12.7.1.ANOVA (Analysis of Variance).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "import scipy.stats\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TypeA_before</th>\n",
       "      <th>TypeA_after</th>\n",
       "      <th>TypeB_before</th>\n",
       "      <th>TypeB_after</th>\n",
       "      <th>TypeC_before</th>\n",
       "      <th>TypeC_after</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277</td>\n",
       "      <td>264</td>\n",
       "      <td>265</td>\n",
       "      <td>268</td>\n",
       "      <td>267</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>269</td>\n",
       "      <td>261</td>\n",
       "      <td>264</td>\n",
       "      <td>267</td>\n",
       "      <td>264</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>263</td>\n",
       "      <td>267</td>\n",
       "      <td>269</td>\n",
       "      <td>272</td>\n",
       "      <td>283</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>266</td>\n",
       "      <td>272</td>\n",
       "      <td>274</td>\n",
       "      <td>277</td>\n",
       "      <td>287</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>262</td>\n",
       "      <td>258</td>\n",
       "      <td>261</td>\n",
       "      <td>264</td>\n",
       "      <td>275</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TypeA_before  TypeA_after  TypeB_before  TypeB_after  TypeC_before  \\\n",
       "0           277          264           265          268           267   \n",
       "1           269          261           264          267           264   \n",
       "2           263          267           269          272           283   \n",
       "3           266          272           274          277           287   \n",
       "4           262          258           261          264           275   \n",
       "\n",
       "   TypeC_after  \n",
       "0          268  \n",
       "1          269  \n",
       "2          283  \n",
       "3          266  \n",
       "4          266  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/sewonghwang/golf-ball-testing\n",
    "df = pd.read_csv(\"datasets/Golf_test.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "일원분산분석 결과 : F=4.2, p=0.01652\n"
     ]
    }
   ],
   "source": [
    "# stats 패키지 아노바 검정\n",
    "\n",
    "F_statistic, pVal = stats.f_oneway(df['TypeA_before'], \n",
    "                                   df['TypeB_before'], \n",
    "                                   df['TypeC_before'])\n",
    "\n",
    "print('일원분산분석 결과 : F={0:.1f}, p={1:.5f}'.format(F_statistic, pVal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TypeA_before</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TypeA_before</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TypeA_before</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TypeA_before</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TypeA_before</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       variable  value\n",
       "0  TypeA_before    277\n",
       "1  TypeA_before    269\n",
       "2  TypeA_before    263\n",
       "3  TypeA_before    266\n",
       "4  TypeA_before    262"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 재구조화\n",
    "\n",
    "df2 =  pd.melt(df)\n",
    "df2 = df2[df2['variable'].isin(['TypeA_before', 'TypeB_before', 'TypeC_before'])]\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                df        sum_sq     mean_sq         F    PR(>F)\n",
      "C(variable)    2.0    675.453333  337.726667  4.220169  0.016515\n",
      "Residual     147.0  11763.940000   80.026803       NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "# ols 패키지 아노바 검정\n",
    "\n",
    "model = ols('value ~ C(variable)', df2).fit()\n",
    "print(anova_lm(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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",
      "TypeA_before TypeB_before     5.14 0.0129  0.9035 9.3765   True\n",
      "TypeA_before TypeC_before      1.9 0.5374 -2.3365 6.1365  False\n",
      "TypeB_before TypeC_before    -3.24 0.1698 -7.4765 0.9965  False\n",
      "---------------------------------------------------------------\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사후검정\n",
    "\n",
    "posthoc = pairwise_tukeyhsd(df2['value'], \n",
    "                            df2['variable'], \n",
    "                            alpha=0.05)\n",
    "print(posthoc)\n",
    "fig = posthoc.plot_simultaneous()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}