{"id":948,"date":"2023-07-28T05:19:18","date_gmt":"2023-07-28T05:19:18","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%82%af%e3%82%99%e3%83%a9%e3%83%95%e3%82%99%e3%82%b9%e3%83%86%e3%82%b9%e3%83%88python\/"},"modified":"2023-07-28T05:19:18","modified_gmt":"2023-07-28T05:19:18","slug":"%e3%82%af%e3%82%99%e3%83%a9%e3%83%95%e3%82%99%e3%82%b9%e3%83%86%e3%82%b9%e3%83%88python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%82%af%e3%82%99%e3%83%a9%e3%83%95%e3%82%99%e3%82%b9%e3%83%86%e3%82%b9%e3%83%88python\/","title":{"rendered":"Python \u3067 grubbs \u306e\u30c6\u30b9\u30bf\u30fc\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u30b0\u30e9\u30d6\u30b9 \u30c6\u30b9\u30c8\u306f\u3001<\/strong>\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u5185\u306e\u5916\u308c\u5024\u306e\u5b58\u5728\u3092\u7279\u5b9a\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u691c\u5b9a\u3092\u4f7f\u7528\u3059\u308b\u306b\u306f\u3001\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u304c\u307b\u307c\u6b63\u898f\u5206\u5e03\u3057\u3066\u304a\u308a\u3001\u5c11\u306a\u304f\u3068\u3082 7 \u3064\u306e\u89b3\u6e2c\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Python \u3067\u306e\u30b0\u30e9\u30d6\u30b9 \u30c6\u30b9\u30c8<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">Python \u3067 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001 <a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e smirnov_grubbs() \u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>smirnov_grubbs.test (\u30c7\u30fc\u30bf\u3001\u30a2\u30eb\u30d5\u30a1 = 0.05)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>data:<\/strong>\u30c7\u30fc\u30bf\u5024\u306e\u6570\u5024\u30d9\u30af\u30c8\u30eb<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>alpha:<\/strong>\u691c\u5b9a\u306b\u4f7f\u7528\u3059\u308b\u6709\u610f\u6c34\u6e96\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f 0.05 \u3067\u3059<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u6a5f\u80fd\u3092\u4f7f\u7528\u3059\u308b\u306b\u306f\u3001\u307e\u305a<a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>pip install outlier_utils\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3068\u3001Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002\u6b21\u306e\u4f8b\u306f\u3001\u3053\u308c\u3092\u884c\u3046\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: \u4e21\u5074\u30b0\u30e9\u30d6\u30b9\u691c\u5b9a<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u306e\u4e21\u7aef\u3067\u5916\u308c\u5024\u3092\u691c\u51fa\u3059\u308b\u4e21\u5074 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\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: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test<\/span>\ngrubbs. <span style=\"color: #3366ff;\">test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29])\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u3001\u5916\u308c\u5024\u3092\u542b\u307e\u306a\u3044\u5358\u7d14\u306a\u914d\u5217\u3092\u8fd4\u3057\u307e\u3059\u3002\u3053\u306e\u5834\u5408\u3001\u6700\u5927\u5024 40 \u306f\u5916\u308c\u5024\u3067\u3042\u308b\u305f\u3081\u3001\u524a\u9664\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: \u7247\u5074 Grubbs \u30c6\u30b9\u30c8<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u5185\u306e\u6700\u5c0f\u5024\u3068\u6700\u5927\u5024\u306b\u5bfe\u3057\u3066\u7247\u5074 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\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: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test to see if minimum value is an outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">min_test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test to see if minimum value is an outlier\n<\/span>grubbs. <span style=\"color: #3366ff;\">max_test<\/span> (data, alpha=.05)\n\narray([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29])\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u5c0f\u5916\u308c\u5024\u30c6\u30b9\u30c8\u3067\u306f\u3001\u6700\u5c0f\u5024\u304c\u5916\u308c\u5024\u3068\u3057\u3066\u691c\u51fa\u3055\u308c\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u305f\u3060\u3057\u3001\u6700\u5927\u5916\u308c\u5024\u30c6\u30b9\u30c8\u3067\u306f\u6700\u5927\u5024 40 \u304c\u5916\u308c\u5024\u3067\u3042\u308b\u3068\u5224\u65ad\u3055\u308c\u305f\u305f\u3081\u3001\u524a\u9664\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 3: \u5916\u308c\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u62bd\u51fa\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5916\u308c\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\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: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test and identify index (if any) of the outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">max_test_indices<\/span> (data, alpha=.05)\n\n[16]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u30c6\u30fc\u30d6\u30eb\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4f4d\u7f6e 16 \u306b\u5916\u308c\u5024\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 4: \u5916\u308c\u5024\u304b\u3089\u5024\u3092\u62bd\u51fa\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5916\u308c\u5024\u304b\u3089\u5024\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\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: #008000;\">from<\/span> outliers <span style=\"color: #008000;\">import<\/span> smirnov_grubbs <span style=\"color: #008000;\">as<\/span> grubbs\n\n<span style=\"color: #008080;\">#define data<\/span>\ndata = np.array([5, 14, 15, 15, 14, 19, 17, 16, 20, 22, 8, 21, 28, 11, 9, 29, 40])\n\n<span style=\"color: #008080;\">#perform Grubbs' test and identify the actual value (if any) of the outlier<\/span>\ngrubbs. <span style=\"color: #3366ff;\">max_test_outliers<\/span> (data, alpha=.05)\n\n[40]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u5024 40 \u306e\u5916\u308c\u5024\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><strong>\u5916\u308c\u5024\u3092\u51e6\u7406\u3059\u308b\u65b9\u6cd5<\/strong><\/h3>\n<p><span style=\"color: #000000;\">Grubbs \u30c6\u30b9\u30c8\u3067\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u5185\u306e\u5916\u308c\u5024\u304c\u7279\u5b9a\u3055\u308c\u305f\u5834\u5408\u3001\u3044\u304f\u3064\u304b\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u5024\u304c\u30bf\u30a4\u30d7\u30df\u30b9\u3084\u30c7\u30fc\u30bf\u5165\u529b\u30a8\u30e9\u30fc\u3067\u306f\u306a\u3044\u3053\u3068\u3092\u518d\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u3067\u5916\u308c\u5024\u3068\u3057\u3066\u8868\u793a\u3055\u308c\u308b\u5024\u306f\u3001\u30c7\u30fc\u30bf\u5165\u529b\u6642\u306b\u500b\u4eba\u304c\u884c\u3063\u305f\u5358\u306a\u308b\u30bf\u30a4\u30d7\u30df\u30b9\u3067\u3042\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u307e\u305a\u3001\u3055\u3089\u306a\u308b\u6c7a\u5b9a\u3092\u4e0b\u3059\u524d\u306b\u3001\u5024\u304c\u6b63\u3057\u304f\u5165\u529b\u3055\u308c\u305f\u3053\u3068\u3092\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u5916\u308c\u5024\u306b\u65b0\u3057\u3044\u5024\u3092\u5272\u308a\u5f53\u3066\u307e\u3059<\/strong>\u3002<\/span>\u5916\u308c\u5024\u304c\u30bf\u30a4\u30d7\u30df\u30b9\u307e\u305f\u306f\u30c7\u30fc\u30bf\u5165\u529b\u30a8\u30e9\u30fc\u306e\u7d50\u679c\u3067\u3042\u308b\u3053\u3068\u304c\u5224\u660e\u3057\u305f\u5834\u5408\u306f\u3001<span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e<\/span>\u5e73\u5747<a href=\"https:\/\/statorials.org\/ja\/\u4e2d\u5fc3\u50be\u5411\u3092\u6e2c\u5b9a\u3059\u308b\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u3084\u4e2d\u592e\u5024<\/a><span style=\"color: #000000;\">\u306a\u3069\u306e\u65b0\u3057\u3044\u5024\u3092\u5272\u308a\u5f53\u3066\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059<\/span>\u3002<\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \u5916\u308c\u5024\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/strong>\u5024\u304c\u672c\u5f53\u306b\u5916\u308c\u5024\u3067\u3042\u308a\u3001\u5206\u6790\u306b\u91cd\u5927\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u5834\u5408\u306f\u3001\u305d\u306e\u5024\u3092\u524a\u9664\u3059\u308b\u3053\u3068\u3092\u9078\u629e\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30b0\u30e9\u30d6\u30b9 \u30c6\u30b9\u30c8\u306f\u3001\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u5185\u306e\u5916\u308c\u5024\u306e\u5b58\u5728\u3092\u7279\u5b9a\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u691c\u5b9a\u3092\u4f7f\u7528\u3059\u308b\u306b\u306f\u3001\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u304c\u307b\u307c\u6b63\u898f\u5206\u5e03\u3057\u3066\u304a\u308a\u3001\u5c11\u306a\u304f\u3068\u3082 7 \u3064\u306e\u89b3\u6e2c\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067 [&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-948","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 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - Statology<\/title>\n<meta name=\"description\" content=\"Python \u3067 Grubbs \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u3066\u5916\u308c\u5024\u3092\u691c\u51fa\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u7c21\u5358\u306b\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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