logistic regression

Member Training: Generalized Linear Models

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.

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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 6 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?

The Difference Between Logistic and Probit Regression

by Karen Grace-Martin 17 Comments

One question that seems to come up pretty often is:

What is the difference between logistic and probit regression?

Well, let’s start with how they’re the same:

Both are types of generalized linear models. This means they have this form:

What Is an ROC Curve?

by Karen Grace-Martin 2 Comments

An incredibly useful tool in evaluating and comparing predictive models is the ROC curve.

Its name is indeed strange. ROC stands for Receiver Operating Characteristic. Its origin is from sonar back in the 1940s. ROCs were used to measure how well a sonar signal (e.g., from an enemy submarine) could be detected from noise (a school of fish).

ROC curves are a nice way to see how any predictive model can distinguish between the true positives and negatives. [Read more…] about What Is an ROC Curve?