beta regression

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.

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Proportion and percentage data are tricky to analyze.

Much like count data, they look like they should work in a linear model.

They’re numerical.  They’re often continuous.

And sometimes they do work.  Some proportion data do look normally distributed so estimates and p-values are reasonable.

But more often they don’t. So estimates and p-values are a mess.  Luckily, there are other options. (more…)

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