{"id":1313,"date":"2023-07-26T22:01:06","date_gmt":"2023-07-26T22:01:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8c%e1%85%ae%e1%84%87%e1%85%a1%e1%86%bc-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-26T22:01:06","modified_gmt":"2023-07-26T22:01:06","slug":"%e1%84%8c%e1%85%ae%e1%84%87%e1%85%a1%e1%86%bc-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%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\/%e1%84%8c%e1%85%ae%e1%84%87%e1%85%a1%e1%86%bc-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%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 cook\uc758 \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Cook\uc758 \uac70\ub9ac\ub294<\/strong> \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uc601\ud5a5\ub825 \uc788\ub294 <a href=\"https:\/\/statorials.org\/ko\/\u1110\u1169\u11bc\u1100\u1168\u110b\u1166\u1109\u1165\u110b\u1174-\u1100\u116a\u11ab\u110e\u1161\u11af\/\" target=\"_blank\" rel=\"noopener\">\uad00\uce21\uce58\ub97c<\/a> \uc2dd\ubcc4\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cook\uc758 \uac70\ub9ac \uacf5\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p style=\"text-align: center;\"> <span style=\"color: #000000;\"><strong>d <sub>i<\/sub><\/strong> = (ri <sub>2<\/sub> \/ p*MSE) * (h <sub>ii<\/sub> \/ (1-h <sub>ii<\/sub> <sup>)<\/sup> <sup>2<\/sup> )<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>r<\/strong> <sub><strong>i<\/strong><\/sub> \ub294 i <sup>\ubc88\uc9f8<\/sup> \uc794\uae30\uc774\ub2e4<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>p<\/strong> \ub294 \ud68c\uadc0 \ubaa8\ub378\uc758 \uacc4\uc218 \uc218\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>MSE<\/strong> \ub294 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>h<\/strong> <sub>ii<\/sub> \ub294 <sup>i\ubc88\uc9f8<\/sup> \ub808\ubc84\ub9ac\uc9c0 \uac12\uc785\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uae30\ubcf8\uc801\uc73c\ub85c Cook\uc758 \uac70\ub9ac\ub294 i <sup>\ubc88\uc9f8<\/sup> \uad00\uce21\uce58\uac00 \uc81c\uac70\ub420 \ub54c \ubaa8\ub378\uc758 \ubaa8\ub4e0 \uc801\ud569\uce58\uac00 \uc5bc\ub9c8\ub098 \ubcc0\uacbd\ub418\ub294\uc9c0 \uce21\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cook\uc758 \uac70\ub9ac \uac12\uc774 \ud074\uc218\ub85d \uc8fc\uc5b4\uc9c4 \uad00\uce21\uac12\uc758 \uc601\ud5a5\ub825\uc774 \ucee4\uc9d1\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc77c\ubc18\uc801\uc73c\ub85c Cook\uc758 \uac70\ub9ac\uac00 4\/n( <em>n<\/em> = \ucd1d \uad00\uce21\uce58)\ubcf4\ub2e4 \ud070 \uad00\uce21\uce58\ub294 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \uac83\uc73c\ub85c \uac04\uc8fc\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 Python\uc5d0\uc11c \uc8fc\uc5b4\uc9c4 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ub300\ud574 Cook\uc758 \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ub370\uc774\ud130 \uc785\ub825<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 Python\uc5d0\uc11c \uc791\uc5c5\ud560 \uc791\uc740 \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create dataset\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #008000;\">x<\/span> ': [8, 12, 12, 13, 14, 16, 17, 22, 24, 26, 29, 30],\n                   ' <span style=\"color: #008000;\">y<\/span> ': [41, 42, 39, 37, 35, 39, 45, 46, 39, 49, 55, 57]})\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: \ud68c\uadc0 \ubaa8\ub378 \uc801\ud569<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\u110b\u1174-\u1100\u1161\u11ab\u1103\u1161\u11ab\u1112\u1161\u11ab-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">\uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \ud53c\ud305\ud558\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> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define response variable\n<\/span>y = df[' <span style=\"color: #008000;\">y<\/span> ']\n\n<span style=\"color: #008080;\">#define explanatory variable\n<\/span>x = df[' <span style=\"color: #008000;\">x<\/span> ']\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \uc694\ub9ac \uac70\ub9ac \uacc4\uc0b0<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c \ubaa8\ub378\uc758 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Cook \uac70\ub9ac\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#suppress scientific notation\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\nn.p. <span style=\"color: #3366ff;\">set_printoptions<\/span> (suppress= <span style=\"color: #008000;\">True<\/span> )\n\n<span style=\"color: #008080;\">#create instance of influence\n<\/span>influence = model. <span style=\"color: #3366ff;\">get_influence<\/span> ()\n\n<span style=\"color: #008080;\">#obtain Cook's distance for each observation\n<\/span>cooks = influence. <span style=\"color: #3366ff;\">cooks_distance<\/span>\n\n<span style=\"color: #008080;\">#display Cook's distances\n<\/span><span style=\"color: #993300;\">print<\/span> (cooks)\n\n(array([0.368, 0.061, 0.001, 0.028, 0.105, 0.022, 0.017, 0. , 0.343,\n        0. , 0.15 , 0.349]),\n array([0.701, 0.941, 0.999, 0.973, 0.901, 0.979, 0.983, 1. , 0.718,\n        1. , 0.863, 0.713]))\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uae30\ubcf8\uc801\uc73c\ub85c <strong>cooks_distance()<\/strong> \ud568\uc218\ub294 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Cook\uc758 \uac70\ub9ac \uac12 \ubc30\uc5f4\uacfc \uadf8\uc5d0 \uc0c1\uc751\ud558\ub294 p-\uac12 \ubc30\uc5f4\uc744 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uad00\ucc30 #1\uc5d0 \ub300\ud55c Cook\uc758 \uac70\ub9ac: <strong>0.368<\/strong> (p-\uac12: 0.701)<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uad00\ucc30 #2\uc5d0 \ub300\ud55c Cook\uc758 \uac70\ub9ac: <strong>0.061<\/strong> (p-\uac12: 0.941)<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uad00\ucc30 #3\uc5d0 \ub300\ud55c Cook\uc758 \uac70\ub9ac: <strong>0.001<\/strong> (p-\uac12: 0.999)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub4f1\ub4f1.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>4\ub2e8\uacc4: \uc694\ub9ac\uc0ac\uc758 \uac70\ub9ac \uc2dc\uac01\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc608\uce21 \ubcc0\uc218\uc758 \uac12\uc744 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Cook\uc758 \uac70\ub9ac \ud568\uc218\ub85c \uc2dc\uac01\ud654\ud558\ub294 \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df.x, cooks[0])\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #008000;\">x<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #008000;\">Cooks Distance<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12869 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/cuisinierspython1.png\" alt=\"\ucfe1\uc758 Python \uac70\ub9ac\" width=\"420\" height=\"284\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\ub9c8\uc9c0\ub9c9 \uc0dd\uac01\ub4e4<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc7a0\uc7ac\uc801\uc73c\ub85c \uc601\ud5a5\ub825\uc774 \uc788\ub294 \uad00\uce21\uce58\ub97c <em>\uc2dd\ubcc4<\/em> \ud558\ub824\uba74 Cook\uc758 \uac70\ub9ac\ub97c \uc0ac\uc6a9\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc5d0 \uc720\uc758\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/span> <span style=\"color: #000000;\">\uad00\ucc30\uc774 \uc601\ud5a5\ub825\uc774 \uc788\ub2e4\uace0 \ud574\uc11c \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c \uc81c\uac70\ub418\uc5b4\uc57c \ud55c\ub2e4\ub294 \uc758\ubbf8\ub294 \uc544\ub2d9\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uba3c\uc800, \uad00\ucc30 \uacb0\uacfc\uac00 \ub370\uc774\ud130 \uc785\ub825 \uc624\ub958\ub098 \uae30\ud0c0 \uc774\uc0c1\ud55c \uc0ac\uac74\uc758 \uacb0\uacfc\uac00 \uc544\ub2cc\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \ud569\ubc95\uc801\uc778 \uac12\uc73c\ub85c \ud310\uba85\ub418\uba74 \uc774\ub97c \uc81c\uac70\ud558\ub294 \uac83\uc774 \uc801\uc808\ud55c\uc9c0, \uadf8\ub300\ub85c \ub450\ub294 \uac83\uc774 \uc801\uc808\ud55c\uc9c0, \uc544\ub2c8\uba74 \ub2e8\uc21c\ud788 \uc911\uc559\uac12\uacfc \uac19\uc740 \ub300\uccb4 \uac12\uc73c\ub85c \ubc14\uafb8\ub294 \uac83\uc774 \uc801\uc808\ud55c\uc9c0 \uacb0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cook\uc758 \uac70\ub9ac\ub294 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uc601\ud5a5\ub825 \uc788\ub294 \uad00\uce21\uce58\ub97c \uc2dd\ubcc4\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. Cook\uc758 \uac70\ub9ac \uacf5\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. d i = (ri 2 \/ p*MSE) * (h ii \/ (1-h ii ) 2 ) \uae08: r i \ub294 i \ubc88\uc9f8 \uc794\uae30\uc774\ub2e4 p \ub294 \ud68c\uadc0 \ubaa8\ub378\uc758 \uacc4\uc218 \uc218\uc785\ub2c8\ub2e4. MSE \ub294 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28\uc785\ub2c8\ub2e4. h ii \ub294 i\ubc88\uc9f8 \ub808\ubc84\ub9ac\uc9c0 \uac12\uc785\ub2c8\ub2e4. [&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-1313","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 Cook\uc758 \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Cook\uc758 \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc640 \ud568\uaed8 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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