{"id":951,"date":"2023-07-28T05:19:18","date_gmt":"2023-07-28T05:19:18","guid":{"rendered":"https:\/\/statorials.org\/cn\/grubbs-%e6%b5%8b%e8%af%95-python\/"},"modified":"2023-07-28T05:19:18","modified_gmt":"2023-07-28T05:19:18","slug":"grubbs-%e6%b5%8b%e8%af%95-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/grubbs-%e6%b5%8b%e8%af%95-python\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u8fd0\u884c grubbs \u6d4b\u8bd5\u5668"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u683c\u62c9\u5e03\u65af\u68c0\u9a8c<\/strong>\u7528\u4e8e\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\u3002\u8981\u4f7f\u7528\u6b64\u68c0\u9a8c\uff0c\u6570\u636e\u96c6\u5fc5\u987b\u8fd1\u4f3c\u6b63\u6001\u5206\u5e03\u5e76\u4e14\u5305\u542b\u81f3\u5c11 7 \u4e2a\u89c2\u6d4b\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Grubbs \u6d4b\u8bd5\u3002<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Python \u4e2d\u7684 Grubbs \u6d4b\u8bd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u8981\u5728 Python \u4e2d\u6267\u884c Grubbs \u6d4b\u8bd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a>\u5305\u4e2d\u7684 smirnov_grubbs() \u51fd\u6570\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>smirnov_grubbs.test\uff08\u6570\u636e\uff0calpha = 0.05\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6570\u636e\uff1a<\/strong>\u6570\u636e\u503c\u7684\u6570\u503c\u5411\u91cf<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>alpha\uff1a<\/strong>\u7528\u4e8e\u6d4b\u8bd5\u7684\u663e\u7740\u6027\u6c34\u5e73\u3002\u9ed8\u8ba4\u503c\u4e3a 0.05<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8981\u4f7f\u7528\u6b64\u529f\u80fd\uff0c\u60a8\u5fc5\u987b\u9996\u5148\u5b89\u88c5<a href=\"https:\/\/pypi.org\/project\/outlier_utils\/\" target=\"_blank\" rel=\"noopener noreferrer\">outlier_utils<\/a>\u5305\uff1a<\/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;\">\u5b89\u88c5\u6b64\u8f6f\u4ef6\u5305\u540e\uff0c\u60a8\u53ef\u4ee5\u6267\u884c Grubbs \u6d4b\u8bd5\u3002\u4ee5\u4e0b\u793a\u4f8b\u8bf4\u660e\u4e86\u5982\u4f55\u6267\u884c\u6b64\u64cd\u4f5c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u53cc\u5c3e\u683c\u62c9\u5e03\u65af\u68c0\u9a8c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u8bf4\u660e\u4e86\u5982\u4f55\u6267\u884c\u53cc\u5c3e Grubbs \u6d4b\u8bd5\uff0c\u8be5\u6d4b\u8bd5\u5c06\u68c0\u6d4b\u6570\u636e\u96c6\u4e24\u7aef\u7684\u5f02\u5e38\u503c\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;\">\u8be5\u51fd\u6570\u4ec5\u8fd4\u56de\u4e00\u4e2a\u6ca1\u6709\u5f02\u5e38\u503c\u7684\u6570\u7ec4\u3002\u5728\u672c\u4f8b\u4e2d\uff0c\u6700\u5927\u503c 40 \u662f\u5f02\u5e38\u503c\uff0c\u56e0\u6b64\u88ab\u5220\u9664\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u5355\u4fa7\u683c\u62c9\u5e03\u65af\u68c0\u9a8c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u5bf9\u6570\u636e\u96c6\u4e2d\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u6267\u884c\u5355\u8fb9 Grubbs \u68c0\u9a8c\uff1a<\/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\u5f02\u5e38\u503c\u6d4b\u8bd5\u672a\u5c06\u6700\u5c0f\u503c\u68c0\u6d4b\u4e3a\u5f02\u5e38\u503c\u3002\u7136\u800c\uff0c\u6700\u5927\u5f02\u5e38\u503c\u6d4b\u8bd5\u786e\u5b9a\u6700\u5927\u503c 40 \u662f\u5f02\u5e38\u503c\uff0c\u56e0\u6b64\u88ab\u5220\u9664\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b3\uff1a\u63d0\u53d6\u5f02\u5e38\u503c\u7684\u7d22\u5f15<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u63d0\u53d6\u5f02\u5e38\u503c\u7684\u7d22\u5f15\uff1a<\/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;\">\u8fd9\u544a\u8bc9\u6211\u4eec\u8868\u7684\u7d22\u5f15\u4f4d\u7f6e 16 \u5904\u5b58\u5728\u5f02\u5e38\u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 4\uff1a\u4ece\u5f02\u5e38\u503c\u4e2d\u63d0\u53d6\u503c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u4e86\u5982\u4f55\u4ece\u79bb\u7fa4\u503c\u4e2d\u63d0\u53d6\u503c\uff1a<\/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;\">\u8fd9\u544a\u8bc9\u6211\u4eec\u5b58\u5728\u4e00\u4e2a\u503c\u4e3a 40 \u7684\u5f02\u5e38\u503c\u3002<\/span><\/p>\n<h3><strong>\u5982\u4f55\u5904\u7406\u5f02\u5e38\u503c<\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u5982\u679c Grubbs \u68c0\u9a8c\u8bc6\u522b\u51fa\u6570\u636e\u96c6\u4e2d\u7684\u5f02\u5e38\u503c\uff0c\u60a8\u6709\u591a\u79cd\u9009\u62e9\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u4ed4\u7ec6\u68c0\u67e5\u8be5\u503c\u662f\u5426\u6709\u62fc\u5199\u9519\u8bef\u6216\u6570\u636e\u8f93\u5165\u9519\u8bef\u3002<\/strong>\u6709\u65f6\uff0c\u6570\u636e\u96c6\u4e2d\u663e\u793a\u4e3a\u5f02\u5e38\u503c\u7684\u503c\u53ea\u662f\u4e2a\u4eba\u5728\u6570\u636e\u8f93\u5165\u8fc7\u7a0b\u4e2d\u72af\u4e0b\u7684\u62fc\u5199\u9519\u8bef\u3002\u9996\u5148\uff0c\u5728\u505a\u51fa\u4efb\u4f55\u8fdb\u4e00\u6b65\u51b3\u5b9a\u4e4b\u524d\u9a8c\u8bc1\u8f93\u5165\u7684\u503c\u662f\u5426\u6b63\u786e\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u4e3a\u79bb\u7fa4\u503c\u6307\u5b9a\u4e00\u4e2a\u65b0\u503c<\/strong>\u3002\u5982\u679c\u5f02\u5e38\u503c\u662f\u7531\u62fc\u5199\u9519\u8bef\u6216\u6570\u636e\u8f93\u5165\u9519\u8bef\u9020\u6210\u7684\uff0c\u60a8\u53ef\u4ee5\u51b3\u5b9a\u4e3a\u5176\u5206\u914d\u4e00\u4e2a\u65b0\u503c\uff0c\u4f8b\u5982<\/span><span style=\"color: #000000;\">\u6570\u636e\u96c6\u7684<\/span>\u5e73\u5747\u503c <a href=\"https:\/\/statorials.org\/cn\/\u8861\u91cf\u96c6\u4e2d\u8d8b\u52bf\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u6216\u4e2d\u4f4d\u6570<\/a>\u3002<\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \u5220\u9664\u5f02\u5e38\u503c\u3002<\/strong>\u5982\u679c\u8be5\u503c\u786e\u5b9e\u662f\u5f02\u5e38\u503c\uff0c\u5e76\u4e14\u4f1a\u5bf9\u60a8\u7684\u5206\u6790\u4ea7\u751f\u91cd\u5927\u5f71\u54cd\uff0c\u5219\u53ef\u4ee5\u9009\u62e9\u5c06\u5176\u5220\u9664\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u683c\u62c9\u5e03\u65af\u68c0\u9a8c\u7528\u4e8e\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\u3002\u8981\u4f7f\u7528\u6b64\u68c0\u9a8c\uff0c\u6570\u636e\u96c6\u5fc5\u987b\u8fd1\u4f3c\u6b63\u6001\u5206\u5e03\u5e76\u4e14\u5305\u542b\u81f3\u5c11 7 \u4e2a\u89c2\u6d4b\u503c\u3002  [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-951","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Grubbs \u68c0\u9a8c - Statorials<\/title>\n<meta name=\"description\" content=\"\u5173\u4e8e\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Grubbs \u6d4b\u8bd5\u6765\u68c0\u6d4b\u5f02\u5e38\u503c\u7684\u7b80\u5355\u8bf4\u660e\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/grubbs-\u6d4b\u8bd5-python\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" 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