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Hi, I have something like this: > x <- 1:10 > y2 <- 30+5*x+rnorm(x,sd=3) > y <- c(y1,y2) > x <- c(x,x) > plot(x,y) > x <- 1:10 > y1 <- 1+5*x+rnorm(x,sd=2) > y2 <- 30+5*x+rnorm(x,sd=5) > y <- c(y1,y2) > x <- c(x,x) > f <- factor(rep(c("a","b"),c(10,10))) > m <- lm(y~x+f) > anova(m) Analysis of Variance

(Sorry for obvious mistakes, as I am quite a newby with no Statistics background). My question is going to be what is the gain of logistic regression over odds ratios when none of the input variables is continuous. My experiment: Outcome: ordinal scale, ``quality'' (QUA=1,2,3) Predictors: ``segment'' (SEG) and ``stress'' (STR). SEG is nominal scale with 24

Dear R users My data set is e > names(e) [1] "yearctry" "discent" "age" "gender" "gemeduc" "gemhhinc" "ref_group" "fearfail_ref" "knowent_ref" "nbgoodc_ref" [11] "nbstatus_ref" "estbbuso_ref" "lngdp" "lngdpsq"

Dear all, is there anyone who can help me extracting p-values from an Anova object from the car library? I can't seem to locate the p-values using str(result) or str(summary(result)) in the example below > A <- factor( rep(1:2,each=3) ) > B <- factor( rep(1:3,times=2) ) > idata <- data.frame(A,B) > fit <- lm( cbind(a1_b1,a1_b2,a1_b3,a2_b1,a2_b2,a2_b3) ? sex,

Dear R users I have a data set which has five variables. One depenedent variable y, and 4 Independent variables (education-level, householdincome, countrygdp and countrygdpsquare). The first two are data corresponding to the individual and the next two coorespond to the country to which the individual belongs to. My data set does not make this distinction between individual level and country

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~