{"id":852,"date":"2023-07-28T13:03:16","date_gmt":"2023-07-28T13:03:16","guid":{"rendered":"https:\/\/statorials.org\/ja\/python-anova%e5%8f%8c%e6%96%b9%e5%90%91\/"},"modified":"2023-07-28T13:03:16","modified_gmt":"2023-07-28T13:03:16","slug":"python-anova%e5%8f%8c%e6%96%b9%e5%90%91","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python-anova%e5%8f%8c%e6%96%b9%e5%90%91\/","title":{"rendered":"Python \u3067\u4e8c\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;\"><a href=\"https:\/\/statorials.org\/ja\/\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001<\/a> 2 \u3064\u306e\u56e0\u5b50\u306b\u5206\u5272\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;\">\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u76ee\u7684\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b 2 \u3064\u306e\u56e0\u5b50\u306e\u5f71\u97ff\u3092\u5224\u65ad\u3057\u3001\u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b 2 \u3064\u306e\u56e0\u5b50\u9593\u306b\u4ea4\u4e92\u4f5c\u7528\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: Python \u3067\u306e\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u690d\u7269\u5b66\u8005\u306f\u3001\u690d\u7269\u306e\u6210\u9577\u304c\u65e5\u5149\u3078\u306e\u66dd\u9732\u3084\u6c34\u3084\u308a\u306e\u983b\u5ea6\u306b\u3088\u3063\u3066\u5f71\u97ff\u3092\u53d7\u3051\u308b\u304b\u3069\u3046\u304b\u3092\u77e5\u308a\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u307e\u3059\u3002\u5f7c\u5973\u306f 30 \u500b\u306e\u7a2e\u3092\u690d\u3048\u3001\u65e5\u5149\u3078\u306e\u66dd\u9732\u3084\u6c34\u3084\u308a\u306e\u983b\u5ea6\u306a\u3069\u306e\u3055\u307e\u3056\u307e\u306a\u6761\u4ef6\u3067 2 \u304b\u6708\u9593\u6210\u9577\u3055\u305b\u307e\u3059\u3002 2 \u304b\u6708\u5f8c\u3001\u5f7c\u5973\u306f\u5404\u690d\u7269\u306e\u9ad8\u3055\u3092\u30a4\u30f3\u30c1\u5358\u4f4d\u3067\u8a18\u9332\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u624b\u9806\u3092\u4f7f\u7528\u3057\u3066\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3001\u6c34\u3084\u308a\u306e\u983b\u5ea6\u3068\u65e5\u5149\u3078\u306e\u66dd\u9732\u304c\u690d\u7269\u306e\u6210\u9577\u306b\u91cd\u5927\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3069\u3046\u304b\u3001\u307e\u305f\u6c34\u3084\u308a\u306e\u983b\u5ea6\u3068\u592a\u967d\u5149\u3078\u306e\u66dd\u9732\u306e\u9593\u306b\u76f8\u4e92\u4f5c\u7528\u52b9\u679c\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u307e\u3059\u3002\u6c34\u3084\u308a\u3068\u592a\u967d\u306b\u3055\u3089\u3059\u3053\u3068\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u5165\u529b\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u6b21\u306e 3 \u3064\u306e\u5909\u6570\u3092\u542b\u3080 pandas DataFrame \u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6c34:<\/strong>\u5404\u690d\u7269\u306b\u6c34\u3092\u3084\u308b\u983b\u5ea6: \u6bce\u65e5\u307e\u305f\u306f\u6bce\u9031<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u592a\u967d:<\/strong>\u5404\u690d\u7269\u304c\u53d7\u3051\u308b\u592a\u967d\u5149\u306e\u91cf: \u4f4e\u3001\u4e2d\u3001\u307e\u305f\u306f\u9ad8<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u9ad8\u3055:<\/strong> 2 \u304b\u6708\u5f8c\u306e\u5404\u690d\u7269\u306e\u9ad8\u3055 (\u30a4\u30f3\u30c1)<\/span><\/li>\n<\/ul>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create data<\/span>\ndf = pd.DataFrame({'water': np.repeat(['daily', 'weekly'], 15),\n                   'sun': np.tile(np.repeat(['low', 'med', 'high'], 5), 2),\n                   'height': [6, 6, 6, 5, 6, 5, 5, 6, 4, 5,\n                              6, 6, 7, 8, 7, 3, 4, 4, 4, 5,\n                              4, 4, 4, 4, 4, 5, 6, 6, 7, 8]})\n\n<span style=\"color: #008080;\">#view first ten rows of data<\/span> \ndf[:10]\n\n\twater sun height\n0 daily low 6\n1 daily low 6\n2 daily low 6\n3 daily low 5\n4 daily low 6\n5 daily med 5\n6 daily med 5\n7 daily med 6\n8 daily med 4\n9 daily med 5\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001statsmodels \u30e9\u30a4\u30d6\u30e9\u30ea\u306e<a href=\"https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.stats.anova.anova_lm.html\" target=\"_blank\" rel=\"noopener\">anova_lm() \u95a2\u6570<\/a>\u3092\u4f7f\u7528\u3057\u3066\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> statsmodels.api <span style=\"color: #107d3f;\">as<\/span> sm\n<span style=\"color: #107d3f;\">from<\/span> statsmodels.formula.api <span style=\"color: #107d3f;\">import<\/span> ols\n\n<span style=\"color: #008080;\">#perform two-way ANOVA<\/span>\nmodel = ols('height ~ C(water) + C(sun) + C(water):C(sun)', data=df).fit()\nsm.stats.anova_lm(model, typ=2)\n\n\t           sum_sq df F PR(&gt;F)\nC(water) 8.533333 1.0 16.0000 0.000527\nC(sun) 24.866667 2.0 23.3125 0.000002\nC(water):C(sun) 2.466667 2.0 2.3125 0.120667\nResidual 12.800000 24.0 NaN NaN\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u7d50\u679c\u3092\u89e3\u91c8\u3057\u307e\u3059\u3002<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u8868\u5185\u306e\u5404\u56e0\u5b50\u306e\u6b21\u306e p \u5024\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6c34:<\/strong> p \u5024 = 0.000527<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u592a\u967d:<\/strong> p \u5024 = 0.0000002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u6c34*\u592a\u967d:<\/strong> p \u5024 = 0.120667<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6c34\u3068\u592a\u967d\u306e p \u5024\u306f\u4e21\u65b9\u3068\u3082 0.05 \u672a\u6e80\u3067\u3042\u308b\u305f\u3081\u3001\u3053\u308c\u306f\u4e21\u65b9\u306e\u8981\u56e0\u304c\u8349\u4e08\u306b\u5bfe\u3057\u3066\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5f71\u97ff\u3092\u53ca\u307c\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u76f8\u4e92\u4f5c\u7528\u52b9\u679c\u306e p \u5024 (0.120667) \u306f 0.05 \u672a\u6e80\u3067\u306f\u306a\u3044\u305f\u3081\u3001\u65e5\u5149\u3078\u306e\u66dd\u9732\u3068\u6c34\u3084\u308a\u306e\u983b\u5ea6\u306e\u9593\u306b\u6709\u610f\u306a\u76f8\u4e92\u4f5c\u7528\u52b9\u679c\u304c\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8:<\/strong> ANOVA \u306e\u7d50\u679c\u306f\u3001\u6c34\u3084\u308a\u306e\u983b\u5ea6\u3068\u65e5\u5149\u3078\u306e\u66dd\u9732\u304c\u8349\u4e08\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u3055\u307e\u3056\u307e\u306a\u30ec\u30d9\u30eb\u306e\u6c34\u3068\u65e5\u5149\u304c\u8349\u4e08\u306b\u3069\u306e\u3088\u3046\u306b\u5f71\u97ff\u3059\u308b\u304b\u3092\u6b63\u78ba\u306b\u5224\u65ad\u3059\u308b\u306b\u306f<a href=\"https:\/\/statorials.org\/ja\/\u4e8b\u5f8c\u5206\u6563\u5206\u6790\u30c6\u30b9\u30c8\/\" target=\"_blank\" rel=\"noopener\">\u3001\u4e8b\u5f8c\u30c6\u30b9\u30c8<\/a>\u3092\u5b9f\u884c\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\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\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\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\/\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u4e09\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001 2 \u3064\u306e\u56e0\u5b50\u306b\u5206\u5272\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 \u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u76ee\u7684\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b 2 \u3064\u306e\u56e0\u5b50\u306e\u5f71\u97ff\u3092\u5224\u65ad\u3057\u3001 [&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-852","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 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