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\ud000\ud2b8 \ub9e4\ub9e4, \uc989 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud22c\uc790 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\uace0 \uc774\ub97c \uc790\ub3d9\uc73c\ub85c \uc2e4\ud589\ud558\uae30 \uc704\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ubc1c\uc804\ud558\uba74\uc11c, \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0 \uc788\uc5b4 \uae4a\uc774 \uc788\ub294 \uc778\uc0ac\uc774\ud2b8\ub97c \ub3c4\ucd9c\ud574\ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 statsmodels<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc18c \uc790\uc2b9\ubc95(Ordinary Least Squares, OLS) \ud68c\uadc0 \ubd84\uc11d\uc744 \ud1b5\ud574 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc77c\uc885\uc73c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uacfc\uac70\uc758 \uc2dc\uc7a5 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ubbf8\ub798\uc758 \uac00\uaca9 \ubcc0\ud654\ub97c \uc608\uce21\ud558\uac70\ub098, \ud2b9\uc815 \ud328\ud134\uc744 \uc2dd\ubcc4\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
1.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc720\ud615<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0 \uc720\ud615\uc73c\ub85c \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n
\uc9c0\ub3c4 \ud559\uc2b5(Supervised Learning)<\/strong>: \uc785\ub825 \ub370\uc774\ud130\uc640 \ub808\uc774\ube14\uc774 \uc8fc\uc5b4\uc9c0\uba70, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n
\ube44\uc9c0\ub3c4 \ud559\uc2b5(Unsupervised Learning)<\/strong>: \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ub808\uc774\ube14\uc774 \uc5c6\uc774 \ud328\ud134\uc774\ub098 \uad70\uc9d1\uc744 \ucc3e\uc544\ub0b4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/li>\n
\uac15\ud654 \ud559\uc2b5(Reinforcement Learning)<\/strong>: agent\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ubcf4\uc0c1\uc744 \ucd5c\ub300\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n
1.2 \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804<\/h3>\n
\ub525\ub7ec\ub2dd\uc740 \ub2e4\uce35 \uc2e0\uacbd\ub9dd(Deep Neural Network)\uc744 \ud1b5\ud574 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc2dd\ubcc4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uadf8\ub9ac\uace0 \uc2dc\uac04\uc5d0 \ub530\ub978 \ub370\uc774\ud130\uc758 \ud328\ud134 \uc778\uc2dd \ub4f1\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. \ucd5c\uadfc \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub3c4 \uc774\ub7ec\ud55c \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud55c \uc608\uce21 \ubaa8\ub378\uc774 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2. \ucd5c\uc18c \uc790\uc2b9\ubc95(Ordinary Least Squares, OLS) \uc18c\uac1c<\/h2>\n
OLS\ub294 \ud1b5\uacc4\ud559\uc5d0\uc11c \uac00\uc7a5 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ud68c\uadc0 \ubd84\uc11d \ubc29\ubc95 \uc911 \ud558\ub098\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc758 \uc801\ud569\ub3c4\ub97c \ucd5c\ub300\ud654\ud558\uae30 \uc704\ud574 \ud68c\uadc0 \uacc4\uc218\ub97c \ucd94\uc815\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc640 \ud68c\uadc0\uc120 \uc0ac\uc774\uc758 \uac70\ub9ac(\uc624\ucc28 \uc81c\uacf1\ud569)\ub97c \ucd5c\uc18c\ud654\ud558\uc5ec \ud68c\uadc0 \ubd84\uc11d\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
2.1 \ucd5c\uc18c \uc790\uc2b9\ubc95\uc758 \uc218\ud559\uc801 \uc6d0\ub9ac<\/h3>\n
OLS \ud68c\uadc0\ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ub429\ub2c8\ub2e4:<\/p>\n
Y = \u03b2\u2080 + \u03b2\u2081X\u2081 + \u03b2\u2082X\u2082 + ... + \u03b2\u2096X\u2096 + \u03b5<\/pre>\n
\uc5ec\uae30\uc11c:<\/p>\n
\n
Y\ub294 \uc885\uc18d \ubcc0\uc218(\ubc18\uc751 \ubcc0\uc218)<\/li>\n
X\ub294 \ub3c5\ub9bd \ubcc0\uc218(\uc124\uba85 \ubcc0\uc218)<\/li>\n
\u03b2\ub294 \ud68c\uadc0 \uacc4\uc218<\/li>\n
\u03b5\ub294 \uc624\ucc28\ud56d<\/li>\n<\/ul>\n
\ud68c\uadc0 \uacc4\uc218 \u03b2\ub97c \ucd94\uc815\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c\uc758 \ube44\uc6a9 \ud568\uc218(\uc624\ucc28 \uc81c\uacf1\ud569)\ub97c \ucd5c\uc18c\ud654\ud574\uc57c \ud569\ub2c8\ub2e4:<\/p>\n
C(\u03b2) = \u03a3(Y\u1d62 - \u0176\u1d62)\u00b2<\/pre>\n
2.2 OLS \ud68c\uadc0\uc758 \uac00\uc815<\/h3>\n
\n
\uc120\ud615\uc131: \ub3c5\ub9bd \ubcc0\uc218\uc640 \uc885\uc18d \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\uac00 \uc120\ud615\uc774\ub2e4.<\/li>\n
\ub3c5\ub9bd\uc131: \uc624\ucc28 \ud56d\uc774 \uc11c\ub85c \ub3c5\ub9bd\uc774\ub2e4.<\/li>\n
\uc815\uaddc\uc131: \uc624\ucc28 \ud56d\uc774 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub978\ub2e4.<\/li>\n
\ub4f1\ubd84\uc0b0\uc131: \uc624\ucc28\uc758 \ubd84\uc0b0\uc774 \uc77c\uc815\ud558\ub2e4.<\/li>\n<\/ul>\n
\uc774\ub7ec\ud55c \uac00\uc815\uc744 \ub9cc\uc871\ud558\ub294 \uacbd\uc6b0, OLS \ud68c\uadc0\ub294 \ucd5c\uace0\uc758 \uc120\ud615 \ubd88\ud3b8 \ucd94\uc815\ub7c9( BLUE: Best Linear Unbiased Estimator)\uc73c\ub85c \uac04\uc8fc\ub429\ub2c8\ub2e4.<\/p>\n
3. statsmodels \ub77c\uc774\ube0c\ub7ec\ub9ac \uc18c\uac1c<\/h2>\n
Python\uc5d0\uc11c \ud68c\uadc0 \ubd84\uc11d\uacfc \ud1b5\uacc4 \ubaa8\ub378\ub9c1\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c statsmodels<\/strong>\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 \uc27d\uace0 \ube60\ub974\uac8c \ub2e4\uc591\ud55c \ud1b5\uacc4 \ubd84\uc11d\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. OLS \ud68c\uadc0 \ubd84\uc11d\uc744 \uc704\ud55c \uac04\ub2e8\ud55c \uad6c\uc870\ub97c \uc81c\uacf5\ud558\uc5ec, \ud6a8\uc728\uc801\uc778 \ubaa8\ub378 \uad6c\ucd95\uacfc \uacb0\uacfc \ud574\uc11d\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
3.1 statsmodels \uc124\uce58\ud558\uae30<\/h3>\n
\uba3c\uc800 statsmodels \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798\uc758 pip \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install statsmodels<\/pre>\n
3.2 \uae30\ubcf8\uc801\uc778 \uc0ac\uc6a9\ubc95<\/h3>\n
statsmodels\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc18c \uc790\uc2b9\ubc95\uc744 \uad6c\ud604\ud558\ub294 \uae30\ubcf8\uc801\uc778 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc784\ud3ec\ud2b8\ud569\ub2c8\ub2e4:<\/p>\n
import pandas as pd\nimport statsmodels.api as sm\n<\/pre>\n
\uc774\ud6c4, \uc608\uc2dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0 OLS \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/p>\n
4. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
OLS \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ud6c8\ub828\uc5d0 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\ub85c \uc0ac\uc6a9\ub418\ub294 \uae08\uc735 \ub370\uc774\ud130\uc14b\uc740 \uc8fc\uac00, \uac70\ub798\ub7c9, \uacbd\uc81c \uc9c0\ud45c \ub4f1\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uac00\uc0c1\uc758 \ub370\uc774\ud130\uc14b\uc744 \uc0dd\uc131\ud558\uc5ec \uc0ac\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import numpy as np\n\n# \ub79c\ub364 \uc2dc\ub4dc \uc124\uc815\nnp.random.seed(42)\n\n# \uac00\uc0c1\uc758 \ub3c5\ub9bd \ubcc0\uc218 \ubc0f \uc885\uc18d \ubcc0\uc218 \uc0dd\uc131\nX = np.random.rand(100, 1) * 10 # 0\ubd80\ud130 10\uae4c\uc9c0\uc758 \uac12\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c4 \ub3c5\ub9bd \ubcc0\uc218\nY = 2.5 * X + np.random.randn(100, 1) * 2 # \uc885\uc18d \ubcc0\uc218\ub294 \ub3c5\ub9bd \ubcc0\uc218\ub97c \uae30\ubc18\uc73c\ub85c \uc0dd\uc131\n<\/pre>\n
5. OLS \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ub370\uc774\ud130\uac00 \uc900\ube44\ub418\uc5c8\uc73c\ub2c8, \uc774\uc81c OLS \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. statsmodels\ub97c \ud65c\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \uacb0\uacfc\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4.<\/p>\n
# \ub3c5\ub9bd \ubcc0\uc218\uc5d0 \uc0c1\uc218\ud56d \ucd94\uac00\nX = sm.add_constant(X)\n\n# OLS \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5\nmodel = sm.OLS(Y, X)\nresults = model.fit()\n\n# \uacb0\uacfc \ucd9c\ub825\nprint(results.summary())\n<\/pre>\n
5.1 \uacb0\uacfc \ud574\uc11d<\/h3>\n
\ubaa8\ub378 \ud559\uc2b5 \ud6c4, summary()<\/code> \uba54\uc18c\ub4dc\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \ud1b5\uacc4 \uc815\ubcf4\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc694 \uc9c0\ud45c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\nR-squared<\/strong>: \ud68c\uadc0 \ubaa8\ub378\uc774 \uc885\uc18d \ubcc0\uc218\ub97c \uc5bc\ub9c8\ub098 \uc798 \uc124\uba85\ud558\ub294\uc9c0\ub97c \ub098\ud0c0\ub0b4\ub294 \uc9c0\ud45c\uc785\ub2c8\ub2e4.<\/li>\n P-values<\/strong>: \uac01 \ud68c\uadc0 \uacc4\uc218\uc758 \ud1b5\uacc4\uc801 \uc720\uc758\uc131\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 0.05 \uc774\ud558\uc758 \uac12\uc740 \uc720\uc758\ubbf8\ud55c \uac83\uc73c\ub85c \uac04\uc8fc\ub429\ub2c8\ub2e4.<\/li>\n Confidence intervals<\/strong>: \ud68c\uadc0 \uacc4\uc218\uac00 \ud2b9\uc815 \uac12\uc73c\ub85c \uc218\ub834\ud560 \uac00\ub2a5\uc131\uc758 \ubc94\uc704\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n6. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc608\uce21<\/h2>\n\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ube44\uad50\ud558\uac70\ub098, \uc794\ucc28 \ubd84\uc11d\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc801\ud569\uc131\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n # \uc608\uce21 \uacb0\uacfc \uacc4\uc0b0\npredictions = results.predict(X)\n\n# \uc794\ucc28 \uacc4\uc0b0\nresiduals = Y - predictions\n<\/pre>\n6.1 \uc794\ucc28 \ubd84\uc11d<\/h3>\n\uc794\ucc28(residual)\ub294 \uc2e4\uc81c \uac12\uacfc \uc608\uce21 \uac12\uc758 \ucc28\uc774\ub85c, \uc774\ub97c \ubd84\uc11d\ud558\uc5ec \ubaa8\ub378\uc758 \uc801\ud569\ub3c4\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc794\ucc28\uac00 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub978\ub2e4\uba74 \ubaa8\ub378\uc774 \uc801\ud569\ud558\ub2e4\uace0 \ud310\ub2e8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc794\ucc28\uc758 \ubd84\ud3ec\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574 \uc2dc\uac01\ud654\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n import matplotlib.pyplot as plt\n\n# \uc794\ucc28 \uc2dc\uac01\ud654\nplt.scatter(predictions, residuals)\nplt.axhline(y=0, color='r', linestyle='--')\nplt.title('\uc794\ucc28 \ubd84\uc11d')\nplt.xlabel('\uc608\uce21\uac12')\nplt.ylabel('\uc794\ucc28')\nplt.show()\n<\/pre>\n7. \uacb0\ub860<\/h2>\n\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc77c\ud658\uc73c\ub85c, statsmodels\ub97c \uc774\uc6a9\ud55c \ucd5c\uc18c \uc790\uc2b9\ubc95 \ud68c\uadc0 \ubd84\uc11d\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. OLS \ud68c\uadc0 \ubaa8\ub378\uc740 \ub2e8\uc21c\ud558\uba74\uc11c\ub3c4 \ub9e4\uc6b0 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c, \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uc608\uce21\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574, \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\ub4e4\uc774 \uac01\uad11\ubc1b\uace0 \uc788\ub294 \uc0c1\ud669\uc785\ub2c8\ub2e4. \uc774\ud6c4 \uac15\uc88c\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uacfc \ud568\uaed8 \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4.<\/p>\n 8. \ucc38\uace0 \uc790\ub8cc<\/h2>\n\nstatsmodels \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n scikit-learn \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n TensorFlow \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\ud000\ud2b8 \ub9e4\ub9e4, \uc989 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud22c\uc790 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\uace0 \uc774\ub97c \uc790\ub3d9\uc73c\ub85c \uc2e4\ud589\ud558\uae30 \uc704\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ubc1c\uc804\ud558\uba74\uc11c, \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0 \uc788\uc5b4 \uae4a\uc774 \uc788\ub294 \uc778\uc0ac\uc774\ud2b8\ub97c \ub3c4\ucd9c\ud574\ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 statsmodels \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc18c \uc790\uc2b9\ubc95(Ordinary Least Squares, OLS) \ud68c\uadc0 \ubd84\uc11d\uc744 \ud1b5\ud574 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, statsmodels\ub97c \ud65c\uc6a9\ud55c \ucd5c\uc18c \uc790\uc2b9\ubc95”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28756","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, statsmodels\ub97c \ud65c\uc6a9\ud55c \ucd5c\uc18c \uc790\uc2b9\ubc95 - 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