{"id":3558,"date":"2023-07-16T20:16:40","date_gmt":"2023-07-16T20:16:40","guid":{"rendered":"https:\/\/statorials.org\/ko\/ols-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-16T20:16:40","modified_gmt":"2023-07-16T20:16:40","slug":"ols-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%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\/ols-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%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 ols \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">OLS(Ordinary Least Square) \ud68c\uadc0\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 <a href=\"https:\/\/statorials.org\/ko\/\u1107\u1167\u11ab\u1109\u116e-\u1109\u1165\u11af\u1106\u1167\u11bc-\u110b\u1173\u11bc\u1103\u1161\u11b8\/\" target=\"_blank\" rel=\"noopener\">\ubc18\uc751 \ubcc0\uc218<\/a> \uac04\uc758 \uad00\uacc4\ub97c \uac00\uc7a5 \uc798 \uc124\uba85\ud558\ub294 \uc120\uc744 \ucc3e\uc744 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uba74 \ub2e4\uc74c \ubc29\uc815\uc2dd\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0177 = <sub>b0<\/sub> + <sub>b1x<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u0177<\/strong> : \uc608\uc0c1\ub41c \ubc18\uc751\uac12<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>b <sub>0<\/sub><\/strong> : \ud68c\uadc0\uc120\uc758 \uc6d0\uc810<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>b <sub>1<\/sub><\/strong> : \ud68c\uadc0\uc120\uc758 \uae30\uc6b8\uae30<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774 \ubc29\uc815\uc2dd\uc740 \uc608\uce21\ubcc0\uc218\uc640 \ubc18\uc751\ubcc0\uc218 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\uc73c\uba70, \uc608\uce21\ubcc0\uc218\uc758 \uac12\uc774 \uc8fc\uc5b4\uc84c\uc744 \ub54c \ubc18\uc751\ubcc0\uc218\uc758 \uac12\uc744 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 Python\uc5d0\uc11c OLS \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><b>1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/b><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 \ud559\uc0dd 15\uba85\uc5d0 \ub300\ud574 \ub2e4\uc74c \ub450 \ubcc0\uc218\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ucd1d \ud559\uc2b5 \uc2dc\uac04<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc2dc\ud5d8 \uacb0\uacfc<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc2dc\uac04\uc744 \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc2dc\ud5d8 \uc810\uc218\ub97c \uc751\ub2f5 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec OLS \ud68c\uadc0\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 Pandas\uc5d0\uc11c \uc774 \uac00\uc9dc \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4dc\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<span style=\"color: #008080;\">\n#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14],\n                   ' <span style=\"color: #ff0000;\">score<\/span> ': [64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n    hours score\n0 1 64\n1 2 66\n2 4 76\n3 5 73\n4 5 74\n5 6 81\n6 6 83\n7 7 82\n8 8 80\n9 10 88\n10 11 84\n11 11 82\n12 12 91\n13 12 93\n14 14 89<\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><b>2\ub2e8\uacc4: OLS \ud68c\uadc0 \uc218\ud589<\/b><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c, <a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a> \ubaa8\ub4c8\uc758 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec <strong>\uc2dc\uac04\uc744<\/strong> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc810\uc218\ub97c <strong>\uc751\ub2f5<\/strong> \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec OLS \ud68c\uadc0\ub97c \uc218\ud589\ud560 \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> statsmodels.api <span style=\"color: #008000;\">as<\/span> sm\n<\/span>\n#define predictor and response variables\n<span style=\"color: #000000;\">y = df[' <span style=\"color: #ff0000;\">score<\/span> ']\nx = df[' <span style=\"color: #ff0000;\">hours<\/span> ']<\/span>\n\n#add constant to predictor variables\n<span style=\"color: #000000;\">x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n<\/span>\n#fit linear regression model\n<span style=\"color: #000000;\">model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n<\/span>\n#view model summary\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.831\nModel: OLS Adj. R-squared: 0.818\nMethod: Least Squares F-statistic: 63.91\nDate: Fri, 26 Aug 2022 Prob (F-statistic): 2.25e-06\nTime: 10:42:24 Log-Likelihood: -39,594\nNo. Observations: 15 AIC: 83.19\nDf Residuals: 13 BIC: 84.60\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 65.3340 2.106 31.023 0.000 60.784 69.884\nhours 1.9824 0.248 7.995 0.000 1.447 2.518\n==================================================== ============================\nOmnibus: 4,351 Durbin-Watson: 1,677\nProb(Omnibus): 0.114 Jarque-Bera (JB): 1.329\nSkew: 0.092 Prob(JB): 0.515\nKurtosis: 1.554 Cond. No. 19.2\n==================================================== ============================<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>coef<\/strong> \uc5f4\uc5d0\uc11c \ud68c\uadc0 \uacc4\uc218\ub97c \ud655\uc778\ud558\uace0 \ub2e4\uc74c\uacfc \uac19\uc740 \uc801\ud569 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc810\uc218 = 65.334 + 1.9824*(\uc2dc\uac04)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \uacf5\ubd80\ud55c \uc2dc\uac04\uc774 \ucd94\uac00\ub420 \ub54c\ub9c8\ub2e4 \ud3c9\uade0 \uc2dc\ud5d8 \uc810\uc218\uac00 <strong>1.9824<\/strong> \uc810 \uc99d\uac00\ud55c\ub2e4\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc6d0\ub798 \uac12 <strong>65,334<\/strong> \ub294 0\uc2dc\uac04 \ub3d9\uc548 \uacf5\ubd80\ud558\ub294 \ud559\uc0dd\uc758 \ud3c9\uade0 \uc608\uc0c1 \uc2dc\ud5d8 \uc810\uc218\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \uc774 \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc0dd\uc774 \uacf5\ubd80\ud558\ub294 \uc2dc\uac04\uc744 \uae30\uc900\uc73c\ub85c \uc608\uc0c1 \uc2dc\ud5d8 \uc810\uc218\ub97c \ucc3e\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4, 10\uc2dc\uac04 \ub3d9\uc548 \uacf5\ubd80\ud55c \ud559\uc0dd\uc740 \uc2dc\ud5d8 \uc810\uc218 <strong>85.158<\/strong> \uc744 \ud68d\ub4dd\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc810\uc218 = 65.334 + 1.9824*(10) = 85.158<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378 \uc694\uc57d\uc758 \ub098\uba38\uc9c0 \ubd80\ubd84\uc744 \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>P(&gt;|t|):<\/strong> \ubaa8\ub378 \uacc4\uc218\uc640 \uc5f0\uad00\ub41c p \uac12\uc785\ub2c8\ub2e4. <em>\uc2dc\uac04<\/em> \uc5d0 \ub300\ud55c p-\uac12(0.000)\uc774 0.05\ubcf4\ub2e4 \uc791\uc73c\ubbc0\ub85c <em>\uc2dc\uac04<\/em> \uacfc <em>\uc810\uc218<\/em> \uc0ac\uc774\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \uc5f0\uad00\uc131\uc774 \uc788\ub2e4\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>R-\uc81c\uacf1:<\/strong> \uc2dc\ud5d8 \uc810\uc218\uc758 \ubcc0\ub3d9 \ube44\uc728\uc774 \uacf5\ubd80\ud55c \uc2dc\uac04\uc5d0 \ub530\ub77c \uc124\uba85\ub420 \uc218 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uc774 \uacbd\uc6b0 \uc810\uc218 \ubcc0\ub3d9\uc758 <strong>83.1%\uac00<\/strong> \uacf5\ubd80 \uc2dc\uac04\uc73c\ub85c \uc124\uba85\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>F-\ud1b5\uacc4\ub7c9 \ubc0f p-\uac12:<\/strong> F-\ud1b5\uacc4\ub7c9( <strong>63.91<\/strong> )\uacfc \ud574\ub2f9 p-\uac12( <strong>2.25e-06<\/strong> )\uc740 \ud68c\uadc0 \ubaa8\ub378\uc758 \uc804\ubc18\uc801\uc778 \uc911\uc694\uc131, \uc989 \ubaa8\ub378\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \ubcc0\ub3d9\uc744 \uc124\uba85\ud558\ub294 \ub370 \uc720\uc6a9\ud55c\uc9c0 \uc5ec\ubd80\ub97c \uc54c\ub824\uc90d\ub2c8\ub2e4. \uc751\ub2f5 \ubcc0\uc218\uc5d0\uc11c. \uc774 \uc608\uc758 p-\uac12\uc740 0.05 \ubbf8\ub9cc\uc774\ubbc0\ub85c \ubaa8\ub378\uc774 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud558\uba70 <em>\uc2dc\uac04\uc740<\/em> <em>\uc810\uc218<\/em> \ubcc0\ud654\ub97c \uc124\uba85\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \uac83\uc73c\ub85c \uac04\uc8fc\ub429\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<h2> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \uac00\uc7a5 \uc801\ud569\ud55c \uc120 \uc2dc\uac01\ud654<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub9c8\uc9c0\ub9c9\uc73c\ub85c <strong>matplotlib<\/strong> \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc5d0 \ub9de\ub294 \ud68c\uadc0\uc120\uc744 \uc2dc\uac01\ud654\ud560 \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<\/span>\n#find line of best fit\n<span style=\"color: #000000;\">a, b = np. <span style=\"color: #3366ff;\">polyfit<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], df[' <span style=\"color: #ff0000;\">score<\/span> '], <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#add points to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">scatter<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], df[' <span style=\"color: #ff0000;\">score<\/span> '], color=' <span style=\"color: #ff0000;\">purple<\/span> ')\n<\/span>\n#add line of best fit to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], a*df[' <span style=\"color: #ff0000;\">hours<\/span> ']+b)\n<\/span>\n#add fitted regression equation to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">text<\/span> ( <span style=\"color: #008000;\">1<\/span> , <span style=\"color: #008000;\">90<\/span> , 'y = ' + '{:.3f}'.format(b) + ' + {:.3f}'.format(a) + 'x', size= <span style=\"color: #008000;\">12<\/span> )\n\n<span style=\"color: #008080;\">#add axis labels\n<\/span>plt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">Hours Studied<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">Exam Score<\/span> ')\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-29456 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ligne11.jpg\" alt=\"\" width=\"502\" height=\"385\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\ubcf4\ub77c\uc0c9 \uc810\uc740 \uc2e4\uc81c \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub97c \ub098\ud0c0\ub0b4\uace0 \ud30c\ub780\uc0c9 \uc120\uc740 \uc801\ud569 \ud68c\uadc0\uc120\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c <strong>plt.text()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud50c\ub86f\uc758 \uc67c\ucabd \uc0c1\ub2e8\uc5d0 \uc801\ud569\ud55c \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \ucd94\uac00\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub798\ud504\ub97c \ubcf4\uba74 \uc801\ud569 \ud68c\uadc0\uc120\uc774 <strong>\uc2dc\uac04<\/strong> \ubcc0\uc218\uc640 <strong>\uc810\uc218<\/strong> \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \ub9e4\uc6b0 \uc798 \ud3ec\ucc29\ud558\uace0 \uc788\ub294 \uac83\uc73c\ub85c \ubcf4\uc785\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\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u110c\u1175\u1109\u116e-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \uc9c0\uc218 \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-aic\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc758 AIC\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>OLS(Ordinary Least Square) \ud68c\uadc0\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uac00\uc7a5 \uc798 \uc124\uba85\ud558\ub294 \uc120\uc744 \ucc3e\uc744 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uba74 \ub2e4\uc74c \ubc29\uc815\uc2dd\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \u0177 = b0 + b1x \uae08: \u0177 : \uc608\uc0c1\ub41c \ubc18\uc751\uac12 b 0 : \ud68c\uadc0\uc120\uc758 \uc6d0\uc810 b 1 : \ud68c\uadc0\uc120\uc758 \uae30\uc6b8\uae30 \uc774 \ubc29\uc815\uc2dd\uc740 \uc608\uce21\ubcc0\uc218\uc640 \ubc18\uc751\ubcc0\uc218 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 [&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-3558","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 OLS \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - \ud1b5\uacc4<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 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