{"id":3344,"date":"2023-07-17T23:22:52","date_gmt":"2023-07-17T23:22:52","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90python\/"},"modified":"2023-07-17T23:22:52","modified_gmt":"2023-07-17T23:22:52","slug":"%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e4%b8%89%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90python\/","title":{"rendered":"Python \u3067\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001<\/strong> 3 \u3064\u306e\u56e0\u5b50\u306b\u5206\u6563\u3055\u308c\u305f 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001Python \u3067 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: Python \u3067\u306e 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u7814\u7a76\u8005\u304c\u30012 \u3064\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u304c\u5927\u5b66\u306e\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u9593\u3067\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306e\u5e73\u5747\u9ad8\u3055\u306e\u7570\u306a\u308b\u5411\u4e0a\u306b\u3064\u306a\u304c\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7814\u7a76\u8005\u306f\u3001\u6027\u5225\u3068\u90e8\u9580 (\u90e8\u9580 I \u307e\u305f\u306f II) \u3082\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u8003\u3048\u3066\u304a\u308a\u3001\u3053\u308c\u3089\u306e\u8981\u56e0\u306b\u95a2\u3059\u308b\u30c7\u30fc\u30bf\u3082\u53ce\u96c6\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f7c\u306e\u76ee\u6a19\u306f\u3001\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u304c\u30b8\u30e3\u30f3\u30d7\u306e\u9ad8\u3055\u306b\u3069\u306e\u3088\u3046\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u5224\u65ad\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">Python \u3067\u3053\u306e 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001\u6b21\u306e\u624b\u9806\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u30c7\u30fc\u30bf\u3092\u4fdd\u6301\u3059\u308b\u30d1\u30f3\u30c0 DataFrame \u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/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>\u30b9\u30c6\u30c3\u30d7 2: \u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <strong>statsmodels<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e<strong>anova_lm()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e09\u5143\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/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>\u30b9\u30c6\u30c3\u30d7 3: \u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>Pr(&gt;F)<\/strong>\u5217\u306b\u306f\u3001\u500b\u3005\u306e\u56e0\u5b50\u306e p \u5024\u3068\u56e0\u5b50\u9593\u306e\u4ea4\u4e92\u4f5c\u7528\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u30013 \u3064\u306e\u56e0\u5b50\u9593\u306e\u76f8\u4e92\u4f5c\u7528\u306f\u3069\u308c\u3082\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u306f\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u30013 \u3064\u306e\u8981\u7d20 (\u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580) \u306e\u305d\u308c\u305e\u308c\u304c\u3001\u6b21\u306e p \u5024\u3067\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u3053\u3068\u3082\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u30d7\u30ed\u30b0\u30e9\u30e0<\/strong>P \u5024: 0.00000000298<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u6027\u5225<\/strong>P \u5024: 0.00000000000171<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5206\u5272<\/strong>P \u5024: 0.00000185<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7d50\u8ad6\u3068\u3057\u3066\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30b0\u30e9\u30e0\u3001\u6027\u5225\u3001\u90e8\u9580\u306f\u3059\u3079\u3066\u3001\u9078\u624b\u306e\u30b8\u30e3\u30f3\u30d7\u9ad8\u3055\u306e\u5411\u4e0a\u3092\u793a\u3059\u91cd\u8981\u306a\u6307\u6a19\u3067\u3042\u308b\u3068\u8a00\u3048\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u3053\u308c\u3089 3 \u3064\u306e\u8981\u7d20\u306e\u9593\u306b\u306f\u6709\u610f\u306a\u76f8\u4e92\u4f5c\u7528\u52b9\u679c\u306f\u306a\u3044\u3068\u8a00\u3048\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4ed6\u306e ANOVA \u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u4e00\u65b9\u5411\u5206\u6563\u5206\u6790python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python-anova\u53cc\u65b9\u5411\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u7e70\u308a\u8fd4\u3057\u6e2c\u5b9a-anova-python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u53cd\u5fa9\u6e2c\u5b9a ANOVA \u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001 3 \u3064\u306e\u56e0\u5b50\u306b\u5206\u6563\u3055\u308c\u305f 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002 \u6b21\u306e\u4f8b\u306f\u3001Python \u3067 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-3344","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 3 \u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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