http://www.commres.net/ 2026-04-17T13:35:52+00:00 http://www.commres.net/ http://www.commres.net/_media/wiki/logo.png text/html 2024-05-12T23:54:15+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2023/schedule/w10.lecture.note?rev=1715558055&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 2025-05-19T06:35:26+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2023/schedule/w11.lecture.note?rev=1747636526&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 2023-05-24T00:01:21+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2023/schedule/w12.lecture.note?rev=1684886481&do=diff w11.lecture.note + partial and semipartial correlation simple regression 에서 slope test 부분 text/html 2024-04-07T23:42:35+00:00 Anonymous (anonymous@undisclosed.example.com) http://www.commres.net/c/ms/2023/schedule/week06_t-test_and_anova_note?rev=1712533355&do=diff R # t-test 이해 확인 pre <- c(3,0,6,7,4,3,2,8,4) post <- c(5,2,5,7,10,9,7,11,8) mean(pre) mean(post) sd(pre) sd(post) diff.prepost <- pre-post mean.diff <- mean(diff.prepost) mean.diff sd.diff <- sd(diff.prepost) sd.diff # # remember t test = diff / rand error # se <- sd.diff/sqrt(length(diff.prepost)) se t.value <- mean.diff/se t.value df.value <- length(diff.prepost)-1 df.value # pt function is for getting percentage of # t score with df value pt(t.value, df=df.val…