{"id":1324,"date":"2023-07-26T21:04:15","date_gmt":"2023-07-26T21:04:15","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%89%e1%85%ae-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1\/"},"modified":"2023-07-26T21:04:15","modified_gmt":"2023-07-26T21:04:15","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%89%e1%85%ae-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b1%e1%84%89%e1%85%ae-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1\/","title":{"rendered":"Python\uc5d0\uc11c \ubd84\uc704\uc218 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc120\ud615 \ud68c\uadc0\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218<\/a> \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc77c\ubc18\uc801\uc73c\ub85c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud560 \ub54c \uc751\ub2f5 \ubcc0\uc218\uc758 \ud3c9\uade0 \uac12\uc744 \ucd94\uc815\ud558\ub824\uace0 \ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uadf8\ub7ec\ub098 \ub300\uc2e0 \ubd84\uc704\uc218 \ud68c\uadc0<\/strong> \ub77c\ub294 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec 70\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218, 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218, 98\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218 \ub4f1\uacfc \uac19\uc740 \ubc18\uc751 \uac12\uc758 \ubd84\uc704\uc218<\/em> \ub610\ub294 \ubc31\ubd84\uc704\uc218 \uac12\uc744 \ucd94\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c \ubd84\uc704\uc218 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \ub85c\ub4dc<\/strong><\/span><\/h3>\n

\uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\uc640 \ud568\uc218\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\nimport<\/span> statsmodels. api<\/span> as<\/span> sm\nimport<\/span> statsmodels. formula<\/span> . api<\/span> as<\/span> smf\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n<\/strong><\/pre>\n
 2\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h3>\n
 \uc774 \uc608\uc5d0\uc11c\ub294 \ud55c \ub300\ud559\uad50\uc5d0\uc11c 100\uba85\uc758 \ud559\uc0dd\uc744 \ub300\uc0c1\uc73c\ub85c \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \uc5bb\uc740 \uc2dc\ud5d8 \uacb0\uacfc\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n
 #make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset\n<\/span>obs = 100\n\nhours = np. random<\/span> . uniform<\/span> (1, 10, obs)\nscore = 60 + 2*hours + np. random<\/span> . normal<\/span> (loc=0, scale=.45*hours, size=100)\n\ndf = pd. DataFrame<\/span> ({' hours<\/span> ':hours, ' score<\/span> ':score})\n\n#view first five rows\n<\/span>df. head<\/span> ()\n\nhours score\n0 5.939322 68.764553\n1 7.436704 77.888040\n2 6.424870 74.196060\n3 5.903949 67.726441\n4 4.812893 72.849046<\/strong><\/pre>\n
 3\ub2e8\uacc4: \ubd84\uc704\uc218 \ud68c\uadc0 \uc218\ud589<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c, \ud559\uc2b5 \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 \ubd84\uc704\uc218 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud654\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc6b0\ub9ac\ub294 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uacf5\ubd80\ud55c \uc2dc\uac04\uc744 \uae30\uc900\uc73c\ub85c \uc2dc\ud5d8 \uc810\uc218\uc758 \uc608\uc0c1 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #fit the model<\/span>\nmodel = smf. quantreg<\/span> ('score~hours', df). fit<\/span> (q= 0.9<\/span> )\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                         QuantReg Regression Results                          \n==================================================== ============================\nDept. Variable: Pseudo R-squared score: 0.6057\nModel: QuantReg Bandwidth: 3.822\nMethod: Least Squares Sparsity: 10.85\nDate: Tue, 29 Dec 2020 No. Observations: 100\nTime: 15:41:44 Df Residuals: 98\n                                        Model: 1\n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nIntercept 59.6104 0.748 79.702 0.000 58.126 61.095\nhours 2.8495 0.128 22.303 0.000 2.596 3.103\n==================================================== ============================<\/strong><\/span><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \ucd94\uc815\ub41c \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2dc\ud5d8 \uc810\uc218\uc758 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218 = 59.6104 + 2.8495*(\uc2dc\uac04)<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, 8\uc2dc\uac04 \uacf5\ubd80\ud55c \ubaa8\ub4e0 \ud559\uc0dd\uc758 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218 \uc810\uc218\ub294 82.4\uc5ec\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2dc\ud5d8 \uc810\uc218\uc758 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218 = 59.6104 + 2.8495*(8) = 82.4<\/strong> .<\/span><\/p>\n
 \ucd9c\ub825\uc5d0\ub294 \uc608\uce21 \ubcc0\uc218\uc758 \uc808\ud3b8\uacfc \uc2dc\uac04\uc5d0 \ub300\ud55c \uc2e0\ub8b0 \uc0c1\ud55c\uacfc \ud558\ud55c\ub3c4 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 4\ub2e8\uacc4: \uacb0\uacfc \uc2dc\uac01\ud654<\/strong><\/span><\/h3>\n
 \uadf8\ub798\ud504\uc5d0 \uacb9\uccd0\uc9c4 \uc801\ud569 \ubd84\uc704\uc218 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0b0\uc810\ub3c4\ub97c \uc0dd\uc131\ud558\uc5ec \ud68c\uadc0 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #define figure and axis\n<\/span>fig, ax = plt.subplots(figsize=(8, 6))\n\n#get y values\n<\/span>get_y = lambda<\/span> a, b: a + b * hours\ny = get_y( model.params<\/span> [' Intercept<\/span> '], model.params<\/span> [' hours<\/span> '])\n\n#plot data points with quantile regression equation overlaid\n<\/span>ax. plot<\/span> (hours, y, color=' black<\/span> ')\nax. scatter<\/span> (hours, score, alpha=.3)\nax. set_xlabel<\/span> (' Hours Studied<\/span> ', fontsize=14)\nax. set_ylabel<\/span> (' Exam Score<\/span> ', fontsize=14)\n<\/strong><\/span><\/pre>\n
\"Python\uc758<\/p>\n
 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\uc120\uacfc \ub2ec\ub9ac \uc774 \uc801\ud569\uc120\uc740 \ub370\uc774\ud130\uc5d0 \ub300\ud55c “\ucd5c\uc801 \uc801\ud569\uc120”\uc744 \ub098\ud0c0\ub0b4\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ub300\uc2e0 \uc608\uce21 \ubcc0\uc218\uc758 \uac01 \uc218\uc900\uc5d0\uc11c \ucd94\uc815\ub41c 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218\ub97c \ud1b5\uacfc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 Python\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc120\ud615 \ud68c\uadc0\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud560 \ub54c \uc751\ub2f5 \ubcc0\uc218\uc758 \ud3c9\uade0 \uac12\uc744 \ucd94\uc815\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ub300\uc2e0 \ubd84\uc704\uc218 \ud68c\uadc0 \ub77c\ub294 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec 70\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218, 90\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218, 98\ubc88\uc9f8 \ubc31\ubd84\uc704\uc218 \ub4f1\uacfc \uac19\uc740 \ubc18\uc751 \uac12\uc758 \ubd84\uc704\uc218 \ub610\ub294 \ubc31\ubd84\uc704\uc218 \uac12\uc744 \ucd94\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \ud568\uc218\ub97c […]<\/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-1324","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \ubd84\uc704\uc218 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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