2_statistics_short_ex.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"statistics_shortex.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.0"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"FbhGkX5Pd6FM","executionInfo":{"status":"ok","timestamp":1632977488280,"user_tz":-540,"elapsed":2832,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"b0b2033b-2d28-44a0-baa5-163bad57bf0e"},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","######### 練習１ ###########\n","data_dir ='./data/'\n","# height_weight.csvを読み込み\n","HW_data = pd.read_csv(data_dir + \"height_weight.csv\")\n","\n","# 体重のヒストグラムを表示\n","plt.hist(HW_data['Wt'])\n","plt.show()\n","\n","# bin数を30に設定\n","plt.hist(HW_data['Wt'], bins=30)\n","plt.show()\n","\n","# bin数を50に設定\n","plt.hist(HW_data['Wt'], bins=50)\n","plt.show()\n","\n","# bin数を1000に設定\n","plt.hist(HW_data['Wt'], bins=1000)\n","plt.show()"],"execution_count":5,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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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"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"UNNlonJHed9b","executionInfo":{"status":"ok","timestamp":1632979031715,"user_tz":-540,"elapsed":359,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"690c5cf9-881c-40ad-ef76-635d4c0dbaf4"},"source":["######### 練習２ ###########\n","HW_data = pd.read_csv(data_dir + \"height_weight.csv\")\n","Wt_mu = np.mean(HW_data['Wt']) # 平均\n","Wt_var = np.var(HW_data['Wt']) # 分散：標準偏差の二乗\n","Wt_std = np.std(HW_data['Wt']) # 標準偏差\n","\n","from scipy.stats import norm\n","# 体重のヒストグラムを表示 (density=Trueで正規化)\n","plt.hist(HW_data['Wt'], density=True)\n","# 横軸の最小値\n","a = np.min(HW_data['Wt'])-5  # -5は正規分布の裾を表示するため\n","# 横軸の最大値\n","b = np.max(HW_data['Wt'])+5  # +5は正規分布の裾を表示するため\n","# aからbまで0.01刻みの数字が入った配列\n","x = np.arange(a,b,0.01)\n","# 平均(loc)がWt_mu，標準偏差(scale)がWt_stdの正規分布を生成\n","y = norm.pdf(x,loc=Wt_mu,scale=Wt_std)\n","# 正規分布を表示\n","plt.plot(x,y,lw=3,color=\"r\")  # lw：線の太さ"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fe2aa65c8d0>]"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":303},"id":"QsXiorYzewI2","executionInfo":{"status":"ok","timestamp":1632979044813,"user_tz":-540,"elapsed":357,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"48c07f68-3032-4b6a-c855-63e244b924f6"},"source":["######### 練習３ ###########\n","import random\n","\n","N = 1000 # 1回の標本中のサンプリング数 変えてみよう！\n","K = 100 # 標本回数 変えてみよう！\n","\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","\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)\n","std = np.std(meanList)\n","print(mu)\n","print(std)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["0.0027365969174211586\n","0.03243311391003242\n"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rjt74S23e7Pc","executionInfo":{"status":"ok","timestamp":1632979062016,"user_tz":-540,"elapsed":342,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"8662e9d5-a629-4e36-831b-d3beb4f30361"},"source":["######### 練習４ ###########\n","from scipy import stats\n","\n","# data読み込み\n","Grade_data=pd.read_csv(data_dir + \"grade_data1.csv\",sep=\",\")\n","\n","# Mathematicsの平均と標準偏差を求める\n","mu = np.mean(Grade_data['Mathematics'])\n","sigma = np.std(Grade_data['Mathematics'])\n","print(mu)\n","print(sigma)\n","\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['Mathematics']) # 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)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["71.7\n","14.913416778190035\n","信頼区間の下限: 60.45450247142524\n","信頼区間の上限: 82.94549752857476\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":301},"id":"wt2nRQWnfFvV","executionInfo":{"status":"ok","timestamp":1632979100576,"user_tz":-540,"elapsed":293,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"da209279-93db-4f7d-a32f-168640f98453"},"source":["######### 練習５ ###########\n","# iris data 読み込み\n","iris=pd.read_csv(data_dir + 'iris_2metrics1.csv')\n","\n","# 可視化 : histgram\n","plt.hist(iris['sepal-length'], color='blue', alpha=0.5)\n","plt.hist(iris['sepal-width'], color='green', alpha=0.5)\n","plt.show()\n","\n","# 対応なしt検定\n","from scipy import stats\n","\n","t, p = stats.ttest_ind(iris['sepal-length'], \n","                       iris['sepal-width'], \n","                       equal_var=False)\n","print(t)\n","print(p)"],"execution_count":13,"outputs":[{"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":"stream","name":"stdout","text":["36.548157693982006\n","1.3503535965078185e-96\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":797},"id":"I38FbTV3fPIt","executionInfo":{"status":"ok","timestamp":1632979925163,"user_tz":-540,"elapsed":776,"user":{"displayName":"Kengo Araki","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11929750928833228227"}},"outputId":"aca8b00e-1f76-4c38-947e-084b23268e87"},"source":["######### 練習６ ###########\n","# アメリカの都市ごとの女性の労働率の平均の差に対する検定\n","# data読み込み\n","laborfor=pd.read_csv(data_dir + 'laborfor.csv')\n","\n","# 可視化 : histgram\n","plt.hist(laborfor['1972'], color='blue', alpha=0.5)\n","plt.hist(laborfor['1968'], color='green', alpha=0.5)\n","plt.show()\n","plt.hist(laborfor['1972']-laborfor['1968'])\n","plt.show()\n","\n","# 平均と分散を計算\n","mu = np.mean(laborfor['1972']-laborfor['1968'])\n","var = np.var(laborfor['1972']-laborfor['1968'])\n","# 正規化ヒストグラムを表示 (binの度数の総和が1になるわけではないことに注意)\n","plt.hist(laborfor['1972']-laborfor['1968'], color='green', density=True)\n","# -0.12から0.16まで0.01刻みの数字が入った配列\n","x = np.arange(-0.12,0.16,0.01)\n","plt.plot(x,stats.norm.pdf(x,loc=mu,scale=np.sqrt(var)),\n","         lw=3,color=\"r\")\n","plt.show()\n","\n","# 対応あり t検定\n","t, p = stats.ttest_rel(laborfor['1972'], laborfor['1968'])\n","print(t)\n","print(p)"],"execution_count":22,"outputs":[{"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 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["2.457703815601802\n","0.024352597586836344\n"]}]}]}