Dummy Coded

In a simple linear regression model, how the constant (a.k.a., intercept) is interpreted depends upon the type of predictor (independent) variable.

If the predictor is categorical and dummy-coded, the constant is the mean value of the outcome variable for the reference category only. If the predictor variable is continuous, the constant equals the predicted value of the outcome variable when the predictor variable equals zero.

Removing the Constant When the Predictor Is Categorical

When your predictor variable X is categorical, the results are logical. Let’s look at an example. [Read more…] about The Impact of Removing the Constant from a Regression Model: The Categorical Case

Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression

by Jeff Meyer 42 Comments

The concept of a statistical interaction is one of those things that seems very abstract. Obtuse definitions, like this one from Wikipedia, don’t help:

In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.

First, we know this is true because we read it on the internet! Second, are you more confused now about interactions than you were before you read that definition? [Read more…] about Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression