You put a lot of work into preparing and cleaning your data. Running the model is the moment of excitement.
You look at your tables and interpret the results. But first you remember that one or more variables had a few outliers. Did these outliers impact your results? (more…)
Why is it we can model non-linear effects in linear regression?
What the heck does it mean for a model to be “linear in the parameters?” (more…)
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…)
by David Lillis, Ph.D.
Last time we created two variables and added a best-fit regression line to our plot of the variables. Here are the two variables again. (more…)
Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people. (more…)
Not too long ago, a client asked for help with using Spotlight Analysis to interpret an interaction in a regression model.
Spotlight Analysis? I had never heard of it.
As it turns out, it’s a (snazzy) new name for an old way of interpreting an interaction between a continuous and a categorical grouping variable in a regression model. (more…)