{"id":3346,"date":"2023-07-17T23:22:52","date_gmt":"2023-07-17T23:22:52","guid":{"rendered":"https:\/\/statorials.org\/ko\/3%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc-%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-17T23:22:52","modified_gmt":"2023-07-17T23:22:52","slug":"3%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc-%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/3%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc-%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/","title":{"rendered":"Python\uc5d0\uc11c 3\uc6d0 anova\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>3\uc6d0 ANOVA\ub294<\/strong> \uc138 \uac00\uc9c0 \uc694\uc778\uc5d0 \uac78\uccd0 \ubd84\ud3ec\ub41c \uc138 \uac1c \uc774\uc0c1\uc758 \ub3c5\ub9bd \uadf8\ub8f9\uc758 \ud3c9\uade0 \uac04\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \ucc28\uc774\uac00 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 3\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\uc608: Python\uc758 3\uc6d0 \ubd84\uc0b0\ubd84\uc11d<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uc5f0\uad6c\uc790\uac00 \ub450 \uac00\uc9c0 \ud6c8\ub828 \ud504\ub85c\uadf8\ub7a8\uc774 \ub300\ud559 \ub18d\uad6c \uc120\uc218\ub4e4 \uc0ac\uc774\uc5d0\uc11c \uc810\ud504 \ub192\uc774\uc758 \ud3c9\uade0 \ud5a5\uc0c1\uc73c\ub85c \uc774\uc5b4\uc9c0\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub824\uace0 \ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc5f0\uad6c\uc6d0\uc740 \uc131\ubcc4\uacfc \ubd80\ubb38(\ub514\ube44\uc804 I \ub610\ub294 II)\ub3c4 \uc810\ud504 \ub192\uc774\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\ub2e4\uace0 \uc758\uc2ec\ud558\uba70, \uc774\uac83\uc774 \uadf8\uac00 \uc774\ub7ec\ud55c \uc694\uc778\uc5d0 \ub300\ud55c \ub370\uc774\ud130\ub3c4 \uc218\uc9d1\ud558\ub294 \uc774\uc720\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\uc758 \ubaa9\ud45c\ub294 \ud6c8\ub828 \ud504\ub85c\uadf8\ub7a8, \uc131\ubcc4, \ubd80\ubb38\uc774 \uc810\ud504 \ub192\uc774\uc5d0 \uc5b4\ub5a4 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574 3\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Python\uc5d0\uc11c \uc774 3\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub824\uba74 \ub2e4\uc74c \ub2e8\uacc4\ub97c \ub530\ub974\uc138\uc694.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 \ub370\uc774\ud130\ub97c \ubcf4\uad00\ud560 pandas DataFrame\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">program<\/span> ': <span style=\"color: #3366ff;\">np.repeat<\/span> ([1,2],20),\n                   ' <span style=\"color: #ff0000;\">gender<\/span> ': np. <span style=\"color: #3366ff;\">tile<\/span> (np. <span style=\"color: #3366ff;\">repeat<\/span> (['M', 'F'], 10), 2),\n                   ' <span style=\"color: #ff0000;\">division<\/span> ': np. <span style=\"color: #3366ff;\">tile<\/span> (np. <span style=\"color: #3366ff;\">repeat<\/span> ([1, 2], 5), 4),\n                   ' <span style=\"color: #ff0000;\">height<\/span> ': [7, 7, 8, 8, 7, 6, 6, 5, 6, 5,\n                              5, 5, 4, 5, 4, 3, 3, 4, 3, 3,\n                              6, 6, 5, 4, 5, 4, 5, 4, 4, 3,\n                              2, 2, 1, 4, 4, 2, 1, 1, 2, 1]})\n\n<span style=\"color: #008080;\">#view first ten rows of DataFrame \n<\/span>df[:10]\n\n\tprogram gender division height\n0 1 M 1 7\n1 1 M 1 7\n2 1 M 1 8\n3 1 M 1 8\n4 1 M 1 7\n5 1 M 2 6\n6 1 M 2 6\n7 1 M 2 5\n8 1 M 2 6\n9 1 M 2 5\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: 3\uc6d0 \ubd84\uc0b0\ubd84\uc11d \uc218\ud589<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c <strong>statsmodels<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 <strong>anova_lm()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec 3\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n<span style=\"color: #008000;\">from<\/span> statsmodels. <span style=\"color: #3366ff;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">import<\/span> ols\n\n<span style=\"color: #008080;\">#perform three-way ANOVA\n<\/span>model = ols(\"\"\"height ~ C(program) + C(gender) + C(division) +\n               C(program):C(gender) + C(program):C(division) + C(gender):C(division) +\n               C(program):C(gender):C(division)\"\"\", data=df) <span style=\"color: #3366ff;\">.fit<\/span> ()\n\nsm. <span style=\"color: #3366ff;\">stats<\/span> . <span style=\"color: #3366ff;\">anova_lm<\/span> (model, typ= <span style=\"color: #008000;\">2<\/span> )\n\n\t                          sum_sq df F PR(&gt;F)\nC(program) 3.610000e+01 1.0 6.563636e+01 2.983934e-09\nC(gender) 6.760000e+01 1.0 1.229091e+02 1.714432e-12\nC(division) 1.960000e+01 1.0 3.563636e+01 1.185218e-06\nC(program):C(gender) 2.621672e-30 1.0 4.766677e-30 1.000000e+00\nC(program):C(division) 4.000000e-01 1.0 7.272727e-01 4.001069e-01\nC(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01\nC(program):C(gender):C(division) 1.000000e-01 1.0 1.818182e-01 6.726702e-01\nResidual 1.760000e+01 32.0 NaN NaN<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \uacb0\uacfc \ud574\uc11d<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Pr(&gt;F)<\/strong> \uc5f4\uc5d0\ub294 \uac01 \uac1c\ubcc4 \uc694\uc778\uc5d0 \ub300\ud55c p-\uac12\uacfc \uc694\uc778 \uac04\uc758 \uc0c1\ud638 \uc791\uc6a9\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uacb0\uacfc\ub97c \ubcf4\uba74, \uc138 \uac00\uc9c0 \uc694\uc778 \uac04\uc758 \uc0c1\ud638\uc791\uc6a9 \uc911 \uc5b4\ub290 \uac83\ub3c4 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud558\uc9c0 \uc54a\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \uc138 \uac00\uc9c0 \uc694\uc778(\ud504\ub85c\uadf8\ub7a8, \uc131\ubcc4, \ubd80\uc11c) \uac01\uac01\uc774 \ub2e4\uc74c p-\uac12\uc5d0\uc11c \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud558\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\ud504\ub85c\uadf8\ub7a8<\/strong> P-\uac12: 0.00000000298<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\uc131\ubcc4<\/strong> P-\uac12: 0.00000000000171<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\ubd84\ud560<\/strong> P \uac12: 0.00000185<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uacb0\ub860\uc801\uc73c\ub85c, \ud6c8\ub828 \ud504\ub85c\uadf8\ub7a8, \uc131\ubcc4, \ubd80\ubb38\uc740 \ubaa8\ub450 \uc120\uc218\uc758 \uc810\ud504 \ub192\uc774 \uc99d\uac00\ub97c \ub098\ud0c0\ub0b4\ub294 \uc911\uc694\ud55c \uc9c0\ud45c\ub77c\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \uc774 \uc138 \uac00\uc9c0 \uc694\uc18c \uc0ac\uc774\uc5d0\ub294 \uc720\uc758\ubbf8\ud55c \uc0c1\ud638\uc791\uc6a9 \ud6a8\uacfc\uac00 \uc5c6\ub2e4\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 ANOVA \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u11ab\u1107\u1161\u11bc\u1112\u1163\u11bc-\u110b\u1161\u1102\u1169\u1107\u1161-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \uc77c\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-anova-\u110b\u1163\u11bc\u1107\u1161\u11bc\u1112\u1163\u11bc\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \uc591\ubc29\ud5a5 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1107\u1161\u11ab\u1107\u1169\u11a8-\u110e\u1173\u11a8\u110c\u1165\u11bc-anova-python\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ubc18\ubcf5 \uce21\uc815 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>3\uc6d0 ANOVA\ub294 \uc138 \uac00\uc9c0 \uc694\uc778\uc5d0 \uac78\uccd0 \ubd84\ud3ec\ub41c \uc138 \uac1c \uc774\uc0c1\uc758 \ub3c5\ub9bd \uadf8\ub8f9\uc758 \ud3c9\uade0 \uac04\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \ucc28\uc774\uac00 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 3\uc6d0 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608: Python\uc758 3\uc6d0 \ubd84\uc0b0\ubd84\uc11d \uc5f0\uad6c\uc790\uac00 \ub450 \uac00\uc9c0 \ud6c8\ub828 \ud504\ub85c\uadf8\ub7a8\uc774 \ub300\ud559 \ub18d\uad6c \uc120\uc218\ub4e4 \uc0ac\uc774\uc5d0\uc11c \uc810\ud504 \ub192\uc774\uc758 \ud3c9\uade0 \ud5a5\uc0c1\uc73c\ub85c \uc774\uc5b4\uc9c0\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub824\uace0 \ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5f0\uad6c\uc6d0\uc740 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3346","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python\uc5d0\uc11c 3\uc6d0 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 3\uc6d0 ANOVA\ub97c 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