You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?
Regression models
Member Training: Preparing to Use (and Interpret) a Linear Regression Model
November 1st, 2020 by TAF Support
What It Really Means to Remove an Interaction From a Model
September 17th, 2020 by Karen Grace-Martin
When you’re model building, a key decision is which interaction terms to include. And which interactions to remove.
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.
Member Training: Explaining Logistic Regression Results to Non-Researchers
August 1st, 2020 by TAF Support
Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?
Simplifying a Categorical Predictor in Regression Models
January 14th, 2020 by Jeff Meyer
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 (more…)
Same Statistical Models, Different (and Confusing) Output Terms
January 7th, 2020 by Jeff Meyer
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.
Confusing Statistical Term #8: Odds
December 10th, 2019 by Karen Grace-Martin
Odds is confusing in a different way than some of the other terms in this series.
First, it’s a bit of an abstract concept, which I’ll explain below.
But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.
I found this incorrect definition recently in a (non-statistics) book: (more…)