pre_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xlrd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./Data/ASCII-comma/CH14/ex14-5.txt\",\",\").reset_index(drop=True)\n",
    "data = df.set_index('Worker')\n",
    "data = data.stack()\n",
    "data.index = data.index.rename('Plan', level=1)\n",
    "data.name = 'Productivity'\n",
    "data = data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.a\n",
    "# \\(H_{0}:\\) All incentive plan yield are having equal productivity.\n",
    "# \\(H_{a}:\\) At least one incentive plan yields differing productivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list all the 'plan's\n",
    "a = df.T.loc['A']\n",
    "b = df.T.loc['B']\n",
    "c = df.T.loc['C']\n",
    "d = df.T.loc['D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱型图\n",
    "sns.set(style=\"ticks\", palette=\"pastel\")\n",
    "\n",
    "sns.boxplot(x=\"Plan\", y=\"Productivity\",\n",
    "             palette=[\"m\", \"g\"],\n",
    "            data=data)\n",
    "sns.despine(offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "from scipy import stats\n",
    "stats.probplot(data['Productivity'], dist=\"norm\", plot=pylab) # qq-plot\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l # > 50, 不适合用shap, < 300, 不适合用 ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8754165172576904, 0.014648374170064926)\n",
      "(0.9121683239936829, 0.07010561227798462)\n",
      "(0.7844040393829346, 0.0005103913135826588)\n",
      "(0.9617589116096497, 0.5795571804046631)\n",
      "KstestResult(statistic=1.0, pvalue=0.0)\n",
      "(0.23561738271242805, 0.004894712459462941)\n",
      "(0.15998327393565404, 0.20223034130881642)\n",
      "(0.24423866376526598, 0.0028592491848186525)\n",
      "(0.12458838423274854, 0.5613769381604545)\n"
     ]
    }
   ],
   "source": [
    "print(stats.shapiro(a)) # shapiro 检验 仅供参考\n",
    "print(stats.shapiro(b))\n",
    "print(stats.shapiro(c)) \n",
    "print(stats.shapiro(d)) \n",
    "print(stats.kstest(data['Productivity'], 'norm')) # k-s 检验 仅供参考\n",
    "\n",
    "\n",
    "from statsmodels.stats.diagnostic import lilliefors\n",
    "print(lilliefors(a)) # lilliefors 检验，部分不正态, so transfer\n",
    "print(lilliefors(b))\n",
    "print(lilliefors(c))\n",
    "print(lilliefors(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=2.0619612832681455, pvalue=0.11230932572459755)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.levene(a,b,c,d,center='median') # 方差齐次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transfer\n",
    "x_most_normal, lmbda_optimal = stats.boxcox(data['Productivity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.45754909812146655"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmbda_optimal # ～ -0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9036062359809875, 0.048209335654973984)\n",
      "(0.9325559139251709, 0.17291471362113953)\n",
      "(0.903323233127594, 0.047620438039302826)\n",
      "(0.9757245779037476, 0.8679212927818298)\n",
      "KstestResult(statistic=0.5132882362454201, pvalue=2.0385013475828667e-05)\n",
      "KstestResult(statistic=0.5129821731666143, pvalue=2.0682924717500763e-05)\n",
      "KstestResult(statistic=0.5104146737720318, pvalue=2.334935614901855e-05)\n",
      "KstestResult(statistic=0.513656855069704, pvalue=2.0031600341401467e-05)\n",
      "(0.1948535105315502, 0.04508151672244269)\n",
      "(0.13891915514687858, 0.39579788797958765)\n",
      "(0.18515279686157915, 0.07071412290428916)\n",
      "(0.08533160140148943, 0.9609490944999963)\n"
     ]
    }
   ],
   "source": [
    "print(stats.shapiro(a**(-0.5))) # shapiro 检验 仅供参考\n",
    "print(stats.shapiro(b**(-0.5)))\n",
    "print(stats.shapiro(c**(-0.5)))\n",
    "print(stats.shapiro(d**(-0.5)))\n",
    "print(stats.kstest(a**(-0.5), 'norm')) # k-s 检验 仅供参考\n",
    "print(stats.kstest(b**(-0.5), 'norm'))\n",
    "print(stats.kstest(c**(-0.5), 'norm'))\n",
    "print(stats.kstest(d**(-0.5), 'norm'))\n",
    "\n",
    "from statsmodels.stats.diagnostic import lilliefors\n",
    "print(lilliefors(a**(-0.5))) # lilliefors 检验，依然会出现非正态\n",
    "print(lilliefors(b**(-0.5)))\n",
    "print(lilliefors(c**(-0.5)))\n",
    "print(lilliefors(d**(-0.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=4.459544819937905, pvalue=0.006123299424499501)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.levene(a**(-0.5),b**(-0.5),c**(-0.5),d**(-0.5),center='median') # 如果变换，则方差不齐次，于是考虑 k-w test"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "transfer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept       514.05\n",
      "C(Plan)[T.B]     43.20\n",
      "C(Plan)[T.C]    114.25\n",
      "C(Plan)[T.D]    135.70\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "model_str_0 = (('Productivity ~ C(Plan)'))\n",
    "model_0 = ols(model_str_0, data=data).fit()\n",
    "print(model.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(Plan)</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.369912e+05</td>\n",
       "      <td>78997.079167</td>\n",
       "      <td>2.02039</td>\n",
       "      <td>0.118133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>76.0</td>\n",
       "      <td>2.971587e+06</td>\n",
       "      <td>39099.824342</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df        sum_sq       mean_sq        F    PR(>F)\n",
       "C(Plan)    3.0  2.369912e+05  78997.079167  2.02039  0.118133\n",
       "Residual  76.0  2.971587e+06  39099.824342                   "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 又由于anova 在方差齐次的条件下有一定的容忍度，所以使用原数据：\n",
    "anova_table_0 = sm.stats.anova_lm(model_0, typ=1).replace(np.nan, '', regex=True)\n",
    "anova_table_0 "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "通过anova，得p-value = 0.118 > 0.05, 没有足够证据表明与四个激励计划相关的平均产出不同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=9.305435394246341, pvalue=0.025493876172639596)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.kruskal(a,b,c,d) # 变换前后的数据不会对 k-w 有影响，所以使用原数据，p-value 《 0.05 ，由于 k-w 更加保守，所以认为正态性超过了 anova的容忍范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 方差齐次\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>   <th>lower</th>     <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>A</td>      <td>B</td>     <td>43.2</td>   <td>0.8969</td> <td>-121.0588</td> <td>207.4588</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>A</td>      <td>C</td>    <td>114.25</td>  <td>0.2689</td> <td>-50.0088</td>  <td>278.5088</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>A</td>      <td>D</td>     <td>135.7</td>  <td>0.1409</td> <td>-28.5588</td>  <td>299.9588</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>B</td>      <td>C</td>     <td>71.05</td>  <td>0.6494</td> <td>-93.2088</td>  <td>235.3088</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>B</td>      <td>D</td>     <td>92.5</td>   <td>0.4562</td> <td>-71.7588</td>  <td>256.7588</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>C</td>      <td>D</td>     <td>21.45</td>    <td>0.9</td>  <td>-142.8088</td> <td>185.7088</td>  <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于方差齐次，且 tukey 不要求正态性\n",
    "# tukey w\n",
    "from statsmodels.stats.multicomp import (pairwise_tukeyhsd,\n",
    "                                         MultiComparison)\n",
    "\n",
    "MultiComp = MultiComparison(data['Productivity'],\n",
    "                            data['Plan'])\n",
    "MultiComp.tukeyhsd().summary() # p-value 》 0。05， 所有pair的ci都包含0，所以没有配对在均值上有明显区别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.a\n",
    "# 这我们之前就已经讨论过了，方差齐次性和正态性检验无法同时满足，且正态性检验始终无法满足。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeveneResult(statistic=0.03465597431214312, pvalue=0.8533086355308221)\n",
      "LeveneResult(statistic=0.704731756031713, pvalue=0.4064488609249448)\n",
      "LeveneResult(statistic=4.42232000315996, pvalue=0.04214953428826169)\n"
     ]
    }
   ],
   "source": [
    "# 2.b\n",
    "# 由于正态性难以满足，考虑使用中位数评估的 bfl-test，考虑是 3组 与 control 组的方差区别，为了逻辑上的通顺，不使用四组同时进行 bfl 判断，而是三个组轮流和 control 比较：\n",
    "print(scipy.stats.levene(a,b,center='median')) # 方差齐次\n",
    "print(scipy.stats.levene(a,c,center='median'))\n",
    "print(scipy.stats.levene(a,d,center='median')) # a-d 有显著性差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.c 题目没看懂，本来考虑是做独立性检验，起初尝试做整体的正态性检验，然后发现这个论证方法没有道理，后来觉察到自己根本无法做独立性检验，所以在这里讲题目视作一个文字题：\n",
    "# 答：因为这是同一生产线上的同一个工位，做着完全一样的事，在这种场景下进行实验，大家的生产力没有道理会互相影响。\n",
    "# 2.d\n",
    "# 之前我们已经解决了这个问题， 使用 Kruskal-Wallis 检验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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