Okey Laboratory wrote: > I'm a biology student working to apply ANOVA models to microarray data. I > wanted to brush up on my statistics before doing that, and I started back at > linear modelling. Hopefully this isn't too obvious a question that I should > have picked up in my reading somewhere.... I'm currently working through > Practical Regression and ANOVA using R by Faraway (it's free, thankfully). > I tried to google this but didn't come up with much. > > My confusion comes from considering the coefficients of a linear-model, say > in R. What you get after fitting the model is a table of: > variable estimate standard error t-value Pr(>|t|) > > I understand that the t-value is the ratio of the estimate to the standard > error. After that, I'm lost though: > a) How do I interpret +ve vs. -ve t-values?
Positive versus negative t-values ... no particular meaning other than the regression coefficient is either positive or negative. > b) What do I make of the Pr(>|t|)? This is the p-value of an hypothesis test in which the null hypothesis is that the coefficient is zero. Basically, if you want an alpha risk of 0.05, if the p-value is smaller than .05, then you would reject the null hypothesis, and accept the alternative hypothesis, that the coefficient is not zero. If this doesn't make a lot of sense to you, then you need to brush up on hypothesis testing. -- Paige Miller [EMAIL PROTECTED] http://www.kodak.com "It's nothing until I call it!" -- Bill Klem, NL Umpire "When you get the choice to sit it out or dance, I hope you dance" -- Lee Ann Womack . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
