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