OptinMon 04 - Interpreting Linear Regression Coefficients

Linear Regression for an Outcome Variable with Boundaries

The following statement might surprise you, but it’s true.

To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is:

Continuous
Unbounded
Measured on an interval or ratio scale

The normality assumption is about the errors in the model, which have the same distribution as Y|X. It’s absolutely possible to have a skewed distribution of Y and a normal distribution of errors because of the effect of X. [Read more…] about Linear Regression for an Outcome Variable with Boundaries

What is a Confounding Variable?

by Karen Grace-Martin Leave a Comment

By Karen Grace-Martin

Confounding variable is one of those statistical terms that confuses a lot of people. Not because it represents a confusing concept, but because of how it’s used. (Well, it’s a bit of a confusing concept, but that’s not the worst part).

First, it has slightly different meanings to different types of researchers. The definition is essentially the same, but the research context can have specific implications for how that definition plays out.

If the person you’re talking to has a different understanding of what it means, you’re going to have a confusing conversation.

Let’s take a look at some examples to unpack this.

Using Predicted Means to Understand Our Models

by Jeff Meyer

The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.

It’s understandable that this can happen. We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.

Your Questions Answered from the Interpreting Regression Coefficients Webinar

by Karen Grace-Martin Leave a Comment

Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.

As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)

We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar

Should I Specify a Model Predictor as Categorical or Continuous?

by Karen Grace-Martin Leave a Comment

Predictor variables in statistical models can be treated as either continuous or categorical.

Usually, this is a very straightforward decision.

Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.

Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.

But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.

Using Marginal Means to Explain an Interaction to a Non-Statistical Audience

by Jeff Meyer 8 Comments

Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.

For example, say you are going to speak to a group of dieticians. They are interested [Read more…] about Using Marginal Means to Explain an Interaction to a Non-Statistical Audience