continuous predictor

Centering a Covariate to Improve Interpretability

Centering a covariate –a continuous predictor variable–can make regression coefficients much more interpretable. That’s a big advantage, particularly when you have many coefficients to interpret. Or when you’ve included terms that are tricky to interpret, like interactions or quadratic terms.

For example, say you had one categorical predictor with 4 categories and one continuous covariate, plus an interaction between them.

First, you’ll notice that if you center your covariate at the mean, there is [Read more…] about Centering a Covariate to Improve Interpretability

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.

A Strategy for Converting a Continuous to a Categorical Predictor

by Jeff Meyer Leave a Comment

At times it is necessary to convert a continuous predictor into a categorical predictor. For example, income per household is shown below.

This data is censored, all family income above $155,000 is stated as $155,000. A further explanation about censored and truncated data can be found here. It would be incorrect to use this variable as a continuous predictor due to its censoring.

A Useful Graph for Interpreting Interactions between Continuous Variables

by Jeff Meyer 3 Comments

What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?

Let’s start by looking at a model without an interaction. In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.

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.

Segmented Regression for Non-Constant Relationships

by Jeff Meyer 2 Comments

by Jeff Meyer

We often have a continuous predictor in a model that we believe has non-constant relationship with the dependent variable along the predictor’s range. But how can we be certain? What is the best way to measure this?

Sometimes including a quadratic term will capture the change in the slope as we move from the bottom of the range to the top of the range. But a quadratic term only works in two situations:

The rate of change increases and then at some point decreases, or:
The opposite happens – the rate of change decreases and at some point increases.

We could also create a categorical variable. Each category within the categorical variable would represent a specific range within the continuous variable. [Read more…] about Segmented Regression for Non-Constant Relationships