Member Training: Zero Inflated Models

A common situation with count outcome variables is there are a lot of zero values.  The Poisson distribution used for modeling count variables takes into account that zeros are often the most common value, but sometimes there are even more zeros than the Poisson distribution can account for.

This can happen in continuous variables as well–most of the distribution follows a beautiful normal distribution, except for the big stack of zeros.

This webinar will explore two ways of modeling zero-inflated data: the Zero Inflated model and the Hurdle model. Both assume there are two different processes: one that affects the probability of a zero and one that affects the actual values, and both allow different sets of predictors for each process.

We’ll explore these models as well as some related models, like Zero-One Inflated Beta models for proportion data.

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.