http://www.commres.net/ 2026-04-17T16:58:40+00:00 http://www.commres.net/ http://www.commres.net/_media/wiki/logo.png text/html 2024-05-12T23:58:22+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2024/schedule/w10.lecture.note?rev=1715558302&do=diff R code set.seed(401) sn <- 25 x <- rnorm(sn, 100, 10) x y <- 1.4 * x + 2 + rnorm(sn, 0, 10) y df <- data.frame(x, y) # density graph ggplot(data=df, aes(y)) + geom_histogram() + geom_vline(aes(xintercept=mean(y)), color="red", linetype="dashed", size=1) + coord_flip() ggplot(data=df, aes(y)) + geom_density(color="blue", size=1.5) + geom_vline(aes(xintercept=mean(y)), color="red", linetype="dashed", size=1) + coord_flip() lm.mod <- lm(y~x, data=df) sum… text/html 2024-05-12T23:59:16+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2024/schedule/w11.lecture.note?rev=1715558356&do=diff 하나의 독립변인이 갖는 고유의 영향력 혹은 설명력 파악하기 multiple regression 참조 partial and semipartial correlation 참조 datavar <- read.csv("http://commres.net/wiki/_media/regression01-bankaccount.csv") datavar colnames(datavar) <- c("y", "x1", "x2") datavar attach(datavar) library(ggplot2) # ggplot 사용을 위해서 # install.packages("ggplot2") lm.y.x1 <- lm(y~x1, data=datavar) summary(lm.y.x1) str(lm.y.x1) intercept.y <- lm.y.x1$coefficients[1] slope.x <- lm.y.x1$coefficients[2] intercept.y slope.x ggplot(data=datavar, … text/html 2025-04-06T22:42:30+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2024/schedule/week06_t-test_and_anova_note?rev=1743979350&do=diff R # see http://commres.net/wiki/r/t-test 문서 for detailed info # ?t.test for help # t.test(A, mu) # one sample t-test # t.test(A, B, var.equal = T) # two sample t-test # t.test(pre, post, paired=TRUE) # paired sample t-test (repeated measure)