outcome variable

Member Training: Logistic Regression for Count and Proportion Data

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.

Member Training: Zero Inflated Models

by Karen Grace-Martin Leave a Comment

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.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Incorporating Graphs in Regression Diagnostics with Stata

by Jeff Meyer Leave a Comment

by Jeff Meyer

You put a lot of work into preparing and cleaning your data. Running the model is the moment of excitement.

You look at your tables and interpret the results. But first you remember that one or more variables had a few outliers. Did these outliers impact your results? [Read more…] about Incorporating Graphs in Regression Diagnostics with Stata