{"id":2116,"date":"2023-07-23T15:01:59","date_gmt":"2023-07-23T15:01:59","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%aa%e1%86%ab%e1%84%87%e1%85%a7%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%80%e1%85%a9%e1%86%bc%e1%84%89%e1%85%a5%e1%86%ab%e1%84%89%e1%85%a5\/"},"modified":"2023-07-23T15:01:59","modified_gmt":"2023-07-23T15:01:59","slug":"%e1%84%8b%e1%85%aa%e1%86%ab%e1%84%87%e1%85%a7%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%80%e1%85%a9%e1%86%bc%e1%84%89%e1%85%a5%e1%86%ab%e1%84%89%e1%85%a5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%aa%e1%86%ab%e1%84%87%e1%85%a7%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc%e1%84%80%e1%85%a9%e1%86%bc%e1%84%89%e1%85%a5%e1%86%ab%e1%84%89%e1%85%a5\/","title":{"rendered":"\uc644\uc804 \ub2e4\uc911\uacf5\uc120\uc131\uc774\ub780 \ubb34\uc5c7\uc778\uac00\uc694? (\uc815\uc758 \ubc0f \uc608)"},"content":{"rendered":"

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\ud1b5\uacc4\uc5d0\uc11c \ub2e4\uc911 \uacf5\uc120\uc131\uc740<\/a> \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc11c\ub85c \ub192\uc740 \uc0c1\uad00\uad00\uacc4\ub97c \uac16\uace0 \uc788\uc5b4 \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \uace0\uc720\ud558\uac70\ub098 \ub3c5\ub9bd\uc801\uc778 \uc815\ubcf4\ub97c \uc81c\uacf5\ud558\uc9c0 \uc54a\uc744 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\ubcc0\uc218 \uac04\uc758 \uc0c1\uad00 \uc815\ub3c4\uac00 \ucda9\ubd84\ud788 \ub192\uc73c\uba74 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uace0 \ud574\uc11d\ud560 \ub54c \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc911\uacf5\uc120\uc131\uc758 \uac00\uc7a5 \uadf9\ub2e8\uc801\uc778 \uacbd\uc6b0\ub97c \uc644\uc804 \ub2e4\uc911\uacf5\uc120\uc131<\/strong> \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \uc774\ub294 \ub450 \uac1c \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc11c\ub85c \uc815\ud655\ud55c \uc120\ud615 \uad00\uacc4\ub97c \uac00\uc9c8 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uac00 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"\"<\/p>\n

\uc608\uce21 \ubcc0\uc218 x 2<\/sub> \uc758 \uac12\uc740 \ub2e8\uc21c\ud788 x 1<\/sub> \uc758 \uac12\uc5d0 2\ub97c \uacf1\ud55c \uac12\uc774\ub77c\ub294 \uc810\uc5d0 \uc720\uc758\ud558\uc138\uc694.<\/span> <\/p>\n

\"\uc644\ubcbd\ud55c<\/p>\n

\uc774\ub294 \uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc758<\/strong> \uc608\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc644\uc804 \ub2e4\uc911\uacf5\uc120\uc131\uc758 \ubb38\uc81c<\/strong><\/span><\/h2>\n

\ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc644\ubcbd\ud55c \ub2e4\uc911 \uacf5\uc120\uc131\uc774 \uc788\ub294 \uacbd\uc6b0 \uc77c\ubc18 \ucd5c\uc18c \uc81c\uacf1\ubc95\uc740 \ud68c\uadc0 \uacc4\uc218 \ucd94\uc815\uce58\ub97c \uc0dd\uc131\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc2e4\uc81c\ub85c, \ub2e4\ub978 \uc608\uce21 \ubcc0\uc218(x 2<\/sub> )\ub97c \uc77c\uc815\ud558\uac8c \uc720\uc9c0\ud558\uba74\uc11c \uc751\ub2f5 \ubcc0\uc218(y)\uc5d0 \ub300\ud55c \uc608\uce21 \ubcc0\uc218(x 1<\/sub> )\uc758 \ud55c\uacc4 \ud6a8\uacfc\ub97c \ucd94\uc815\ud558\ub294 \uac83\uc740 \ubd88\uac00\ub2a5\ud569\ub2c8\ub2e4. \uc65c\ub0d0\ud558\uba74 x 1 \uc774<\/sub> \uc6c0\uc9c1\uc77c \ub54c \ud56d\uc0c1 x 2 \uac00<\/sub> \uc815\ud655\ud558\uac8c \uc6c0\uc9c1\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc989, \uc644\ubcbd\ud55c \ub2e4\uc911 \uacf5\uc120\uc131\uc740 \ud68c\uadc0 \ubaa8\ub378\uc758 \uac01 \uacc4\uc218\uc5d0 \ub300\ud55c \uac12\uc744 \ucd94\uc815\ud558\ub294 \uac83\uc744 \ubd88\uac00\ub2a5\ud558\uac8c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n

\uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc744 \ub2e4\ub8e8\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n

\uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc744 \ucc98\ub9ac\ud558\ub294 \uac00\uc7a5 \uac04\ub2e8\ud55c \ubc29\ubc95\uc740 \ub2e4\ub978 \ubcc0\uc218\uc640 \uc815\ud655\ud55c \uc120\ud615 \uad00\uacc4\ub97c \uac16\ub294 \ubcc0\uc218 \uc911 \ud558\ub098\ub97c \uc81c\uac70\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\ub97c \ub4e4\uc5b4 \uc774\uc804 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c\ub294 \uc608\uce21 \ubcc0\uc218\uc778 x 2\ub97c<\/sub> \uac04\ub2e8\ud788 \uc81c\uac70\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"\"<\/p>\n

\uadf8\ub7f0 \ub2e4\uc74c x 1\uc744<\/sub> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 y\ub97c \uc751\ub2f5 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc758 \uc608<\/strong><\/span><\/h2>\n

\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc644\ubcbd\ud55c \ub2e4\uc911 \uacf5\uc120\uc131\uc758 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \uc138 \uac00\uc9c0 \uc2dc\ub098\ub9ac\uc624\ub97c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

1. \uc608\uce21 \ubcc0\uc218\ub294 \ub2e4\ub978 \uc608\uce21 \ubcc0\uc218\uc758 \ubc30\uc218\uc785\ub2c8\ub2e4.<\/strong><\/span><\/h3>\n

\ud2b9\uc815 \uc885\uc758 \ub3cc\uace0\ub798\uc758 \uccb4\uc911\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 “\uc13c\ud2f0\ubbf8\ud130 \ub2e8\uc704\uc758 \ud0a4”\uc640 “\ubbf8\ud130 \ub2e8\uc704\uc758 \ud0a4”\ub97c \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub370\uc774\ud130 \uc138\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"\"<\/p>\n

“\uc13c\ud2f0\ubbf8\ud130 \ub2e8\uc704\uc758 \ub192\uc774” \uac12\uc740 \ub2e8\uc21c\ud788 “\ubbf8\ud130 \ub2e8\uc704\uc758 \ub192\uc774”\uc5d0 100\uc744 \uacf1\ud55c \uac12\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc644\ubcbd\ud55c \ub2e4\uc911 \uacf5\uc120\uc131\uc758 \uacbd\uc6b0\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uace0 \ud558\uba74 \uc608\uce21 \ubcc0\uc218 “\ubbf8\ud130”\uc5d0 \ub300\ud55c \uacc4\uc218 \ucd94\uc815\uce58\ub97c \uc0dd\uc131\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 #define data\n<\/span>df <- data. frame<\/span> (weight=c(400, 460, 470, 475, 490, 440, 430, 490, 500, 540),\n                 m=c(1.3, .7, .6, 1.3, 1.2, 1.5, 1.2, 1.6, 1.1, 1.4),\n                 cm=c(130, 70, 60, 130, 120, 150, 120, 160, 110, 140))\n\n#fit multiple linear regression model\n<\/span>model <- lm(weight~m+cm, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = weight ~ m + cm, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-70,501 -25,501 5,183 19,499 68,590 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 458,676 53,403 8,589 2.61e-05 ***\nm 9.096 43.473 0.209 0.839    \ncm NA NA NA NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 41.9 on 8 degrees of freedom\nMultiple R-squared: 0.005442, Adjusted R-squared: -0.1189 \nF-statistic: 0.04378 on 1 and 8 DF, p-value: 0.8395<\/strong><\/pre>\n
 2. \uc608\uce21\ubcc0\uc218\ub294 \ub2e4\ub978 \uc608\uce21\ubcc0\uc218\uc758 \ubcc0\ud658\ub41c \ubc84\uc804\uc785\ub2c8\ub2e4.<\/strong><\/span><\/h3>\n
 \ub18d\uad6c \uc120\uc218\uc758 \ub4f1\uae09\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 “\ud3ec\uc778\ud2b8”\uc640 “\uc870\uc815 \ud3ec\uc778\ud2b8”\ub97c \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubcc0\uc218 “scaled points”\uac00 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub41c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2a4\ucf00\uc77c\ub9c1\ub41c \ud3ec\uc778\ud2b8 = (\ud3ec\uc778\ud2b8 \u2013 \u03bc \ud3ec\uc778\ud2b8<\/sub> ) \/ \u03c3 \ud3ec\uc778\ud2b8<\/sub><\/span><\/p>\n
 \ub370\uc774\ud130 \uc138\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
\"\"<\/p>\n
 \uac01 “\uc870\uc815\ub41c \ud3ec\uc778\ud2b8” \uac12\uc740 \ub2e8\uc21c\ud788 “\ud3ec\uc778\ud2b8”\uc758 \ud45c\uc900\ud654\ub41c \ubc84\uc804\uc785\ub2c8\ub2e4. \uc774\ub294 \uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc758 \uacbd\uc6b0\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uace0 \ud558\uba74 “\uc870\uc815\ub41c \uc810” \uc608\uce21 \ubcc0\uc218\uc5d0 \ub300\ud55c \uacc4\uc218 \ucd94\uc815\uce58\ub97c \uc0dd\uc131\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define data\n<\/span>df <- data. frame<\/span> (rating=c(88, 83, 90, 94, 96, 78, 79, 91, 90, 82),\n                 pts=c(17, 19, 24, 29, 33, 15, 14, 29, 25, 22))\n\ndf$scaled_pts <- (df$pts - mean(df$pts)) \/ sd(df$pts)\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating~pts+scaled_pts, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ pts + scaled_pts, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.4932 -1.3941 -0.2935 1.3055 5.8412 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 67.4218 3.5896 18.783 6.67e-08 ***\npts 0.8669 0.1527 5.678 0.000466 ***\nscaled_pts NA NA NA NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.953 on 8 degrees of freedom\nMultiple R-squared: 0.8012, Adjusted R-squared: 0.7763 \nF-statistic: 32.23 on 1 and 8 DF, p-value: 0.0004663\n<\/strong><\/pre>\n
 3. \ub354\ubbf8\ubcc0\uc218 \ud2b8\ub7a9<\/strong><\/span><\/h3>\n
 \uc644\ubcbd\ud55c \ub2e4\uc911 \uacf5\uc120\uc131\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \ub610 \ub2e4\ub978 \uc2dc\ub098\ub9ac\uc624\ub294 \ub354\ubbf8 \ubcc0\uc218 \ud2b8\ub7a9<\/a> \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. \ud68c\uadc0 \ubaa8\ub378\uc5d0\uc11c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uac00\uc838\uc640 \uc774\ub97c 0, 1, 2 \ub4f1\uc758 \uac12\uc744 \ucde8\ud558\ub294 “\ub354\ubbf8 \ubcc0\uc218”\ub85c \ubcc0\ud658\ud558\ub824\ub294 \uacbd\uc6b0\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uc18c\ub4dd\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 \uc608\uce21 \ubcc0\uc218 “\ub098\uc774”\uc640 “\uacb0\ud63c \uc0c1\ud0dc”\ub97c \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
\"\"<\/p>\n
 “\uacb0\ud63c \uc5ec\ubd80”\ub97c \uc608\uce21\ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\ub824\uba74 \uba3c\uc800 \uc774\ub97c \ub354\ubbf8\ubcc0\uc218\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub807\uac8c \ud558\ub824\uba74 \uac00\uc7a5 \uc790\uc8fc \ubc1c\uc0dd\ud558\ubbc0\ub85c “Single”\uc744 \uae30\ubcf8 \uac12\uc73c\ub85c \ub450\uace0 \ub2e4\uc74c\uacfc \uac19\uc774 “Married” \ubc0f “Divorce”\uc5d0 0 \ub610\ub294 1\uc758 \uac12\uc744 \ud560\ub2f9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
\"\"<\/p>\n
 \uc2e4\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc138 \uac1c\uc758 \uc0c8\ub85c\uc6b4 \ub354\ubbf8 \ubcc0\uc218\ub97c \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span> <\/p>\n
\"\"<\/p>\n
 \uc774 \uacbd\uc6b0 “\ubbf8\ud63c” \ubcc0\uc218\ub294 “\uae30\ud63c” \ubcc0\uc218\uc640 “\uc774\ud63c” \ubcc0\uc218\uc758 \uc644\ubcbd\ud55c \uc120\ud615 \uc870\ud569\uc785\ub2c8\ub2e4. \uc774\ub294 \uc644\ubcbd\ud55c \ub2e4\uc911\uacf5\uc120\uc131\uc758 \uc608\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uace0 \ud558\uba74 \uac01 \uc608\uce21 \ubcc0\uc218\uc5d0 \ub300\ud55c \uacc4\uc218 \ucd94\uc815\uce58\ub97c \uc0dd\uc131\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define data\n<\/span>df <- data. frame<\/span> (income=c(45, 48, 54, 57, 65, 69, 78, 83, 98, 104, 107),\n                 age=c(23, 25, 24, 29, 38, 36, 40, 59, 56, 64, 53),\n                 single=c(1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0),\n                 married=c(0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1),\n                 divorced=c(0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0))\n\n#fit multiple linear regression model\n<\/span>model <- lm(income~age+single+married+divorced, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = income ~ age + single + married + divorced, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-9.7075 -5.0338 0.0453 3.3904 12.2454 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 16.7559 17.7811 0.942 0.37739   \nage 1.4717 0.3544 4.152 0.00428 **\nsingle -2.4797 9.4313 -0.263 0.80018   \nmarried NA NA NA NA   \ndivorced -8.3974 12.7714 -0.658 0.53187   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.391 on 7 degrees of freedom\nMultiple R-squared: 0.9008, Adjusted R-squared: 0.8584 \nF-statistic: 21.2 on 3 and 7 DF, p-value: 0.0006865\n<\/strong><\/pre>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ud68c\uadc0 \ubd84\uc11d\uc758 \ub2e4\uc911 \uacf5\uc120\uc131\uacfc VIF\uc5d0 \ub300\ud55c \uac00\uc774\ub4dc<\/a>
 R\uc5d0\uc11c VIF\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c VIF\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Excel\uc5d0\uc11c VIF\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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