Thanks greatly -- exactly answers my questions. Tats "Paige Miller" <[EMAIL PROTECTED]> wrote in message news:[EMAIL PROTECTED] > 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 >
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