{"id":2457,"date":"2023-07-22T04:29:06","date_gmt":"2023-07-22T04:29:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/python%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8\/"},"modified":"2023-07-22T04:29:06","modified_gmt":"2023-07-22T04:29:06","slug":"python%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/python%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8\/","title":{"rendered":"Python\uc5d0\uc11c \uc774\ubcc0\ub7c9 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95: \uc608\uc81c \ud3ec\ud568"},"content":{"rendered":"

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\uc774\ubcc0\ub7c9 \ubd84\uc11d\uc774\ub77c\ub294<\/strong> \uc6a9\uc5b4\ub294 \ub450 \ubcc0\uc218\uc5d0 \ub300\ud55c \ubd84\uc11d\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc811\ub450\uc0ac “bi”\uac00 “\ub458”\uc744 \uc758\ubbf8\ud558\uae30 \ub54c\ubb38\uc5d0 \uc774\uac83\uc744 \uae30\uc5b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774\ubcc0\ub7c9 \ubd84\uc11d\uc758 \ubaa9\ud45c\ub294 \ub450 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc774\ubcc0\ub7c9 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \uc138 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

1.<\/strong> \ud3ec\uc778\ud2b8 \ud074\ub77c\uc6b0\ub4dc<\/span><\/p>\n

2.<\/strong> \uc0c1\uad00\uacc4\uc218<\/span><\/p>\n

3.<\/strong> \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0<\/span><\/p>\n

\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub450 \uac00\uc9c0 \ubcc0\uc218( (1)<\/strong> \uacf5\ubd80\uc5d0 \uc18c\ube44\ud55c \uc2dc\uac04 \ubc0f (2)<\/strong> 20\uba85\uc758 \ud559\uc0dd\uc774 \uc5bb\uc740 \uc2dc\ud5d8 \uc810\uc218)\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c pandas DataFrame\uc744 \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c \uc774\ub7ec\ud55c \uac01 \uc720\ud615\uc758 \uc774\ubcc0\ub7c9 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 1, 1, 2, 2, 2, 3, 3, 3, 3,\n                             3, 4, 4, 5, 5, 6, 6, 6, 7, 8],\n                   ' score<\/span> ': [75, 66, 68, 74, 78, 72, 85, 82, 90, 82,\n                             80, 88, 85, 90, 92, 94, 94, 88, 91, 96]})\n\n#view first five rows of DataFrame\n<\/span>df. head<\/span> ()\n\n\thours score\n0 1 75\n1 1 66\n2 1 68\n3 2 74\n4 2 78<\/strong><\/pre>\n
 1. \ud3ec\uc778\ud2b8 \ud074\ub77c\uc6b0\ub4dc<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \uc2dc\ud5d8 \uacb0\uacfc\uc758 \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#create scatterplot of hours vs. score<\/span>\nplt. scatter<\/span> (df. hours<\/span> , df. score<\/span> )\nplt. title<\/span> (' Hours Studied vs. Exam Score<\/span> ')\nplt. xlabel<\/span> (' Hours Studied<\/span> ')\nplt. ylabel<\/span> (' Exam Score<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 x\ucd95\uc740 \uacf5\ubd80\ud55c \uc2dc\uac04\uc744 \ub098\ud0c0\ub0b4\uace0 y\ucd95\uc740 \uc2dc\ud5d8\uc5d0\uc11c \uc5bb\uc740 \uc131\uc801\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uadf8\ub798\ud504\ub294 \ub450 \ubcc0\uc218 \uc0ac\uc774\uc5d0 \uae0d\uc815\uc801\uc778 \uad00\uacc4\uac00 \uc788\uc74c\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ud559\uc2b5 \uc2dc\uac04\uc774 \uc99d\uac00\ud560\uc218\ub85d \uc2dc\ud5d8 \uc810\uc218\ub3c4 \uc99d\uac00\ud558\ub294 \uacbd\ud5a5\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 2. \uc0c1\uad00\uacc4\uc218<\/strong><\/span><\/h3>\n
 \ud53c\uc5b4\uc2a8 \uc0c1\uad00 \uacc4\uc218\ub294 \ub450 \ubcc0\uc218 \uac04\uc758 \uc120\ud615 \uad00\uacc4\ub97c \uc218\ub7c9\ud654\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas\uc5d0\uc11c corr()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c1\uad00 \ud589\ub82c\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #create correlation matrix\n<\/span>df. corr<\/span> ()\n\n\thours score\nhours 1.000000 0.891306\nscore 0.891306 1.000000<\/strong><\/pre>\n
 \uc0c1\uad00\uacc4\uc218\ub294 0.891<\/strong> \ub85c \ub098\ud0c0\ub0ac\ub2e4. \uc774\ub294<\/span> \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \uc2dc\ud5d8 \uc131\uc801 \uc0ac\uc774\uc5d0 \uac15\ud55c \uc591\uc758 \uc0c1\uad00\uad00\uacc4\uac00 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 3. \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0<\/strong><\/span><\/h3>\n
 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub294 \ub450 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc218\ub7c9\ud654\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud1b5\uacc4 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 statsmodels \ud328\ud0a4\uc9c0\uc758 OLS()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uacf5\ubd80\ud55c \uc2dc\uac04\uacfc \ubc1b\uc740 \uc2dc\ud5d8 \uacb0\uacfc\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \ube60\ub974\uac8c \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df[' score<\/span> ']\n\n#define explanatory variable\n<\/span>x = df[[' hours<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.794\nModel: OLS Adj. R-squared: 0.783\nMethod: Least Squares F-statistic: 69.56\nDate: Mon, 22 Nov 2021 Prob (F-statistic): 1.35e-07\nTime: 16:15:52 Log-Likelihood: -55,886\nNo. Observations: 20 AIC: 115.8\nDf Residuals: 18 BIC: 117.8\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 69.0734 1.965 35.149 0.000 64.945 73.202\nhours 3.8471 0.461 8.340 0.000 2.878 4.816\n==================================================== ============================\nOmnibus: 0.171 Durbin-Watson: 1.404\nProb(Omnibus): 0.918 Jarque-Bera (JB): 0.177\nSkew: 0.165 Prob(JB): 0.915\nKurtosis: 2.679 Cond. No. 9.37\n==================================================== ============================\n<\/strong><\/pre>\n
 \uc801\ud569 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2dc\ud5d8 \uc810\uc218 = 69.0734 + 3.8471*(\uacf5\ubd80\ud55c \uc2dc\uac04)<\/span><\/p>\n
 \uc774\ub294 \uacf5\ubd80\ud55c \uc2dc\uac04\uc774 \ucd94\uac00\ub420 \ub54c\ub9c8\ub2e4 \uc2dc\ud5d8 \uc810\uc218\uac00 \ud3c9\uade0 3.8471<\/strong> \uc99d\uac00\ud558\ub294 \uac83\uacfc \uad00\ub828\uc774 \uc788\uc74c\uc744 \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \ub610\ud55c \uc801\ud569 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd1d \uacf5\ubd80 \uc2dc\uac04\uc744 \uae30\uc900\uc73c\ub85c \ud559\uc0dd\uc774 \ubc1b\uac8c \ub420 \uc810\uc218\ub97c \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, 3\uc2dc\uac04 \ub3d9\uc548 \uacf5\ubd80\ud55c \ud559\uc0dd\uc740 81.6147<\/strong> \uc810\uc744 \ubc1b\uc544\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n