{"id":3522,"date":"2023-07-17T00:49:18","date_gmt":"2023-07-17T00:49:18","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0\/"},"modified":"2023-07-17T00:49:18","modified_gmt":"2023-07-17T00:49:18","slug":"%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%ad%e3%82%b7%e3%82%99%e3%82%b9%e3%83%86%e3%82%a3%e3%83%83%e3%82%af%e5%9b%9e%e5%b8%b0\/","title":{"rendered":"\u7d71\u8a08\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">Python \u306e<a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a>\u30e2\u30b8\u30e5\u30fc\u30eb\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u7d71\u8a08\u30e2\u30c7\u30eb\u3092\u9069\u5fdc\u3055\u305b\u308b\u305f\u3081\u306e\u3055\u307e\u3056\u307e\u306a\u95a2\u6570\u3068\u30af\u30e9\u30b9\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001statsmodels \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066<a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30-1\/\" target=\"_blank\" rel=\"noopener\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30<\/a>\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u307e\u305a\u30013 \u3064\u306e\u5909\u6570\u3092\u542b\u3080 pandas DataFrame \u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5b66\u7fd2\u6642\u9593\uff08\u5168\u4f53\u5024\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u52c9\u5f37\u6cd5\uff08A\u6cd5\u307e\u305f\u306fB\u6cd5\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8a66\u9a13\u7d50\u679c\uff08\u5408\u5426\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5b66\u7fd2\u6642\u9593\u3068\u5b66\u7fd2\u65b9\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u3001\u5b66\u751f\u304c\u7279\u5b9a\u306e\u8a66\u9a13\u306b\u5408\u683c\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001pandas DataFrame \u3092\u4f5c\u6210\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: #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;\">result<\/span> ': [0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n                              0, 1, 1, 1, 0, 1, 1, 1, 1, 1],\n                   ' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 2, 2, 3, 2, 5, 4, 3, 6,\n                            5, 8, 8, 7, 6, 7, 5, 4, 8, 9],\n                   ' <span style=\"color: #ff0000;\">method<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B',\n                             'B', 'B', 'A', 'B', 'A', 'B', 'B',\n                             'A', 'A', 'B', 'A', 'B', 'A']})\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n\tresult hours method\n0 0 1 A\n1 1 2 A\n2 0 2 A\n3 0 2 B\n4 0 3 B<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <strong>logit()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\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;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> smf\n\n<span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model = smf. <span style=\"color: #3366ff;\">logit<\/span> (' <span style=\"color: #ff0000;\">result~hours+method<\/span> ', data=df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view model summary\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\nOptimization completed successfully.\n         Current function value: 0.557786\n         Iterations 5\n                           Logit Regression Results                           \n==================================================== ============================\nDept. Variable: result No. Observations: 20\nModel: Logit Df Residuals: 17\nMethod: MLE Df Model: 2\nDate: Mon, 22 Aug 2022 Pseudo R-squ.: 0.1894\nTime: 09:53:35 Log-Likelihood: -11.156\nconverged: True LL-Null: -13.763\nCovariance Type: nonrobust LLR p-value: 0.07375\n==================================================== ============================\n                  coef std err z P&gt;|z| [0.025 0.975]\n-------------------------------------------------- -----------------------------\nIntercept -2.1569 1.416 -1.523 0.128 -4.932 0.618\nmethod[TB] 0.0875 1.051 0.083 0.934 -1.973 2.148\nhours 0.4909 0.245 2.002 0.045 0.010 0.972\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u51fa\u529b\u306e<strong>coef<\/strong>\u5217\u306e\u5024\u306f\u3001\u8a66\u9a13\u306b\u5408\u683c\u3059\u308b\u5bfe\u6570\u30aa\u30c3\u30ba\u306e\u5e73\u5747\u5909\u5316\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u4f8b\u3048\u3070\uff1a<\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u52c9\u5f37\u6cd5 B \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u52c9\u5f37\u6cd5 A \u3092\u4f7f\u7528\u3057\u305f\u5834\u5408\u3068\u6bd4\u8f03\u3057\u3066\u3001\u8a66\u9a13\u306b\u5408\u683c\u3059\u308b\u5bfe\u6570\u30aa\u30c3\u30ba\u304c\u5e73\u5747<strong>0.0875<\/strong>\u5897\u52a0\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8ffd\u52a0\u306e\u5b66\u7fd2\u6642\u9593\u304c 1 \u6642\u9593\u5897\u3048\u308b\u3054\u3068\u306b\u3001\u8a66\u9a13\u306b\u5408\u683c\u3059\u308b\u5bfe\u6570\u30aa\u30c3\u30ba\u304c\u5e73\u5747<strong>0.4909<\/strong>\u5897\u52a0\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>P&gt;|z|<\/strong>\u306e\u5024\u5217\u306f\u5404\u4fc2\u6570\u306e p \u5024\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u3048\u3070\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u3053\u306e\u7814\u7a76\u65b9\u6cd5\u306e p \u5024\u306f<strong>0.934<\/strong>\u3067\u3059\u3002\u3053\u306e\u5024\u306f 0.05 \u672a\u6e80\u3067\u306f\u306a\u3044\u305f\u3081\u3001\u5b66\u7fd2\u6642\u9593\u3068\u8a66\u9a13\u306e\u5408\u683c\u3068\u306e\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u95a2\u4fc2\u304c\u306a\u3044\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u7fd2\u6642\u9593\u306e p \u5024\u306f<strong>0.045<\/strong>\u3067\u3059\u3002\u3053\u306e\u5024\u306f 0.05 \u672a\u6e80\u3067\u3042\u308b\u305f\u3081\u3001\u5b66\u7fd2\u6642\u9593\u3068\u5b66\u751f\u304c\u8a66\u9a13\u306b\u5408\u683c\u3059\u308b\u304b\u3069\u3046\u304b\u306e\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u95a2\u4fc2\u304c\u3042\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u51fa\u529b\u5185\u306e 2 \u3064\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u611b\u79f0 R-2\u4e57<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u5024\u306f\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e R \u4e8c\u4e57\u5024\u306e\u4ee3\u66ff\u5024\u3068\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u30cc\u30eb \u30e2\u30c7\u30eb\u304b\u3089\u5b8c\u5168\u30e2\u30c7\u30eb\u3078\u306e\u6700\u5927\u5316\u3055\u308c\u305f\u5bfe\u6570\u5c24\u5ea6\u95a2\u6570\u306e\u6bd4\u7387\u3068\u3057\u3066\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u5024\u306e\u7bc4\u56f2\u306f 0 \u304b\u3089 1 \u3067\u3001\u5024\u304c\u5927\u304d\u3044\u307b\u3069\u30e2\u30c7\u30eb\u306e\u9069\u5408\u6027\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u64ec\u4f3c R \u4e8c\u4e57\u5024\u306f<strong>0.1894<\/strong>\u3067\u3042\u308a\u3001\u975e\u5e38\u306b\u4f4e\u3044\u5024\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5909\u6570\u304c\u5fdc\u7b54\u5909\u6570\u306e\u5024\u3092\u3046\u307e\u304f\u4e88\u6e2c\u3057\u3066\u3044\u306a\u3044\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. LLR p \u5024<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u5024\u306f\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e<a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u306b\u304a\u3051\u308b\u5168\u4f53\u7684\u306a\u6709\u610f\u6027\u306e-f-\u691c\u5b9a\u3092\u7406\u89e3\u3059\u308b\u305f\u3081\u306e\u7c21\u5358\u306a\u30ab\u3099\u30a4\u30c8\u3099\/\" target=\"_blank\" rel=\"noopener\">\u5168\u4f53\u7684\u306a F \u5024\u306e<\/a>p \u5024\u306e\u4ee3\u66ff\u3068\u3057\u3066\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u5024\u304c\u7279\u5b9a\u306e\u3057\u304d\u3044\u5024 (\u305f\u3068\u3048\u3070\u3001\u03b1 = 0.05) \u3092\u4e0b\u56de\u3063\u3066\u3044\u308b\u5834\u5408\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u304c\u300c\u6709\u7528\u300d\u3067\u3042\u308b\u3068\u7d50\u8ad6\u4ed8\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u4e88\u6e2c\u5909\u6570\u306e\u306a\u3044\u30e2\u30c7\u30eb\u3068\u6bd4\u8f03\u3057\u3066\u5fdc\u7b54\u5909\u6570\u306e\u5024\u3092\u3088\u308a\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001LLR \u306e p \u5024\u306f<strong>0.07375<\/strong>\u3067\u3059\u3002\u9078\u629e\u3057\u305f\u6709\u610f\u6c34\u6e96 (0.01\u30010.05\u30010.1 \u306a\u3069) \u306b\u5fdc\u3058\u3066\u3001\u30e2\u30c7\u30eb\u5168\u4f53\u304c\u6709\u7528\u3067\u3042\u308b\u3068\u7d50\u8ad6\u4ed8\u3051\u308b\u5834\u5408\u3082\u3042\u308c\u3070\u3001\u305d\u3046\u3067\u306a\u3044\u5834\u5408\u3082\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\/\u7dda\u5f62\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u5bfe\u6570\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u5bfe\u6570\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u306e\u5206\u4f4d\u70b9\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u5206\u4f4d\u70b9\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python \u306estatsmodels\u30e2\u30b8\u30e5\u30fc\u30eb\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u7d71\u8a08\u30e2\u30c7\u30eb\u3092\u9069\u5fdc\u3055\u305b\u308b\u305f\u3081\u306e\u3055\u307e\u3056\u307e\u306a\u95a2\u6570\u3068\u30af\u30e9\u30b9\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002 \u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001statsmodels \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f [&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-3522","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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