{"id":1886,"date":"2019-07-04T05:23:27","date_gmt":"2019-07-03T20:23:27","guid":{"rendered":"http:\/\/141.164.34.82\/?p=1886"},"modified":"2019-07-04T05:33:55","modified_gmt":"2019-07-03T20:33:55","slug":"%ec%9d%b8%ea%b3%bc%ea%b4%80%ea%b3%84-%ec%b6%94%ec%a0%95%ec%97%90%ec%84%9c-%eb%aa%a8%ed%98%95-%ec%84%a4%ec%a0%95%ec%9d%98-%ec%a4%91%ec%9a%94%ec%84%b1","status":"publish","type":"post","link":"http:\/\/ds.sumeun.org\/?p=1886","title":{"rendered":"\uc778\uacfc\uad00\uacc4 \ucd94\uc815\uc5d0\uc11c \ubaa8\ud615 \uc124\uc815\uc758 \uc911\uc694\uc131"},"content":{"rendered":"

\uc2ec\uc2a8\uacfc \ubc84\ud06c\uc2a8<\/h1>\n

\uc2ec\uc2a8\uc758 \uc5ed\uc124(Simpson's paradox)<\/h2>\n

R\uc758 datasets::UCBAdmissions<\/code>\uc740 1973\ub144 UC Berkeley\uc758 \ub300\ud559\uc6d0 \uc785\ud559 \uc2dc\ud5d8 \uacb0\uacfc\ub97c \ubcf4\uc5ec \uc900\ub2e4. <\/p>\n

1973\ub144 UC \ubc84\ud074\ub9ac(Berkeley) \ub300\ud559\uc740 \uc131\ucc28\ubcc4\ub85c \uace0\uc18c\ub97c \ub2f9\ud55c\ub2e4. \uc9c0\uc6d0\uc790 \uc911 \ub0a8\uc131\uc740 44%\uac00 \ud569\uaca9\ud588\uc9c0\ub9cc, \uc5ec\uc131\uc740 35%\ub9cc\uc774 \ud569\uaca9\uc744 \ud588\uae30 \ub54c\ubb38\uc774\ub2e4. \ub2e4\uc74c\uc740 \ubc84\ud074\ub9ac \ub300\ud559\uc6d0 \uc785\ud559 \uacb0\uacfc\ub97c \uadf8\ub798\ud504\ub85c \ubcf4\uc5ec\uc8fc\uace0 \uc788\ub2e4. <\/p>\n

# from help(UCBAdmissions)\r\nrequire(graphics)\r\n## Data aggregated over departments\r\napply(UCBAdmissions, c(1, 2), sum)\r\n<\/code><\/pre>\n
##           Gender\r\n## Admit      Male Female\r\n##   Admitted 1198    557\r\n##   Rejected 1493   1278\r\n<\/pre>\n
library(vcd)\r\nmosaic(apply(UCBAdmissions, c(1,2), sum), shade=TRUE, legend=TRUE)\r\n<\/code><\/pre>\n
\"plot<\/p>\n
#mosaicplot(apply(UCBAdmissions, c(1, 2), sum),\r\n<\/code><\/pre>\n
\ub9cc\uc57d \ud559\uacfc\ubcc4\ub85c \uc790\ub8cc\ub97c \ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\ub2e4.<\/p>\n
round(prop.table(ftable(Admit ~ Dept + Gender, UCBAdmissions), margin=1), 2)\r\n<\/code><\/pre>\n
##             Admit Admitted Rejected\r\n## Dept Gender                        \r\n## A    Male             0.62     0.38\r\n##      Female           0.82     0.18\r\n## B    Male             0.63     0.37\r\n##      Female           0.68     0.32\r\n## C    Male             0.37     0.63\r\n##      Female           0.34     0.66\r\n## D    Male             0.33     0.67\r\n##      Female           0.35     0.65\r\n## E    Male             0.28     0.72\r\n##      Female           0.24     0.76\r\n## F    Male             0.06     0.94\r\n##      Female           0.07     0.93\r\n<\/pre>\n
for (idim in 1:dim(UCBAdmissions)[3]) {\r\n  mosaic(UCBAdmissions[,,idim], shade=TRUE, legend=TRUE, main=dimnames(UCBAdmissions)[[3]][idim])  \r\n}\r\n<\/code><\/pre>\n
\"plot\"plot\"plot\"plot\"plot\"plot<\/p>\n
\uc804\uccb4 \uc9c0\uc6d0\uc790\ub97c \ubcf4\uba74 \ub0a8\uc131\uc758 \ud569\uaca9\ub960\uc774 \uc5ec\uc131\uc758 \ud569\uaca9\ub960\ubcf4\ub2e4 \ub192\ub2e4. \ud558\uc9c0\ub9cc \ud559\uacfc\ubcc4\ub85c \ub098\ub220 \ubcf4\uba74 A \ud559\uacfc\uc758 \uacbd\uc6b0 \uc5ec\uc131\uc758 \ud569\uaca9\ub960\uc774 \ub192\uace0, \ub098\uba38\uc9c0 \ud559\uacfc\ub294 \uac70\uc758 \ube44\uc2b7\ud558\ub2e4. \uc5b4\ub5bb\uac8c \uc774\ub7f0 \uc77c\uc774 \uc77c\uc5b4\ub0a0 \uc218 \uc788\ub294\uac00?<\/p>\n
\uacb0\ub860\ubd80\ud130 \ub9d0\ud558\uc790\uba74, \uc131\uc740 \ud559\uacfc \uc120\ud0dd\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\uace0 \ub2e4\uc2dc \ud559\uacfc\uc5d0 \uc758\ud574 \ud569\uaca9\ub960\uc774 \uc601\ud5a5\uc744 \ubc1b\ub294\ub2e4. \uadf8\ub9ac\uace0 \uc5ec\uc131\uc740 \ub0a8\uc131\uc5d0 \ube44\ud574 \ud569\uaca9\ub960\uc774 \ub0ae\uc740 \ud559\uacfc\uc5d0 \ub300\ud55c \uc9c0\uc6d0\uc728\uc774 \ub192\uc558\ub2e4.<\/strong><\/p>\n
\uc0ac\uc2e4 \uc5ec\ub7ec \ubc88 \uc598\uae30\ud574\ub3c4 \uc774\ud574\ud558\uae30 \uc5b4\ub824\uc6b4 \uac1c\ub150\uc774\ub2e4. \ub2e4\uc2dc \ud55c\ubc88 \uc124\uba85\ud574\ubcf4\uc790.<\/p>\n
\uac01 \ud559\uacfc\ubcc4\ub85c \uc0b4\ud3b4\ubcf4\uba74, \uc131\ubcc4\uc5d0 \ub530\ub978 \ud569\uaca9\ub960\uc758 \ucc28\uc774\uac00 \uc5c6\uc5c8\ub2e4. \ud558\uc9c0\ub9cc \uc5ec\uc131\uc758 \uacbd\uc6b0 \ud569\uaca9\ub960\uc774 \ub0ae\uc740 \ud559\uacfc(\uc778\ubb38\ub300, \uc0ac\ud68c\ub300\uc758 \uacbd\uc6b0 \uc815\uc6d0\uc5d0 \ube44\ud574 \uc9c0\uc6d0\uc790\uac00 \ub9ce\uc544 \ud569\uaca9\ub960\uc774 \ub0ae\ub2e4)\uc5d0 \ub300\ud55c \uc9c0\uc6d0\uc774 \ub9ce\uc544\uace0, \ub0a8\uc131\uc758 \uacbd\uc6b0 \ud569\uaca9\ub960\uc774 \ub192\uc740 \ud559\uacfc(\uc790\uc5f0\ub300, \uacf5\ub300 \ub4f1)\uc5d0 \ub300\ud55c \uc9c0\uc6d0\uc774 \ub9ce\ub2e4. \ub530\ub77c\uc11c \ud559\uacfc\ubcc4\ub85c \uc131\uc5d0 \ub530\ub978 \ud569\uaca9\ub960\uc774 \uc5c6\uc5c8\uc9c0\ub9cc, \uc5ec\uc131\uc758 \uacbd\uc6b0 \ud569\uaca9\ub960\uc774 \ub0ae\uc740 \ud559\uacfc\uc5d0 \ub300\ud55c \uc9c0\uc6d0\uc774 \ub9ce\uc544\uc11c \uc804\uccb4 \uc5ec\uc131\uc5d0 \ub300\ud55c \ud569\uaca9\ub960\uc744 \uacc4\uc0b0\ud574\ubcf4\uba74 \ub0a8\uc131\uc5d0 \ube44\ud574 \ub0ae\uc558\ub358 \uac83\uc774\ub2e4.<\/p>\n
\uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uadf8\ub798\ud504\ub85c \uadf8\ub824\ubcfc \uc218\ub3c4 \uc788\ub2e4. \ud559\uacfc\ub97c \uc0dd\uac01\ud558\uc9c0 \uc54a\uace0 \ub0a8\/\ub140\uc640 \ud569\uaca9\ub960\ub9cc \ubcf8\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uc778\uacfc\uad00\uacc4 \ubaa8\ud615\uc744 \uadf8\ub824 \ubcfc \uc218 \uc788\ub2e4. <\/p>\n
\"\uc798\ubabb\ub41c<\/p>\n
\ud558\uc9c0\ub9cc \uc774\ub294 \ud559\uacfc\ub97c \uace0\ub824\ud558\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc774\uba70, \ud559\uacfc\ub97c \ubaa8\ud615\uc5d0 \ucd94\uac00\ud558\uc5ec \uc2e4\uc81c \uc778\uacfc\uad00\uacc4 \ubaa8\ud615\uc744 \uadf8\ub824\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\ub2e4. \ud559\uacfc\ub294 \ud569\uaca9\uc728\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uace0, \uc131\ubcc4\uc774 \ud559\uacfc \uc120\ud0dd\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uc9c0\uba74, \uc131\ubcc4\uc774 \ud569\uaca9\uc728\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uc9c0\ub294 \uc54a\ub294\ub2e4.<\/p>\n
\"\ubc84\ud074\ub9ac\ub300<\/p>\n
\uc774\ub294 \ub0b4\uc0dd\uc131<\/strong>\uc5d0\uc11c\ub3c4 \uc0b4\ud3b4\ubd24\ub4ef\uc774 \ud1b5\uc81c\ubcc0\uc218\ub97c \uc801\uc808\ud788 \ud3ec\ud568\ud558\uc9c0 \uc54a\uc544\uc11c \uc624\ucc28\uc640 \uc124\uba85\ubcc0\uc218\uc5d0 \uc0c1\uad00\uc774 \uc0dd\uacbc\uae30 \ub54c\ubb38\uc774\ub2e4. (\ucc38 \uc778\uacfc\uad00\uacc4 \ubaa8\ud615\uc5d0\uc11c \\(\ud569\uaca9\ub960 = f(0 \\cdot \ub0a8\ub140 + e)\\) \ub77c\uace0 \ud560 \ub54c, \ub0a8\uc790\uc77c \ub54c \\(e\\) \ub294 \uc591\uc218\uc774\uace0, \uc5ec\uc790\uc77c \ub54c \\(e\\) \ub294 \uc74c\uc218\uc774\ub2e4.)<\/p>\n
\ub2e4\uc74c\uc740 R\uc758 glm<\/code>\uc73c\ub85c \ubd84\uc11d\ud55c \uacb0\uacfc\uc774\ub2e4. \ud1b5\uc81c\ubcc0\uc218\ub85c \ubb34\uc5c7\uc744 \ud3ec\ud568\uc2dc\ud0a4\ub290\ub0d0\uc5d0 \ub530\ub77c GenderFemale<\/code>\uc758 \uacc4\uc218\uac00 \ub2ec\ub77c\uc9d0\uc744 \ud655\uc778\ud574\ubcf4\uc790. <\/p>\n
require(tidyr)\r\nUCBAdf <- as.data.frame(UCBAdmissions)\r\ndf <- spread(UCBAdf, key='Admit', value='Freq')\r\nsummary(glm(cbind(Admitted, Rejected) ~ Gender, family=binomial, data=df))\r\n<\/code><\/pre>\n
## \r\n## Call:\r\n## glm(formula = cbind(Admitted, Rejected) ~ Gender, family = binomial, \r\n##     data = df)\r\n## \r\n## Deviance Residuals: \r\n##      Min        1Q    Median        3Q       Max  \r\n## -16.7915   -4.7613   -0.4365    5.1025   11.2022  \r\n## \r\n## Coefficients:\r\n##              Estimate Std. Error z value Pr(>|z|)    \r\n## (Intercept)  -0.22013    0.03879  -5.675 1.38e-08 ***\r\n## GenderFemale -0.61035    0.06389  -9.553  < 2e-16 ***\r\n## ---\r\n## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\r\n## \r\n## (Dispersion parameter for binomial family taken to be 1)\r\n## \r\n##     Null deviance: 877.06  on 11  degrees of freedom\r\n## Residual deviance: 783.61  on 10  degrees of freedom\r\n## AIC: 856.55\r\n## \r\n## Number of Fisher Scoring iterations: 4\r\n<\/pre>\n
summary(glm(cbind(Admitted, Rejected) ~ Gender + Dept, family=binomial, data=df))\r\n<\/code><\/pre>\n
## \r\n## Call:\r\n## glm(formula = cbind(Admitted, Rejected) ~ Gender + Dept, family = binomial, \r\n##     data = df)\r\n## \r\n## Deviance Residuals: \r\n##       1        2        3        4        5        6        7        8  \r\n## -1.2487  -0.0560   1.2533   0.0826   1.2205  -0.2076   3.7189   0.2706  \r\n##       9       10       11       12  \r\n## -0.9243  -0.0858  -0.8509   0.2052  \r\n## \r\n## Coefficients:\r\n##              Estimate Std. Error z value Pr(>|z|)    \r\n## (Intercept)   0.58205    0.06899   8.436   <2e-16 ***\r\n## GenderFemale  0.09987    0.08085   1.235    0.217    \r\n## DeptB        -0.04340    0.10984  -0.395    0.693    \r\n## DeptC        -1.26260    0.10663 -11.841   <2e-16 ***\r\n## DeptD        -1.29461    0.10582 -12.234   <2e-16 ***\r\n## DeptE        -1.73931    0.12611 -13.792   <2e-16 ***\r\n## DeptF        -3.30648    0.16998 -19.452   <2e-16 ***\r\n## ---\r\n## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\r\n## \r\n## (Dispersion parameter for binomial family taken to be 1)\r\n## \r\n##     Null deviance: 877.056  on 11  degrees of freedom\r\n## Residual deviance:  20.204  on  5  degrees of freedom\r\n## AIC: 103.14\r\n## \r\n## Number of Fisher Scoring iterations: 4\r\n<\/pre>\n
#summary(glm(cbind(Admitted, Rejected) ~ Gender * Dept, family=binomial, data=df))\r\n<\/code><\/pre>\n
\uc774\ub54c \ub2e4\uc74c\uacfc \uac19\uc774 \uc624\ud574\ud558\uae30 \uc27d\ub2e4.<\/p>\n
\uadf8\ub807\ub2e4\uba74 \uc778\uacfc \uad00\uacc4\ub97c \ucd94\uc815\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub420 \uc218 \uc788\uc73c\uba74 \ub9ce\uc740 \ud1b5\uc81c \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\uc5ec \ubaa8\ud615\uc744 \uad6c\uc131\ud558\uba74 \ub418\ub294\uac70 \uc544\ub2cc\uac00?<\/strong><\/p>\n
\uc74c.. \ub531\ud788 \uadf8\ub807\uc9c0 \uc54a\ub2e4. \ub2e4\uc74c\uc758 \uc608\ub97c \ubcf4\uc790.<\/p>\n
\ubc84\ud06c\uc2a8\uc758 \uc5ed\uc124(Berkson's paradox)<\/h2>\n
\uc2ec\uc2a8\uc758 \uc5ed\uc124\uc5d0 \uc775\uc219\ud55c \uc0ac\ub78c\ub3c4 \ubc84\ud06c\uc2a8\uc758 \uc5ed\uc124\uc740 \uc0dd\uc18c\ud560 \uc218 \uc788\ub2e4. \ubc84\ud06c\uc2a8\uc758 \uc5ed\uc124\uc740 \ud1b5\uc81c \ubcc0\uc218<\/strong>\uc640 \uc778\uacfc\uad00\uacc4 \ucd94\uc815\uc5d0\uc11c \uc2ec\uc2a8 \uc5ed\uc124\uc758 \uc815\ubc18\ub300 \uc9c0\uc810\uc5d0 \ub193\uc5ec\uc788\ub2e4.<\/p>\n
\uc2ec\uc2a8 \uc5ed\uc124\uc740 \ud1b5\uc81c \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\uc9c0 \uc54a\uc544 \uc0dd\uae30\ub294<\/strong> \uac00\uc9dc \uc778\uacfc \uad00\uacc4\ub97c \ubcf4\uc5ec\uc900\ub2e4\uba74, \ubc84\ud06c\uc2a8\uc758 \uc5ed\uc124\uc740 \ud1b5\uc81c \ubcc0\uc218\ub97c \ud3ec\ud568\ud574\uc11c \uc0dd\uae30\ub294<\/strong> \uac00\uc9dc \uc778\uacfc\uad00\uacc4\ub97c \ubcf4\uc5ec\uc900\ub2e4. <\/p>\n
\ub2e4\uc74c\uc758 \uc608\ub97c \ubcf4\uc790.<\/p>\n
\uc2dd\ub2f9\uc758 \ud3c9\uc810 = \ub9db + \uc778\ud14c\ub9ac\uc5b4<\/h3>\n
\uc2dd\ub2f9\uc758 \ud3c9\uc810\uc740 \ud06c\uac8c \ub450 \uac00\uc9c0 \uc694\uc18c(\ub9db\uacfc \uc778\ud14c\ub9ac\uc5b4)\uc5d0 \ub300\ud55c \ud3c9\uc810\uc758 \ud569\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c4\ub2e4. \ub530\ub77c\uc11c \uc778\uacfc\uad00\uacc4 \ubaa8\ud615\uc740 \ub2e4\uc74c\uacfc \uac19\ub2e4.<\/p>\n
\\[\ud3c9\uc810 = \ub9db + \uc778\ud14c\ub9ac\uc5b4 + e\\]<\/p>\n
\uc774\ub97c \uadf8\ub798\ud504\ub85c \ud45c\ud604\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\ub2e4.<\/p>\n
\"\ud3c9\uc810<\/p>\n
\uc774\ub54c \ub9db\uacfc \uc778\ud14c\ub9ac\uc5b4\ub294 \uc11c\ub85c \uc778\uacfc \uad00\uacc4\uac00 \uc5c6\uae30 \ub54c\ubb38\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc774 \uc778\uacfc\uad00\uacc4 \uacc4\uc218\ub294 \uc720\uc758\ubbf8\ud558\uc9c0 \uc54a\ub2e4. <\/p>\n
N <- 1000\r\ntaste <- rnorm(N)\r\ninterior <- rnorm(N)\r\nscore <- taste + interior + (e=rnorm(N))\r\ndat <- data.frame(taste, interior, score)\r\nsummary(lm(taste ~ interior, data=dat))\r\n<\/code><\/pre>\n
## \r\n## Call:\r\n## lm(formula = taste ~ interior, data = dat)\r\n## \r\n## Residuals:\r\n##      Min       1Q   Median       3Q      Max \r\n## -2.58375 -0.66466  0.00948  0.66956  2.45230 \r\n## \r\n## Coefficients:\r\n##             Estimate Std. Error t value Pr(>|t|)\r\n## (Intercept) -0.02091    0.03014  -0.694    0.488\r\n## interior    -0.01450    0.03093  -0.469    0.639\r\n## \r\n## Residual standard error: 0.9525 on 998 degrees of freedom\r\n## Multiple R-squared:  0.0002203,  Adjusted R-squared:  -0.0007815 \r\n## F-statistic: 0.2199 on 1 and 998 DF,  p-value: 0.6392\r\n<\/pre>\n
\ud558\uc9c0\ub9cc score<\/code>(\ud3c9\uc810)\uc744 \ud1b5\uc81c\ubcc0\uc218\ub97c \ub123\uc5b4 \ud68c\uadc0 \ubd84\uc11d\uc744 \ud558\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 interior<\/code>(\uc778\ud14c\ub9ac\uc5b4)\ub294 taste<\/code>(\ub9db)\uc744 \uac10\uc18c\uc2dc\ud0a4\ub294 \uac83\uc73c\ub85c \ub098\ud0c0\ub09c\ub2e4.<\/p>\n
summary(lm(taste ~ interior + score, data=dat))\r\n<\/code><\/pre>\n
## \r\n## Call:\r\n## lm(formula = taste ~ interior + score, data = dat)\r\n## \r\n## Residuals:\r\n##     Min      1Q  Median      3Q     Max \r\n## -2.0488 -0.4223 -0.0136  0.4515  2.2760 \r\n## \r\n## Coefficients:\r\n##             Estimate Std. Error t value Pr(>|t|)    \r\n## (Intercept) -0.02000    0.02147  -0.932    0.352    \r\n## interior    -0.46642    0.02637 -17.687   <2e-16 ***\r\n## score        0.47923    0.01538  31.162   <2e-16 ***\r\n## ---\r\n## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\r\n## \r\n## Residual standard error: 0.6783 on 997 degrees of freedom\r\n## Multiple R-squared:  0.4935, Adjusted R-squared:  0.4925 \r\n## F-statistic: 485.8 on 2 and 997 DF,  p-value: < 2.2e-16\r\n<\/pre>\n
\"\ub9db<\/p>\n
\uc774\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc740 \ub2e4\uc74c\uacfc \uac19\ub2e4. \ud3c9\uc810\uc774 \uac19\uc740 \uacbd\uc6b0, \uc778\ud14c\ub9ac\uc5b4\uac00 \ub192\ub2e4\uba74, \ub9db\uc740 \ub0ae\uc544\uc9c0\uac8c \ub9c8\ub828\uc774\ub2e4. \uc774\ub294 \uc608\uce21\ubaa8\ud615\uc73c\ub85c\ub294 \ud6cc\ub96d\ud558\ub2e4. \ud3c9\uc810\uc774 \uc778\ud14c\ub9ac\uc5b4\uc640 \ub9db \uc810\uc218\uc758 \ud569\uc774\uae30 \ub54c\ubb38\uc5d0 \uc911\uac04 \ud3c9\uc810\uc758 \uc2dd\ub2f9\uc740 (\uc911 \uc778\ud14c\ub9ac\uc5b4, \uc911 \ub9db), (\uace0 \uc778\ud14c\ub9ac\uc5b4, \uc800 \ub9db), (\uc800 \uc778\ud14c\ub9ac\uc5b4, \uace0 \ub9db) \uc911\uc758 \ud558\ub098\uc77c \uac83\uc774\ub2e4. \uc774\ub7f0 \uc0c1\ud669(\ud3c9\uc810\uc744 \ud1b5\uc81c\ud55c \uc0c1\ud0dc)\uc5d0\uc11c \uc778\ud14c\ub9ac\uc5b4\uac00 \ub0ae\uc744 \uc218\ub85d \ub9db\uc774 \uc88b\ub2e4\uace0 \uae30\ub300\ud560 \uc218 \uc788\ub2e4.<\/p>\n
\uc5ec\uce5c : “\uc790\uae30\uc57c, \uc774 \uc2dd\ub2f9\uc740 \ud3c9\uc810\uc740 \uc5c4\uccad \ub192\uc740\ub370 \uc778\ud14c\ub9ac\uc5b4\ub294 \ubcc4\ub85c\ub124. \ub2e4\ub978 \uc2dd\ub2f9 \uac08\uae4c?”<\/p>\n
\uc774\uae00\uc744 \uc77d\uc740 \ub0a8\uce5c : “\ubc84\ud06c\uc2a8\uc758 \uc5ed\uc124\uc774\ub77c\uace0 \uc788\uc5b4. \uc5ec\uae34 \uc544\ub9c8 \ub9db\uc774 \ub05d\ub0b4\uc904\uaebc\uc57c!”<\/p>\n
\ub9e4\uac1c \ud6a8\uacfc \ubaa8\ud615<\/h2>\n
\uc774\ub7f0 \ubb38\uc81c\ub294 \ub9e4\uac1c \ud6a8\uacfc \ubaa8\ud615\uc5d0\uc11c\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\uc774\ub2e4. \ub9e4\uac1c\ubcc0\uc218\ub9cc\uc744 \ub123\uc5b4 \ud68c\uadc0\ubd84\uc11d\uc744 \ud558\uba74 \ub9e4\uac1c \ubcc0\uc218\uc758 \uc778\uacfc\uad00\uacc4 \uacc4\uc218\ub97c \uacfc\ub300 \ub610\ub294 \uacfc\uc18c \ucd94\uc815\ud558\uac8c \ub418\uace0, \ub3c5\ub9bd\ubcc0\uc218\uc5d0 \ub9e4\uac1c\ubcc0\uc218\ub97c \ud1b5\uc81c\ubcc0\uc218\ub85c \ub193\uc5b4 \ud68c\uadc0\ubd84\uc11d\uc744 \ud558\uba74 \ucd1d\ud6a8\uacfc\uac00 \uc544\ub2c8\ub77c \uc9c1\uc811\ud6a8\uacfc(\ucd1d\ud6a8\uacfc-\uac04\uc811\ud6a8\uacfc)\ub9cc\uc744 \ucd94\uc815\ud558\uac8c \ub41c\ub2e4. <\/p>\n
\uc774\ub7f0 \uc5f0\uc720\ub85c \ub9ce\uc740 \uacbd\uc81c\ud559 \ub17c\ubb38\ub4e4\uc774 \uc5ec\ub7ec \ubcc0\uc218\ub97c \ud1b5\uc81c\ubcc0\uc218\ub85c \ub123\uc5b4\uc11c \ubd84\uc11d\ud574\ubcf4\uae30\ub3c4 \ud558\uace0, \ube7c\ub193\uace0 \ud574\ubcf4\uae30\ub3c4 \ud558\uba74\uc11c \uac00\uc815<\/strong>\uc5d0 \ub530\ub77c \uacb0\ub860\uc774 \uc5b4\ub5bb\uac8c \ub2ec\ub77c\uc9c0\ub294 \uc0b4\ud3b4\ubcf8\ub2e4. \ud558\uc9c0\ub9cc \uac1c\uc778\uc801\uc73c\ub85c \uadf8\ub807\uac8c \ud1b5\uc81c \ubcc0\uc218\ub97c \uc120\ud0dd\ud558\ub294 \uac83\ub9cc\uc73c\ub85c \uc778\uacfc \uad00\uacc4\ub97c \uc815\ud655\ud558\uac8c \ucd94\uc815\ud560 \uc218 \uc788\uc744\uc9c0\ub294 \uc758\ubb38\uc774\ub2e4. \uc77c\ub2e8 \ud1b5\uc81c \ubcc0\uc218\ub85c \ud3ec\ud568\uc2dc\ud0a4\uace0 \uc548 \ud558\uace0\ub294 \uc774\ub860\uc801 \ubc30\uacbd\uc5d0 \uc758\ud574 \ub2ec\ub77c\uc838\uc57c \ud558\ub294\ub370 \uadf8\ub7f0 \uac83\uc740 \ubcc4\ub85c \uc5c6\uace0, \ud1b5\uc81c\ub418\uc5b4\uc57c \ud558\uc9c0\ub9cc \ud1b5\uc81c\ub418\uc9c0 \ubabb\ud55c(\ud639\uc740 \uad00\ucc30\ub418\uc9c0 \ubabb\ud55c) \ubcc0\uc218\uac00 \uc788\ub2e4\uba74 \uacb0\ub860\uc740 \ub2e4\uc2dc \ubc14\ub014 \uc218 \uc788\uc73c\uba70, \ud1b5\uc81c\ubcc0\uc218\uc640 \uacb0\uacfc \ubcc0\uc218\uc758 \uad00\uacc4\uac00 \uc120\ud615\uc801\uc774\ub77c\ub294 \ubcf4\uc7a5\ub3c4 \uc5c6\uc73c\uba70…<\/p>\n
\uacb0\ub860<\/h2>\n
\ud1b5\uc81c \ubcc0\uc218\ub97c \ub9ce\uc774 \ub123\ub294\ub2e4\uace0 \uc88b\uc740 \uac83\uc740 \uc544\ub2c8\ub2e4.<\/strong><\/p>\n
\uc608\uce21 \ubaa8\ud615\uc774\ub77c\uba74 \uacfc\uc801\ud569\uc774 \uc77c\uc5b4\ub098\uc9c0 \uc54a\ub294 \ud55c\ub3c4\uc5d0\uc11c \uc124\uba85 \ubcc0\uc218\ub97c \ub9ce\uc774 \ub123\uc5b4\uc11c \ub098\uc060 \uac83\uc740 \uc5c6\uc9c0\ub9cc,<\/p>\n
\uc778\uacfc\uad00\uacc4 \ubaa8\ud615\uc774\ub77c\uba74 \ud1b5\uc81c \ubcc0\uc218\uc5d0 \ub530\ub77c \uacb0\uacfc\uac00 \ubc14\ub014 \uc218\ub3c4 \uc788\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
\uc2ec\uc2a8\uacfc \ubc84\ud06c\uc2a8 \uc2ec\uc2a8\uc758 \uc5ed\uc124(Simpson's paradox) R\uc758 datasets::UCBAdmissions\uc740 1973\ub144 UC Berkeley\uc758 \ub300\ud559\uc6d0 \uc785\ud559 \uc2dc\ud5d8 \uacb0\uacfc\ub97c \ubcf4\uc5ec \uc900\ub2e4. 1973\ub144 UC \ubc84\ud074\ub9ac(Berkeley) \ub300\ud559\uc740 \uc131\ucc28\ubcc4\ub85c \uace0\uc18c\ub97c \ub2f9\ud55c\ub2e4. \uc9c0\uc6d0\uc790 \uc911 \ub0a8\uc131\uc740 44%\uac00 \ud569\uaca9\ud588\uc9c0\ub9cc, \uc5ec\uc131\uc740 35%\ub9cc\uc774 \ud569\uaca9\uc744 \ud588\uae30 \ub54c\ubb38\uc774\ub2e4. \ub2e4\uc74c\uc740 \ubc84\ud074\ub9ac \ub300\ud559\uc6d0 \uc785\ud559 \uacb0\uacfc\ub97c \uadf8\ub798\ud504\ub85c \ubcf4\uc5ec\uc8fc\uace0 \uc788\ub2e4. # from help(UCBAdmissions) require(graphics) ## Data aggregated over departments apply(UCBAdmissions, c(1, 2), sum) ## […]<\/p>\n","protected":false},"author":1,"featured_media":1884,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[146,319],"tags":[354,351,353,352,337],"jetpack_featured_media_url":"http:\/\/ds.sumeun.org\/wp-content\/uploads\/2019\/07\/Simpson_Dia02.png","_links":{"self":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1886"}],"collection":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1886"}],"version-history":[{"count":2,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1886\/revisions"}],"predecessor-version":[{"id":1888,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1886\/revisions\/1888"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/media\/1884"}],"wp:attachment":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1886"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1886"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}