Count Regression Models

Like continuous variables, count variable are numerical.  So it seems you should be able to apply methods for continuous data to count data.

After all, a mean makes sense for both.

But there are differences in the types of distributions that count and continuous variables tend to follow, and these differences can actually make the results quite different.

It’s generally a better idea to apply models that assume count data.  Get more info on these kinds of models here.

Online Workshops

Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models

The Craft of Statistical Analysis Free Webinars

Poisson and Negative Binomial Regression for Count Data

Statistical Speaking Trainings

Zero Inflated Models

Making Sense of Statistical Distributions

Generalized Linear Models

Types of Regression Models and When to Use Them

Articles at The Analysis Factor

About Poisson and Negative Binomial Regression

Poisson Regression Analysis for Count Data

Differences Between the Normal and Poisson Distributions

Poisson and Negative Binomial Regression for Count Data

Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

Two-Way Tables and Count Models: Expected and Predicted Counts

Understanding Incidence Rate Ratios through the Eyes of a Two-Way Table

The Exposure Variable in Poisson Regression Models

Zero-Inflated Poisson Models for Count Outcomes

When Can Count Data be Considered Continuous?

Interpreting Regression Coefficients in Models other than Ordinary Linear Regression

Count Models: Understanding the Log Link Function

Issues with Truncated Data

Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model

About Count Models in the Context of Generalized Linear Models

Confusing Statistical Term #7: GLM

Generalized Linear Models in R, Part 6: Poisson Regression for Count Variables

Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression

Five Extensions of the General Linear Model

How to Combine Complicated Models with Tricky Effects

When Dependent Variables Are Not Fit for Linear Models, Now What?

6 Types of Dependent Variables that will Never Meet the GLM Normality Assumption

Interpreting Regression Coefficients in Models other than Ordinary Linear Regression