{"id":3524,"date":"2023-07-17T00:49:18","date_gmt":"2023-07-17T00:49:18","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80\/"},"modified":"2023-07-17T00:49:18","modified_gmt":"2023-07-17T00:49:18","slug":"%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80\/","title":{"rendered":"\ud1b5\uacc4 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">Python\uc758 <a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a> \ubaa8\ub4c8\uc740 \ub2e4\uc591\ud55c \ud1b5\uacc4 \ubaa8\ub378\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ud568\uc218\uc640 \ud074\ub798\uc2a4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 statsmodels \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec <a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-1\/\" target=\"_blank\" rel=\"noopener\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c<\/a> \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 \uc138 \uac00\uc9c0 \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\ub294 pandas DataFrame\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uacf5\ubd80\ud55c \uc2dc\uac04(\uc804\uccb4 \uac12)<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc5f0\uad6c \ubc29\ubc95(\ubc29\ubc95 A \ub610\ub294 B)<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc2dc\ud5d8 \uacb0\uacfc(\ud569\uaca9 \ub610\ub294 \ubd88\ud569\uaca9)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc6b0\ub9ac\ub294 \ud559\uc0dd\uc774 \uc8fc\uc5b4\uc9c4 \uc2dc\ud5d8\uc5d0 \ud569\uaca9\ud560\uc9c0 \uc5ec\ubd80\ub97c \uc608\uce21\ud558\uae30 \uc704\ud574 \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \uacf5\ubd80 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 Pandas DataFrame\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\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;\">#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>2\ub2e8\uacc4: \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc801\ud569<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c <strong>logit()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud569\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;\">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;\">\ucd9c\ub825\uc758 <strong>coef<\/strong> \uc5f4\uc5d0 \uc788\ub294 \uac12\uc740 \uc2dc\ud5d8 \ud569\uaca9 \ub85c\uadf8 \ud655\ub960\uc758 \ud3c9\uade0 \ubcc0\ud654\ub97c \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4:<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ud559\uc2b5 \ubc29\ubc95 B\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc740 \ud559\uc2b5 \ubc29\ubc95 A\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uacfc \ube44\uad50\ud558\uc5ec \uc2dc\ud5d8 \ud569\uaca9 \ub85c\uadf8 \ud655\ub960\uc774 \ud3c9\uade0 <strong>0.0875<\/strong> \uc99d\uac00\ud558\ub294 \uac83\uacfc \uad00\ub828\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uacf5\ubd80\ud55c \uac01 \ucd94\uac00 \uc2dc\uac04\uc740 \uc2dc\ud5d8 \ud569\uaca9 \ub85c\uadf8 \ud655\ub960\uc774 \ud3c9\uade0 <strong>0.4909<\/strong> \uc99d\uac00\ud558\ub294 \uac83\uacfc \uad00\ub828\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>P&gt;|z|<\/strong> \uc758 \uac12 \uc5f4\uc740 \uac01 \uacc4\uc218\uc5d0 \ub300\ud55c p-\uac12\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc5f0\uad6c \ubc29\ubc95\uc758 p-\uac12\uc740 <strong>0.934<\/strong> \uc785\ub2c8\ub2e4. \uc774 \uac12\uc740 0.05 \uc774\uc0c1\uc774\ubbc0\ub85c \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \uc2dc\ud5d8 \ud569\uaca9 \uc5ec\ubd80 \uc0ac\uc774\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \uad00\uacc4\uac00 \uc5c6\ub2e4\ub294 \ub73b\uc774\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc5f0\uad6c\ud55c \uc2dc\uac04\uc758 p-\uac12\uc740 <strong>0.045<\/strong> \uc785\ub2c8\ub2e4. \uc774 \uac12\uc740 0.05\ubcf4\ub2e4 \uc791\uc73c\ubbc0\ub85c \uacf5\ubd80\uc2dc\uac04\uacfc \uc2dc\ud5d8 \ud569\uaca9 \uc5ec\ubd80 \uc0ac\uc774\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ud55c \uad00\uacc4\uac00 \uc788\ub2e4\ub294 \ub73b\uc774\ub2e4.<\/span><\/li>\n<\/ul>\n<h2> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ucd9c\ub825\uc5d0\uc11c \ub450 \uac00\uc9c0 \uce21\uc815\ud56d\ubaa9\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \ubcc4\uba85 R-\uc81c\uacf1<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc740 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc758 R \uc81c\uacf1 \uac12\uc744 \ub300\uccb4\ud558\ub294 \uac83\uc73c\ub85c \uac04\uc8fc\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \ub110 \ubaa8\ub378\uc5d0\uc11c \uc804\uccb4 \ubaa8\ub378\uc5d0 \ub300\ud55c \ucd5c\ub300\ud654\ub41c \ub85c\uadf8 \uc6b0\ub3c4 \ud568\uc218\uc758 \ube44\uc728\ub85c \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc758 \ubc94\uc704\ub294 0\uc5d0\uc11c 1\uae4c\uc9c0\uc774\uba70, \uac12\uc774 \ub192\uc744\uc218\ub85d \ubaa8\ub378 \uc801\ud569\ub3c4\uac00 \ub354 \uc6b0\uc218\ud568\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c \uc758\uc0ac R-\uc81c\uacf1 \uac12\uc740 <strong>0.1894<\/strong> \ub85c \ub9e4\uc6b0 \ub0ae\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc751\ub2f5 \ubcc0\uc218\uc758 \uac12\uc744 \uc798 \uc608\uce21\ud558\uc9c0 \ubabb\ud558\uace0 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. LLR p-\uac12<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc740 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc758 <a href=\"https:\/\/statorials.org\/ko\/\u1112\u116c\u1100\u1171-\u1107\u116e\u11ab\u1109\u1165\u11a8\u110b\u1174-\u110c\u1165\u11ab\u1107\u1161\u11ab\u110c\u1165\u11a8\u110b\u1175\u11ab-\u110c\u116e\u11bc\u110b\u116d\u1109\u1165\u11bc\u110b\u1166-\u1103\u1162\u1112\u1161\u11ab-f-\u1110\u1166\u1109\u1173\u1110\u1173\u1105\u1173\u11af-\u110b\u1175\u1112\u1162\u1112\u1161\u1100\u1175-\u110b\u1171\u1112\u1161\u11ab-\u1100\u1161\u11ab\u1103\u1161\u11ab\u1112\u1161\u11ab-\u1100\u1161\u110b\u1175\u1103\u1173\/\" target=\"_blank\" rel=\"noopener\">\uc804\uccb4 F-\uac12<\/a> \uc5d0 \ub300\ud55c p-\uac12\uc744 \ub300\uccb4\ud558\ub294 \uac83\uc73c\ub85c \uac04\uc8fc\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc774 \ud2b9\uc815 \uc784\uacc4\uac12(\uc608: \u03b1 = 0.05)\ubcf4\ub2e4 \ub0ae\uc73c\uba74 \ubaa8\ub378 \uc804\uccb4\uac00 &#8220;\uc720\uc6a9\ud558\ub2e4&#8221;\uace0 \uacb0\ub860\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc73c\uba70 \uc608\uce21 \ubcc0\uc218\uac00 \uc5c6\ub294 \ubaa8\ub378\uc5d0 \ube44\ud574 \uc751\ub2f5 \ubcc0\uc218 \uac12\uc744 \ub354 \uc798 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c LLR\uc758 p-\uac12\uc740 <strong>0.07375<\/strong> \uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc120\ud0dd\ud55c \uc720\uc758 \uc218\uc900(\uc608: 0.01, 0.05, 0.1)\uc5d0 \ub530\ub77c \ubaa8\ub378 \uc804\uccb4\uac00 \uc720\uc6a9\ud558\ub2e4\ub294 \uacb0\ub860\uc744 \ub0b4\ub9b4 \uc218\ub3c4 \uc788\uace0 \uadf8\ub807\uc9c0 \uc54a\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1162\u1109\u116e-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ub85c\uadf8 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\u110b\u1174-\u1107\u116e\u11ab\u110b\u1171\u1109\u116e-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ubd84\uc704\uc218 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python\uc758 statsmodels \ubaa8\ub4c8\uc740 \ub2e4\uc591\ud55c \ud1b5\uacc4 \ubaa8\ub378\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ud568\uc218\uc640 \ud074\ub798\uc2a4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 statsmodels \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. 1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131 \uba3c\uc800 \uc138 \uac00\uc9c0 \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\ub294 pandas DataFrame\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uacf5\ubd80\ud55c \uc2dc\uac04(\uc804\uccb4 \uac12) \uc5f0\uad6c \ubc29\ubc95(\ubc29\ubc95 A \ub610\ub294 B) \uc2dc\ud5d8 \uacb0\uacfc(\ud569\uaca9 \ub610\ub294 \ubd88\ud569\uaca9) \uc6b0\ub9ac\ub294 \ud559\uc0dd\uc774 \uc8fc\uc5b4\uc9c4 \uc2dc\ud5d8\uc5d0 \ud569\uaca9\ud560\uc9c0 \uc5ec\ubd80\ub97c \uc608\uce21\ud558\uae30 [&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-3524","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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