The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
by David Lillis, Ph.D.
Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command.
Just as we did last time, we perform the regression using lm(). This time we store it as an object M. [Read more…] about Linear Models in R: Improving Our Regression Model
A normal probability plot is extremely useful for testing normality assumptions. It’s more precise than a histogram, which can’t pick up subtle deviations, and doesn’t suffer from too much or too little power, as do tests of normality.
There are two versions of normal probability plots: Q-Q and P-P. I’ll start with the Q-Q. [Read more…] about Anatomy of a Normal Probability Plot
Here’s a little reminder for those of you checking assumptions in regression and ANOVA:
The assumptions of normality and homogeneity of variance for linear models are not about Y, the dependent variable. (If you think I’m either stupid, crazy, or just plain nit-picking, read on. This distinction really is important). [Read more…] about Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables