Dear All I have data in the following format: three columns with X_i, Y_i and SE (Y_i), for i from 1 to 1000. X is independent variable, Y is explained variable (a measurement of some sort) and SE(Y) is standard error of measurement of Y, it's different for each Y_i. I'm stuck with the following statistics questions on using R and on statistics in general: I can do anova(lm(Y~X)) to get an estimate of how much variability in Y is explained by X and how much is in the residuals. But now I want to do weighted linear fit MyLm<-lm(Y~X, weights=1/SE(Y) ^2 ). I think this is a right thing to do, because my Y_i have errors on them, and the errors vary greatly for different Y_i. So, if I do that, how do I get a "fair" estimate of explained and unexplained variability? Since ANOVA doesn't know about weights (or does it?) what should I use? Thank you, Vlad. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._