Q-Q plot

Linear Models in R: Improving Our Regression Model

April 23rd, 2015 by

by David Lillis, Ph.D.Stage 2

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. (more…)

Anatomy of a Normal Probability Plot

April 7th, 2014 by

Stage 2A 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.   (more…)

Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables

April 10th, 2009 by

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). (more…)