Hi guys, I ran the below R code:
> department <- c(rep("B", 2), rep("C", 2), rep("D", 2), rep("E", 2), rep("F", 2)) > gender <- rep(c("Male", "Female"), 5) > admitted <- c(353, 17, 120, 202, 138, 131, 53, 94, 22, 24) > not.admitted <- c(207, 8, 205, 391, 279, 244, 138, 299, 351, 317) > cbind(department, gender, admitted, not.admitted) department gender admitted not.admitted [1,] "B" "Male" "353" "207" [2,] "B" "Female" "17" "8" [3,] "C" "Male" "120" "205" [4,] "C" "Female" "202" "391" [5,] "D" "Male" "138" "279" [6,] "D" "Female" "131" "244" [7,] "E" "Male" "53" "138" [8,] "E" "Female" "94" "299" [9,] "F" "Male" "22" "351" [10,] "F" "Female" "24" "317" > gender <- factor(gender) > department <- factor(department) > y <- cbind(admitted, not.admitted) > fit1 <- glm(y ~ department + gender, family = binomial) > summary(fit1) Call: glm(formula = y ~ department + gender, family = binomial) Deviance Residuals: 1 2 3 4 5 6 7 8 9 10 -0.1191 0.5680 0.5239 -0.3914 -0.5164 0.5440 0.6868 -0.4892 -0.5024 0.5158 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.51349 0.11936 4.302 1.69e-05 *** departmentC -1.14008 0.12188 -9.354 < 2e-16 *** departmentD -1.19456 0.11984 -9.968 < 2e-16 *** departmentE -1.61308 0.13928 -11.581 < 2e-16 *** departmentF -3.20527 0.17880 -17.927 < 2e-16 *** genderMale 0.03069 0.08676 0.354 0.724 Can someone tell me how should I intercept these coefficients given that the dependent variable Y is y <- cbind(admitted, not.admitted) Thanks [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.