OptinMon 30 - Four Critical Steps in Building Linear Regression Models

What It Really Means to Take an Interaction Out of a Model

When you’re model building, a key decision is which interaction terms to include.

As a general rule, the default in regression is to leave them out. Add interactions only with a solid reason. It would seem like data fishing to simply add in all possible interactions.

And yet, that’s a common practice in most ANOVA models: put in all possible interactions and only take them out if there’s a solid reason. Even many software procedures default to creating interactions among categorical predictors.

Simplifying a Categorical Predictor in Regression Models

by Jeff Meyer Leave a Comment

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of this decision is whether to combine categories of a categorical predictor.

The greater the number of parameter estimates in a model the greater the number of observations that are needed to keep power constant. The parameter estimates in a linear [Read more…] about Simplifying a Categorical Predictor in Regression Models

Same Statistical Models, Different (and Confusing) Output Terms

by Jeff Meyer Leave a Comment

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?

I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.

We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.

A Visual Description of Multicollinearity

by Karen Grace-Martin 2 Comments

Multicollinearity is one of those terms in statistics that is often defined in one of two ways:

1. Very mathematical terms that make no sense — I mean, what is a linear combination anyway?

2. Completely oversimplified in order to avoid the mathematical terms — it’s a high correlation, right?

So what is it really? In English?

Recoding a Variable from a Survey Question to Use in a Statistical Model

by Jeff Meyer Leave a Comment

Survey questions are often structured without regard for ease of use within a statistical model.

Take for example a survey done by the Centers for Disease Control (CDC) regarding child births in the U.S. One of the variables in the data set is “interval since last pregnancy”. Here is a histogram of the results.

Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 4 Comments

Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.

When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).

So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.