUfHqsMoqpZBTqOOt0xnBu6IVEKZuULeaWgpo3Q0ACJwRxUBRxKBN6huWZo+e9qWvXEtXs0BQ6vgXWJ54qZWRT0HOabMprAocn09LWz6otTlH3hC3/dr0X5HYv67AJF6OeHBJ+XPtekuWP4Rqbp5bXpk4dkZwCuXQE8jlflKUhl4U5cLRUlwA7O3WQbKePLBcOouBb+Xi5XjwXMOdLXgUnKpq67UvuhwFlwDQ92Bnf/EKddoaDXeB0XNwAPeNpY8GcS1GFt1NMisL5IZGCTXzKNKCCsvCpEkpjqA75M/cxyL13FJRsVCWT6L12Hq91Pef5gDyjwKc3XMv9TLnI1SXbXjx/rKGFq035Gwl8taeCrlhH6/lEjOXAlzNsn/r8u+JJONptdSd54pInsDuOJnSU5rUhl4oxYXQq31b5XZG0CYmTMoNcPN3S5Ge1uT78JUWfw+eRu/9d80dWSWF+QyOOplxJNY5t7jDMnAnZ1SnmfJG3tBzTqpi/YluCcROsQhEgVVkJOvL3Nub4CSOVKOmF8ivURSWULxeRO2eA06EkLiG5k9p8jzDXLEvFDaFkwWQsgsvPukdo/uMzu1ab+jMf8aWZ5ydes/RignX5bCg01fHFvXyo/LWvhr30/NOaaQ7AzgAeXCAnEmqQy8sVOVECYSwLfJkkHFIm0nyS2EmSsoat+F1WySG5kuOQuzrCAFsyWFgKu/KevgO3+S8Lepo+MSHa3W4xqlKC8Hi9k0pgEPZfY68A4n1tDjGZEdjLECuBD6Ta06jow/dqqVKJ1HAhavCQRwm4UeV0gJBeKXUQIGWU3WKOWldLLkJtlfsEPDxJ4DT8Iv3gsltbDmtuTOv+BaQEmdnPCt+6TMden7xx6z5MGlfyu17wG/omwlOwO4ox7MudR6T9M/7MGn09CqscvFzOI8bNY4t6mKIgN4/eX6sovaSxAte5nrMNPc5SJ3uIvR3BQ2Z9RfLv/w3/hhwo0QZcFuzMTq4D1DnrELjqtzTIGiom5kJlJG6TomNyjDW+jD0WMr63XLjGtcAK9LbQklOEItvsWrdCQMXCRLaqX5VwIBfETk0WurT3KhOjBbZGNP0zZojeNN4vfBi/8Mv/2s9FW589WJfxdaqV4l9fupKKO0HZJlkg13yp8rlNW3ySRkW3Zn4dkZwM05ULGIGaON0tBqWJ+UsLEzwTFq3Sfl7D6t5ROV2kvBN8rmgrM0dw1R6OtBSfYPPZyr/wVGBmQQT4BytRszwTp4j2sUhy3wJnB2TOwOLK6Rt6WJbGSqXZKxMnAYy8C1bDZ1n5BysVB9eWk99J2VpY9U0LI3YYtXhz1kcpQ5B0rnxS+hnNvDKcsCCvJTcIemhzW3gcUma+HRGOmHx2+BN/8b1t0Bn/jtxI5aPZhMctTayZeTlxPueED+HGv+euJzVpssq5z6C5ybhNFyaSI7AzhA5RLKXLKZR08dXFGUxCWE6mZHPAOraNRuAgQbTEc409lLCU6sxTP1HSsaVUthxUdh54MyWMVB9UPpTDAD73a6ZRcmBEooYRcgIaS3cyJSwo4GmYmWzov9Oke9LFWossVECF4cQvTljno5NKA//u8lIVr2JWzx6rBZGfX6GXYH9hrKF8TWgntHoe0dDouFFOUn0eSVDPkO2djzzq8iW/F2nYSfvgsat8KN98INP5iY4SbD/MDGvOo1o4fBdrn+lR+TP08k1n1GPrctveN800n2BvCqJeSPdFCMU1cdvNM5yuCoN/H6d1GNflP9fAdULWXR6DuU+PsBKKxIQRNPOJv/SX7c+m9xX1qmOQN3y7q5zwPDPZFvlWvWy4zZGWd0XkcDlC+Kb0egR4nS0SBnapYtGHsslUqUUac8R4INNupdS0/oRmZPU/QGpTY54eeAf15yXbrJsuFz8k5m98/GP37yFfjZFvk38MnfwdpPp/7c87Yg5YRJDDve/TP5t7rh89Ffk1soPdGPvyC14VlI9gbwwO33InFWlyNhUIEST0Lo90PzG7J8kszueu2lVPYdYKaQu+sVM1KgAQ+nZLYclXXgSRkIYmCz5mCzmhOqgSuKQu+Qm9ICq7TThYmbmCDroBC/jNJxJH79G/TZynY0yOAdKu9LZTNP6wFZv080gIc6EkJgvJov+sUkUF/f6a6jKH8SJYThlM2DRdfD7oekuklRpBzvsZuljvqOrVIimw7sZfJuTm8d3DMsG3cWXgfl82O/dsOd0o4iS2vhWR/AF5rO6RpunLCEsKNB3s5FG5+WKLWXYPYNs8UsbwtLKlPQhRmJy74sh9a+/C9xXyq7MeMH8MFRLx6fIlvDg008EQL4zBUy+41VRhnulYqZePVvkFJAhLbA294w8eJQWC1LNqlQogQ3MBPNwEPa6UFqwSH6RmbLHpSCGZwcLZ7aDBykV/hwD+x7BJ77Ivz5H2VQ//Sf5Ui2dDL/GlmqUhMGLRx8Sr5nN90V/7V5xTLpOfL7sQawLCJ7A3hRNUpeMReJM7pq4I2dTnJzTMwqyY/9wqZt8qPe+rdKYMDDjeadAIiCquSOFw1bKVz+FZm9qGuPQpk9NyEZoaoBLw1t4olUQrHky3mgsZQoHUflx0QCeE6u3BxNtPQxMiCHLIf7q5hMUgGSihJKy155rAQtXicMHlFLO51R6uAte/HOWAWIqauBq9ReKi/Kz38V9j8KV/4f+KtfShO1dLPgGkDRXkZR7xRmLJNd04mw8fPSjXHbDzQvc6rJ3gAuBFQuYbH5nK4aeGOXi/pye/xGiaZtsvatxRozEgUVUL6QOhHYFIqUwaaK9Z+VNfuXvhFTwVGeoB+KGuRLC6xj9e1o65+9Xs6ijKb4iDaFJxpabGU71YtDhPb20vrUlFDiOBCGM86REGTwK5oVWYky3AvdJ3FVrACgKG8KSygg32NX/h+wlcOHfw6b/1GbjUQyzFwp33d/ugeOanAPPPmK3CQObdyJh61Ubmge/o3coM0isjeAA6JyCQvFWXo0znbE52Fu6x/5uPU1eXsY69/p7cln3yq1l8iP+aVxW7CTwpInNzTPvy3NhaKQqCNhT6w2+nBq1knlSLSGnvYGWXNUh1HEQ0szT0dAgRLp4uCoh57m5PwvBtulkkVLAM9XM/CQC2U0T5QW6UDY65BmaVOegQNcdD3ccxKWfmByz2sywV//Uap2nvyYzI4T+b/bcb8cErL0g9rOt+lu6W3z+n/qW+8UMcWX+CSpXEwhQzDYAiTu2ubd9RBfH70XOoDfJfANC6/Tu8Lx1F4mPYnTVT4JZcVH4K374ZVvwUU3RrxglBVY6Xa58fuVmHciPYHJPUEr2Zw8uYMfidAJPZEm1XQckSWORLOj0np50Rh1xr91b28Ai10OEA7HUQfuQVkb1TvhJjhCLfEAnmM2UZSXM/4usXyhtF1VlPG/h0AA7yhcCjRMfQ1cJZnN+2QoqoZPPQ+/uxv+8q/SwfKm+6P757Q3SF33ln/WniAVVMDaT8lu5iu/OrbxneFkdwAPNGsUD2q47fH78L/1APv882l/9//wnovj6LHN1uS7y1TUDDzVTTyRMJllc8/jH5YXjQ13TnhJmT0Xn1+hf9gTVEtEQm0FL1V9UOyV0d/UJXPkBercblh/x/jnFEWWULRkR6GuhDMujv1adcJPpNv8UCWK3gDeegAQsr6qgVK7dbxvffkCeTEZbB2bawrBCT+9PrkvM6UqlEzBkg8f/Kl8r7/8TWnB8JHH5d5IODsekP45eqWNl/yNVN28cS+877+TW/ckkdUlFHWzqmLoVOLfc+x5rAOn+an3BmbVLpB/CLH+pTLYFs+S9VnVEyPdLLhGZv2vfTfiQIPywkA7fZzZmD2uUfIsJmk54IrQhRmKEDILjzRibbBVdvBFm8ITiUQHHCtKnBFtKbCVbT0gg6/GTbwSm3ViCQXGl1FCJvwMjMhgnzEZ+FQjhFRXffRJ6G6EBzdL86xQnJ1w8GlY+VH9HaFFM2H1J+TdUf+55Nc9CWR3AM930G+poNqt4U254wF6rTN43byBxTOL0re2aHz6ebj225NzLiHgmm9Jk/w3fzTh6fJA1t05GHsjszt0Gn0kH5RwZq+Xio/whp5EW+hDSbSZx9khyyNRR7TVJnacWLQekKoMjcgMPOR3rBqihW5k9p+VF8dZa4LWEBlRA88kFl0Ht78sh0X84kZ4+9Gx5/Y8BL5R2PiF5M5x6d8CCmz/r+SOM0lkdwAHeuzzmes/jdeXgG/C+bfh9HZ+ZbqBlbXl0llvsskrHm/Dmm5q1sCS98Ob900YeRY0tIqbgbtl+QQCJZQ4Chq1Dt6yZ/zjQZ/uKFlyJPIdcgpSvMy5I87FwZIvvVr0KlFc3XLEm44AXmKz0OsKKaEUVEk73tAMPERfPjDiRQgm1ws8W6i8CO74i/T2fu4ueOGf5AShXT+FBe+Wd0jJUDJHWlLs/UVkG4EMI+sDuKt4AfPFefpdCXhhv/UAirWA+/o3saE+BcY72cK7viGzk9e+O+7hRNvpgwHc708sA69eJRt6wssoHUdkENV6i5uIEiVeAIfkbGXbAq3WejLw8BKKEDLQhGrBW/bK/ZaqixkY9lCQmzO5XuDZhK0UPv4b2e6/4354YKO8y9yUZPatcvnfSRuBCHetmUbWB3B32WJyhQdnaxyLzoHzcPg3nKm9mQHFxvoLKYCXzZP2mXt/Pm4ajMNmxSSI240ZDOAjfbINPF4GbsmXG33hHZnth7Vl3yqJ2Mp2NMh1FcRYWzK2sqpXhsYNTJDt9ENuYx5DawAAGnZJREFU3/jhGeHzMc/tlU1QOVYGRjxG/Tse5hx4z3fhvf8NA61QdbH2aVnRKJ0Lyz4Me/5XXyfoJJL1AZwqGRA8rXEGCex6EBQ/v89/L9YcEytml0zC4jKIeVsCHhzNwYfMJkGpPX4zTzCAO+NowEOpWS+zSrWhx++TGaeW+reKo16Os4tlB9seYwNTpbRebqRqmFwUpPWA7MCM5mwXA7WdfpzlQ/lCGDwvN5fVCT81awEYGPYa9e9EWXMbfOEt+NjTqZU7Xv4V+XfylobBFlNA1gfw3BlL8Ckito+B2wV7HoaLbuTF83msnF1CnsU8eYvMBEoC2uiwKe9l9tjNPCMeH0NuX/w2+nDUhh61tNHTKMs4ugJ4nbylHWiJ/LzfL7swYwwYlsdRN0RPa1+Dzg1MCHEkDNeCg6yDdx4dN+FHZuBG/TthyhdIhVcqqVgop/js+mnCQ1KmgqwP4I7iIpqVGeR2H43+ov2Pw0gfQ2s/z6GWfjZeSOUTlSgBvLzQGtMPZfww4zht9KHMVht6AnXwWF2S8YinROlrlgEw3rH12sqO9MsLkN4Ablcz8EgB/MQEg6yBYY+RgWcCV9wj9foaRhVONtkfwG1WjimzKRyIUgP3++VkkVlr2OWZh1+B9fVlk7vITCC/RLawR8jAY9XAxxlZBX1QEsjAS2rl61Rjq/YGQECFjjmP8TTcicoT9drKtr0jP85MvNs3FLWEMs50rbRebvR2HZdqnbySoN/84IjXqIFnAlVLZRfzzh9Lo7QMJOsDeL7VzCkxh+Lhc+AemviCE3+W3Vub7mJXcy85JsHq2gus/q1SMmdiAC+wxlShqEEnWEIR5sTqwGpDj7qR2XFYBihLHPfHSBRVg8kSPfCq5bN4FwdbGVgLtStR1A3Mmcu1fV8AR7gjIcgJNqVzAwF8/IQfmYEbJZSM4Iq/l3dgux6c6pVEJOsDOECrtR6BMuZGF8pb90tnvsU3sauph2U1xfGHGE9XIgTw8oJcnKPe8QqJEMb5oKizMBN1pJu9Tl48Xd2JD3GIhMksG3GilT46DsuMP16HZHDSvY4AXlituyu3JD/MkVClfCGcPzBuwo/PrzA4amTgGUP1Kjkw/K37pR9PhjEtAninPTB1I3wjs/UANL8OGz7LsE9w4FzfhSUfDEcN4CGubupw42hlFDU7L7PnSklVIuUTFXVSffM2WUPWs4GpEkvD3XEk8fb80jrtJZQkNjABrDkmCnNzxmvBQW6+9Z8ZN+HHOSKVNkYNPIO44h452GLvw1O9kgnEDeBCiNlCiK1CiCNCiMNCiL8Nee5uIcSxwONTNhl0tHAOo1jHNspU3npAutOt/iRvn+3F41PYeCHWv1VK5shNmeHe4ENqi3y0MkqPy02OSchbeldHbJ11ONWrZMnl7UdlkEoqgNdFnlDvHZUbgYke21EvVSiJTjx3u2SZI4kADlBit0TIwBeNfR6iQIEM8AI3GGP2eqkx3/7f+iSoaSSRDNwLfEVRlMXARuAuIcQSIcRm4CZguaIoS4EpG2dRbM+n2VQjG0VUBlrh0K/ldO38EnY29iAErKnTruOdNkRQogS7MaO00/e43DjsVoQQY06EiWK1SQfBk6/Ir5MJ4KX1MDow7uIDyOCt+BJvEHLUSTnj4PnEXt9+WF58kgzgshszbPSfqkQpmRO8MPYbPiiZyZVflQnMvkemeiXjiBvAFUVpVRRlX+DzQeAIMAv4PPAfiqKMBp7rSOdCY1Fqs3DUP3t8CWX3T6V2eOPnANjV1MOSmUUXdm0xQgAvD/ihRGvm6XG55SxMRZEyQq1WrDXrAUWa5QdUFrqIpkQJyhMTLaFoVKK06m+hD2WCIyGMDdwN8Rc3nAgzlLrLYM4l8Mb/k3d9GYKmGrgQog5YBewEFgKXCyF2CiFeE0Ksi/I9dwoh9ggh9nR2dkZ6SdI47FYOeWaBs02K7t1Dsg32ohugdC5ur599Z3rZcCGXTyBmBh6tBh7swnQ7wTusfSNPnVRfsUi2P+slmoa7/bBUqJTFmT4ePI5GW9nW/XKkWKhvtw4mOBKCNDa75O5x/tUDw2oN3CihZBxX3iPv3PY/NtUrCZJwABdCFAC/Br6kKMoAchiEA1lWuQd4WoiJvayKojyoKMpaRVHWVlSkZw5kqV1qwQGZkR14Qt5qB6ZSHzzXx6jXf2FvYEJELbjNmoPNao5ZA5ezMNU2eo0BXHUmTKZ8AtEDeMcRWYowJ5ixFtfIunyiShR1AzPJNu0JjoQq134b6q8Ifmlk4BnM3M3ybumNe8EX4f9yCkgogAshLMjg/ZiiKL8JPHwO+I0i2QX4AZ2jTpLDYbNyzB8I4O2HZePOzJXSchLY2SRbYS/4AA5RpYTR2umlF7h1zNRH6zBmRx0svwWW3axjsSFYbXLWYU/z+Mc7GrTJE80WOaA6kRKKdxQ6jiZdPgFZA3eOenF7Y2+eGl7gGYwQcMVX5fvn4NNTvRogMRWKAB4CjiiK8sOQp54FtgResxCwAlNi3eWwWWnHgddaDDv/B7pPjJtKvbOph4VVBWOe1hcyUZp5ItXAPT4//cOeMB8UjQFcCPjgg3I6ULKEa7hHBuQgBK0Oh4naynYcAb8nJQG8JFI7fQQML/AMZ+G7pWvk6/8pDdqmmEQy8EuBTwBbhBD7A/+uB/4XmCuEOAQ8CdymKMmM/NaP7HQTDBQtkHrjwmppRAN4fX72NvcY2bdKBC14tHZ61T1vvBPhJMzzjEZp/fjMWd20jmdiFU6izTxJdmCGUhpop5+gRAnD8ALPcISQuvCeU3DoN/Ffn2YSUaG8oSiKUBRluaIoKwP//qQoiltRlI8rinKxoiirFUX5y2QsOBJqZt1lmycf2HBnsCZ6+PwALrfP2MBUiaAFLy+IbGiluueVjiuhTEmVTOKol77unhH5dXCIg8YMvLRe/vwh3ugRaT0g9wzUjc8kiOhIGAHDCzwLuOhGuafz+g/i9xMMtsHRP8Ff/g36zqZ8KdPiPk01CzpRuIGFpbvl8IIAuwL17wtqAk8sQpUogck45QW59Ljc+P3KuMyvO7SN/myH9EBJdLMwHTjqAAX6TktVS0cDWAvGfqaEjxMiJcyPYVDVekBm3ynwmY7oSBgBwws8CzCZpF/4rz8DR34XvNvH1S3HNp5/G87vkx8HW+Vzwiw3QEtmp3Qp0yKA51nM2Kxm3s7fxA1/85lxz+1s6qG+3E5l0STOocxkQgN4tQxeZQVWfH6FPrXeHaDHFdJG7+yY2vIJjNdwVywaG+KgNcCGKlqqowRwnxfaD8G62/WudvwpIzkSRsDwAs8Sln4AXv0OvPJNOPxbGbBD95bKFkh1UfUqqF4tJzlZbSlfxrT5S3HYrBPeHH6/wu7mHq5bOmOKVpWBRNSCq+30oxEDeLCEotPMKWWEargVRWbgi9+r4zh18mMsJUrXcfCOpGQDE6SMEMKm8kRgYNjD7NLUv9ENUozJDJv/CZ75tNzMnLVGXuyrV8m/mbziSVnG9AngEbwmjrUP0j/sMTYwQ4mgBR8ztHKzoGrspWoAL7FZpApFxzzIlGIvl942vU3gbJcGQ4l2YIaSVyStZWMpUVLUgRk8ZeAuMV4N3PACzyIu/hAsugEsU3d3Py3cCEHNwMdnNzsbuwHYMNcI4OMIkxKOtdOPV6L0uNwU51uwmE3afVDSgRBjShS9G5gqjjiDklsPgMWWeIdnIqeM1E4fhuEFnmVMYfCGaRTAS+3WCRtEu5p7mFWST43DuCUdR1gALwuUTcKbeYJNPJ4RGO3XrgFPB446mTknOoUnGuGSxHBaD8g7DlPqZqdGuksMxfACN9DKtAngDpt13O2poijsauox1CeRCNOCl9ismAQTpIQ9zoAPypDOLsx0oNrKth+WdwR6ZY2OOug/B94IAdXvh7aDKSufBE8ZyZEwBMML3EAr0yaAl9qtDI548fikLvNUp4sup9uof0ciTAtuNglKIzTzBI2sMqGJR6W0XtrBNm7VP+EHZAlF8ctOznB6GqV5V1oCePQM3PACN9DKtAngaqOE+gbZZfifRCeoRDkdfKg8Qjt9z1AggKvT6KdahQJjSpTBVu0dmKHEmnTful9+THEAL7VbY5ZQDC9wA61MnwBuV+cOyjfBzqZuKgpzqS+3T+WyMpMotrKhNXBFUeh1hQXwqezCVFElgKB/AzP0OJGUKK0HwGyNPyRZIyU2CwMjXry+yN17hhOhgVamTQAf85pwoygKOxul/0kEh1uDKIMdQmvgA8NevH4l80ooJXNkVxskV0IpmAE5eZE3MtsOSnliirtOVY1933DkOrjhBW6glWkTwMcycDfneodpGxhho1E+iUwELXiZPZeuwbEMXG2jLysIZODWgrR0kmnGbJGe3ojkMmSTaWxDNBRFSXqIcTRKbGN/o5EwMnADrUybS72a3fQMudkR0H+vNwysohMuJSyw4nL7GHb7yA9pOCm15wZGqWWAAkWlbJ6U91mTLI+pksRQ+s/Kzd0ZyTsQhhPPkdDwAjfQyrQJ4Gqrcq/LzenuIUpsFhZUFkzxqjKYkjnjNvDKQ4Yb11htYwHcFiihZFIAv/bbclp8sjjqoel1mXWrpbZgB2YMkyudlMRxJDS8wA20Mm1KKLk5ZuxWMz0uDzubelhfV2p4KsciTAsePtw4GMDVEkomKFBUqpaOzdpMhtJ68LjGNmlBBnBhTq6+Hu10cRwJDS9wA61MmwAOsg5+pHWAMz1DhnwwHiVzpNY5oAUPNbSCsaaeMlWFkkkZeKqIpERpPSBr65b81J8ujiOh4QVuoJVpFcBL7VZ2NUv998a5Rv07JmFa8LF2+rEM3GY1k2cGhrqnaQCPoAVP0wYmQL7VTJ7FFNWR0PACN9DKtArgDpv0tS7IzWHxzKKpXk5mEyYlVEsonYEMPNiFOdQtOxYzqYSSKkrmAGJMiTLYJl0O0xTAYaLlQyiGF7iBVqZVAFdrjGvrHJiNOmJswgJ4vlXuIYRm4OM14NMwA7fkQVH1WAklxRaykXDYJpquqUgnQiMDN0icaRXA1V1+Y/5lAkTSghfkBvXfPRO6MKdhAIfxtrKtBwABMy5O3+nslqgZuOEFbqCVaRXAVZ2tsYGZIBG04BMy8EzyQUkHpXVjJZTWA9L/O7cwbaeTGXh0HbjRhWmghWn113LlogpOdjpZXjM544yyngla8FzO9gwBUg9eNt1LKCCVKM52qStvPQCzN6T3dBFG/4HhBW6gj2mVgS+vKeG/PrJKTpAxiM8ELbh0JBxyexnx+Me6MM3WSZvxN+moSpSWfbILM431b5BS1/5hDz6/Mu5xwwvcQA9GpLuQCdeC23PpcY0GyyildsuYBny6moKptrINz8mP6Q7gNguKMmYdq2J4gRvowQjgFzJhWvDyAit+BU51OoGAD0qmtdGnGjUDP/J7+XFm6j1QQlGVUuGDHQwvcAM9GAH8QiZMSqh2Yx5vHwQY28ScrhuYAPkOqcZxtkFJrfw6jURzJDScCA30YATwC5kJAVwGl+PtMgOf1m30KkJIJQqkvXwC0R0JDS9wAz0YAfxCJkwLrnZjnghm4JbpH8BhzBMlzeUTGO+aGYqRgRvowQjgFzohWvBgAO9wYjELCnGBzz29SygwVgdPg4VsONFq4IYXuIEejAB+oRMSwEvyLZgEDLl9OGxWhKtLvma6Z+Cz1siJQ9Wr034qm9WM1WyaoAU3vMAN9GAE8AudEC24ySSk8gSmvw9KKIvfC/ecBHv6LRiEEDjsFvpc4TVwwwvcQDtGAL/QCdOCq5N5grMwYfqXUIRIi/93NCJ1Yxpe4AZ6iBvAhRD/v727j62zLOM4/v21a8d6NuhpKILA2CT8ASEESIMkIBIUgmgAEzViJBhNJgkmEDG+gIloYmJ8QWNIMBhINEGRyIvEaIQ/MMofIGNsDpwomgGDycrL4rohY3D5x3M/6+nZaXv29Dx9ztP9PknT9mnPznXnbq/dvZ77vs7xkh6WtEXS05Kubfv6lySFpCPLC9NKc8Be8HwFvnzpN7KqSKeOhO4FbkV0swLfB1wfEScDZwPXSDoFsuQOXAg8P8fjrZ/NspVwbGQoK6FoAEbc3bGXOnUkdC9wK2LeBB4R2yNiQ/p4F7AFODZ9+YfAl4GY5eHW79oTeKN1Bb4jS94Dg1VFtyR16kjoXuBWxEHVwCWtAc4AHpN0KfBiRGwqIS5bLO17wVelFfjKYdj9CjSWeP27As2RYV7fs5d3WhpauRe4FdH132ySVgL3ANeRlVVuBC7q4nHrgHUAq1evLhallat1L3hage9vJdvwrY1eazaynjO7/rePI9LBHvcCtyK6WoFLGiJL3ndGxL3AicBaYJOkrcBxwAZJR7c/NiJui4iJiJgYH/fNsL7UksCPOjxL4OOrUgllqe9AqUAzJe18J4p7gVtR3exCEXA7sCUibgaIiM0RcVRErImINcA24MyI+E+p0Vo5WvaCv++kcW751BlMnNB0CaUkzbbTmO4FbkV1swI/B7gSuEDSxvR2Sclx2WJq2Qs+OCA+ctq70VtvZNdcQum5ZltHQvcCt6Lm/YmJiEeAOY+HpVW41VXrXvCR9Hqiu9MpTJdQeq69I6F7gVtRPolpB2wlBLLyCbiEUoLRxsyOhO5EaEU5gVvnBL6/D4pLKL22avkylg1ofw3cvcCtKCdwO2AvOOASSokkMZr2goNX4FacE7hlWrYSAu6DUrKxxhCvp46E7gVuRTmBW6Y9gU9NwmFHwLLl1cW0hLV2JHQvcCvKCdwyLXvBgayE4tV3aVo7EroXuBXlBG6Ztr7gTE16B0qJmo1hXstLKO4FbgU5gVumrS84uydhpVfgZWmODLFzz14iwr3ArTAncMu0byV0CaVUY41h9qUeKO4FbkU5gVumNYG//VZWSnEJpTSj6TTmzt1vuRe4FeYEbpnWveD5KUyXUEoz1pjuSOhe4FaUE7hNy3ei7D5EXo2+QqMj0x0J3QvcinICt2l5Ap/KD/G4hFKWvKHVK7vedC9wK8wJ3Ka1r8BdQilN3lL2hdffAHwK04pxArdp+V7wyWeyz70CL82qw5YxOCCee3U34F7gVowTuE3Ld6K8+AQsWwHDjWrjWcIGBsToiiGee3UP4BW4FeMEbtPyBP7Sk1n5RD7aXaZmY7hlBe4EbgfPCdym5Ql875TLJ4ugOTK0/1V5vAvFinACt2n5XnDwFsJFkN/IBK/ArRgncJspX4V7B0rpZiRw18CtACdwmylP4C6hlK7ZyBK4e4FbUU7gNtP+BO4VeNmaI9mq273ArSgncJvJJZRFk6/AXf+2opzAbabmmuz9yqMrDeNQkNfAXf+2opzAbaaTLoRLb4HVZ1cdyZKXdyT0KUwryj85NtPgEJx5ZdVRHBJGvQK3BfIK3KwieUdC18CtKCdws4ocvmIo20LoEooV5J8cs4oMDoivf/gU3rt2rOpQrKacwM0q9Llz11YdgtWYSyhmZjXlBG5mVlNO4GZmNTVvApd0vKSHJW2R9LSka9P170n6u6S/SrpP0mj54ZqZWa6bFfg+4PqIOBk4G7hG0inAQ8CpEXEa8A/ga+WFaWZm7eZN4BGxPSI2pI93AVuAYyPiwYjYl77tUeC48sI0M7N2B1UDl7QGOAN4rO1LnwV+P8tj1klaL2n95ORkkRjNzKyDrhO4pJXAPcB1EfHflus3kpVZ7uz0uIi4LSImImJifNwtSs3MekURMf83SUPAb4E/RMTNLdevAq4GPhARe7r4dyaB5wrGeiTwSsHH9huPpf8slXGAx9KvFjKWEyLigBXwvAlckoCfAa9FxHUt1y8GbgbeHxGl10YkrY+IibKfZzF4LP1nqYwDPJZ+VcZYujlKfw5wJbBZ0sZ07Qbgx8By4KEsx/NoRFzdy+DMzGx28ybwiHgE6PSCfb/rfThmZtatOp3EvK3qAHrIY+k/S2Uc4LH0q56PpaubmGZm1n/qtAI3M7MWTuBmZjVViwQu6WJJz0h6VtJXq45nISRtlbRZ0kZJ66uOp1uS7pC0Q9JTLdfGJD0k6Z/pfbPKGLs1y1hukvRimpeNki6pMsZuzdFsrlZzM8c4ajcvkg6T9BdJm9JYvpmur5X0WJqTX0kaXvBz9XsNXNIgWbOsC4FtwOPAFRHxt0oDK0jSVmAiImp1OEHSecAU8POIODVd+y7Z+YDvpP9YmxHxlSrj7MYsY7kJmIqI71cZ28GSdAxwTERskLQKeAK4HPgMNZqbOcbxCWo2L+nsTCMiptIhyEeAa4EvAvdGxF2SfgJsiohbF/JcdViBnwU8GxH/joi9wF3AZRXHdMiJiD8Br7VdvozskBfp/eWLGlRBs4yllmZrNkfN5maOcdROZKbSp0PpLYALgF+n6z2Zkzok8GOBF1o+30ZNJzYJ4EFJT0haV3UwC/SuiNgO2S8gcFTF8SzUF1J/+zv6veTQSVuzudrOTYemebWbF0mD6eDjDrLW2/8CdrZ0cO1JHqtDAu90iKi/6z5zOycizgQ+RNZb/byqAzIAbgVOBE4HtgM/qDacgzNbs7m66TCOWs5LRLwdEaeTtdk+Czi507ct9HnqkMC3Ace3fH4c8FJFsSxYRLyU3u8A7iOb3Lp6OdUu8xrmjorjKSwiXk6/dO8AP6VG85LqrPcAd0bEvely7eam0zjqPC8AEbET+CPZi+GMSspPv/ckj9UhgT8OnJTu4A4DnwQeqDimQiQ10g0aJDWAi4Cn5n5UX3sAuCp9fBXwmwpjWZA82SUfpSbzkm6Y3Q5sae0USs3mZrZx1HFeJI0rvcSkpBXAB8lq+g8DH0vf1pM56ftdKABp69CPgEHgjoj4dsUhFSLpPWSrbsj60PyiLmOR9EvgfLKWmC8D3wDuB+4GVgPPAx+PiL6/OTjLWM4n+zM9gK3A5/Macj+TdC7wZ2Az8E66fANZ/bg2czPHOK6gZvMi6TSym5SDZIvkuyPiW+n3/y5gDHgS+HREvLmg56pDAjczswPVoYRiZmYdOIGbmdWUE7iZWU05gZuZ1ZQTuJlZTTmBm5nVlBO4mVlN/R+6/sO/IpHpEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.30967741935483\n",
      "4.553132154006245\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b4fdaf82c8>]"
      ]
     },
     "execution_count": 5,
     "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": [
    "################################\n",
    "\n",
    "# 福岡と東京に気温差はあるか？\n",
    "\n",
    "import pandas as pd\n",
    "# 日付がついておらず、対応は取れないデータ\n",
    "temper_FT=pd.read_csv(data_dir + '/temperature_FT_August.csv')\n",
    "\n",
    "# 可視化 : plot\n",
    "plt.plot(temper_FT['Fukuoka'])\n",
    "plt.plot(temper_FT['Tokyo'])\n",
    "plt.show()\n",
    "\n",
    "# 可視化 : histgram\n",
    "plt.hist(temper_FT['Fukuoka'], color='blue', alpha=0.5)\n",
    "plt.hist(temper_FT['Tokyo'], color='green', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "# 福岡：histogram へガウス分布を当てはめる\n",
    "from scipy.stats import norm\n",
    "F_mean = np.mean(temper_FT['Fukuoka']) # 平均\n",
    "F_var = np.var(temper_FT['Fukuoka']) # 分散\n",
    "print(F_mean)\n",
    "print(F_var)\n",
    "\n",
    "# ヒストグラムをプロット\n",
    "plt.hist(temper_FT['Fukuoka'], color='blue', range=(20,35), density=True)\n",
    "# 正規分布を線でプロット\n",
    "x = np.arange(20,35,0.01) # xの値を20～35で0.01刻みで生成\n",
    "plt.plot(x,norm.pdf(x,loc=F_mean,scale=np.sqrt(F_var)),lw=3,color=\"r\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2955,
     "status": "ok",
     "timestamp": 1565657296787,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "QM_2aMR_WPu2",
    "outputId": "fbb5c59c-3dc9-4c2a-ba21-b48d13697897"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.116129032258065\n",
      "2.8071592091571267\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b4fdcecf48>]"
      ]
     },
     "execution_count": 6,
     "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": [
    "\n",
    "# 東京：histogram へガウス分布を当てはめる\n",
    "T_mean = np.mean(temper_FT['Tokyo'])\n",
    "T_var = np.var(temper_FT['Tokyo'])\n",
    "print(T_mean)\n",
    "print(T_var)\n",
    "\n",
    "# ヒストグラムをプロット\n",
    "plt.hist(temper_FT['Tokyo'], color='green', \n",
    "         range=(20,35), density=True)\n",
    "# 正規分布を線でプロット\n",
    "x = np.arange(20,35,0.01) # xの値を20～35で0.01刻みで生成\n",
    "plt.plot(x,norm.pdf(x,loc=T_mean,scale=np.sqrt(T_var)),\n",
    "         lw=3,color=\"r\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3185,
     "status": "ok",
     "timestamp": 1565657297031,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "D05hkHhiWm0q",
    "outputId": "82ad63f6-84c6-49b0-ee78-7872c62dab1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.42853766713343\n",
      "4.370711613741879e-05\n"
     ]
    }
   ],
   "source": [
    "# 対応なしt検定\n",
    "from scipy import stats\n",
    "t, p = stats.ttest_ind(temper_FT['Fukuoka'], \n",
    "                       temper_FT['Tokyo'], \n",
    "                       equal_var=False)\n",
    "print(t)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 635,
     "status": "ok",
     "timestamp": 1565657318145,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "3NRQqgKKcaOY",
    "outputId": "e14604d2-e212-4647-ba83-3d277bbe9139"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.977020238021845\n",
      "1.4868011795091875e-06\n"
     ]
    }
   ],
   "source": [
    "# 対応あり t検定\n",
    "import pandas as pd\n",
    "temper_FT=pd.read_csv(data_dir + 'temperature_FT_August_pairs.csv') # 日付で対応が取れている\n",
    "t, p = stats.ttest_rel(temper_FT['Fukuoka'], temper_FT['Tokyo'])\n",
    "print(t)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 756,
     "status": "ok",
     "timestamp": 1565657356874,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "SfK48w8BcwRZ",
    "outputId": "fbd0e8a6-c165-4680-ff4f-7503d69780c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.890806873380345\n",
      "2.61618782053936e-05\n"
     ]
    },
    {
     "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": [
    "# 分散分析\n",
    "# 東京のデータだけランダムに並び替え\n",
    "#random_temper_tokyo = np.random.permutation(temper_FT['Tokyo'])\n",
    "\n",
    "# データ読み込み\n",
    "temper_FTK_August=pd.read_csv(data_dir + '/temperature_FTK_August.csv')\n",
    "\n",
    "# 3群のデータをグラフでみる\n",
    "plt.boxplot([temper_FTK_August['Fukuoka'],\n",
    "             temper_FTK_August['Tokyo'],\n",
    "             temper_FTK_August['Kyoto']])\n",
    "\n",
    "# 3群の検定（分散分析）\n",
    "f, p = stats.f_oneway(temper_FTK_August['Fukuoka'], \n",
    "                      temper_FTK_August['Tokyo'],\n",
    "                      temper_FTK_August['Kyoto'])\n",
    "print(f)\n",
    "print(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 718,
     "status": "ok",
     "timestamp": 1565657385443,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "HNtdvSgTc2kX",
    "outputId": "91bef881-eeee-4b2b-a176-9a786480ff8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple Comparison of Means - Tukey HSD, FWER=0.05\n",
      "===================================================\n",
      "group1 group2 meandiff p-adj   lower  upper  reject\n",
      "---------------------------------------------------\n",
      "   0.0    1.0  -2.1935  0.001 -3.3511 -1.036   True\n",
      "   0.0    2.0  -0.3226 0.7647 -1.4802  0.835  False\n",
      "   1.0    2.0    1.871  0.001  0.7134 3.0286   True\n",
      "---------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 多重比較\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from statsmodels.stats.multicomp import MultiComparison\n",
    "\n",
    "def TukeyKramer(*args): #可変長引数で比較するリストを受け取る\n",
    "    idx = 0\n",
    "    data_arr = np.array([]) #全てのデータを一次元配列で列記する\n",
    "    idx_arr = np.array([]) #データに対応するインデックスを一次元配列で列記する\n",
    "    # (今回であればFukuokaは0, Tokyoは1, Kyotoは2になる)\n",
    "    \n",
    "    for lst in args: # argsは複数の引数が格納されたlistであり、for文でargsの要素(lst)を1つずつ処理していく\n",
    "        for nums in lst: # lstは数字(温度)が格納されたlistであり、for文でlstの要素(nums)を1つずつ処理していく\n",
    "            if isinstance(nums, str) == False: # numsが文字列(欠損値など)であればskip\n",
    "                \n",
    "                # 温度の値を一次元配列に列記していく\n",
    "                data_arr = np.append(data_arr, nums)\n",
    "                # データに対応するインデックスを一次元配列に列記していく\n",
    "                idx_arr = np.append(idx_arr, idx)\n",
    "        \n",
    "        # 1種類のデータ(Fukuoka等)を全て列記し終えたらインデックスを+1\n",
    "        idx += 1\n",
    "        \n",
    "    # Tukey-Kramer検定\n",
    "    print(pairwise_tukeyhsd(data_arr, idx_arr)) #第3引数のdefaultはp=0.05\n",
    "\n",
    "# Fukuoka, Tokyo, Kyotoの温度を多重比較\n",
    "TukeyKramer(temper_FTK_August['Fukuoka'], \n",
    "            temper_FTK_August['Tokyo'],\n",
    "            temper_FTK_August['Kyoto'])     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 626,
     "status": "ok",
     "timestamp": 1565657400384,
     "user": {
      "displayName": "竜馬備瀬",
      "photoUrl": "",
      "userId": "17932567482837919211"
     },
     "user_tz": -540
    },
    "id": "XMtp2nylc9UF",
    "outputId": "9203ea4c-287d-40cf-bc88-3878df6ee684"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.904761904761905\n",
      "0.007716629386744526\n"
     ]
    }
   ],
   "source": [
    "# カイ2乗分布\n",
    "from pandas import DataFrame,Series\n",
    "from scipy.stats import chisquare\n",
    "# 肺癌調査結果\n",
    "#　　　　　　　　　　正常，肺癌\n",
    "#'smoking'    :60,40\n",
    "#'non-smoking':120,30\n",
    "numb  = Series([60,120,40,30],name =\"観測度数\")\n",
    "theo = Series([100/250*180, 150/250*180, 100/250*70, 150/250*70],name=\"期待度数\")\n",
    "x, p = chisquare(numb,theo)\n",
    "print(x)\n",
    "print(p)"
   ]
  }
 ],
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   "name": "statistics.ipynb",
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   "version": "0.3.2"
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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