The most important skill in regression is to RECOGNIZE the intercept. It sounds trivial, and is, until you start including interactions between terms. The numbe

The most important skill in regression is to RECOGNIZE the intercept. It sounds trivial, and is, until you start including interactions between terms. The number of times I've found a young graduate student screw this up... Take a simple linear model involving a test score, their age in years (age range 7-16 years), and a binary categorical variable autism diagnosis (0=control,1=autism): score = age + diagnosis + age:diagnosis score = (X1)age + (X2)diagnosis + (X3)age:diagnosis. If the X2 is significant, the naive student would say, "look a group difference!!", not realizing this is the predicted group difference at the intercept, which is when participants were 0 years old. [[ You center age by the mean, or median, or better yet, the age you are most interested in. Once interactions are in the equation, all "lower order" parameter estimates are in reference to the intercept.]] They might also note a significant effect of age, and then assume it applies to both groups, but the parameter X1 only tells you what the predicted slope is for the reference group (controls), while the interaction tests if the age slopes differ between groups...moreover, even if the interaction isn't significant, the age effect in the autism group might not significantly differ from zero...the data is in the wish washy zone, and you have to be careful in how one interprets the data. To some here all this will seem obvious, but to many, getting their head firmly into the conditional space of parameters when their are interaction terms takes work. (note: for now I am ignoring other ways of coding groups (grand mean vs one group being the reference) but the lesson still remains. Understand what the intercept means and to whom/what it refers.