logistic regression

How to Decide Between Multinomial and Ordinal Logistic Regression Models

A great tool to have in your statistical tool belt is logistic regression.

It comes in many varieties and many of us are familiar with the variety for binary outcomes.

But multinomial and ordinal varieties of logistic regression are also incredibly useful and worth knowing.

They can be tricky to decide between in practice, however. In some — but not all — situations you [Read more…] about How to Decide Between Multinomial and Ordinal Logistic Regression Models

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.

Member Training: Generalized Linear Models

by guest Leave a Comment

In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).

For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.

[Read more…]

Member Training: Logistic Regression for Count and Proportion Data

by Karen Grace-Martin Leave a Comment

Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.

There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.

When to Use Logistic Regression for Percentages and Counts

by Karen Grace-Martin 2 Comments

One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.

For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.

Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the [Read more…] about When to Use Logistic Regression for Percentages and Counts

Member Training: Marginal Means, Your New Best Friend

by Jeff Meyer Leave a Comment

Interpreting regression coefficients can be tricky, especially when the model has interactions or categorical predictors (or worse – both).

But there is a secret weapon that can help you make sense of your regression results: marginal means.

They’re not the same as descriptive stats. They aren’t usually included by default in our output. And they sometimes go by the name LS or Least-Square means.

And they’re your new best friend.

So what are these mysterious, helpful creatures?

What do they tell us, really? And how can we use them?