
You’ll be excited to hear we’re doing another Statistics Skills Accelerator for our Statistically Speaking members: Count Models.
Stats Skills Accelerators are structured events focused on an important topic. They feature Stat’s Amore Trainings in a suggested order, as well as
live Q&As specific to the Accelerator.
In August, our mentors will be running a new Accelerator. The first Q&A is August 6, 2025 at 3 pm ET, hosted by Jeff Meyer.
Count models are used when the outcome variable in a model or group comparison is a discrete count:
- Number of eggs in a clutch
- Number of days in intensive care
- Number of aggressive incidents in detention
Count models come in a few types, and any of these can also be used for rates:
- Poisson Regression is the simplest and is the basis for all the other models, but its assumptions are rarely met with real data.
- Negative Binomial regression adds an extra parameter to a Poisson regression measure the extra variance that often occurs in real data.
- Truncated count models work when the lowest values (often just zero) cannot occur. This happens when a count has to occur in order to be part of the population of interest.
- Zero inflated count models are used when there are more zeros than expected. For this model, some zeros could have been something else and others couldn’t.
- Hurdle models also work when there are more zeros than expected, but the process of having a zero is different. In these models, there is an actual “hurdle” one has to pass in order to have a non-zero count.
- Logistic regression, when your count is out of of maximum number.
In this accelerator, learn about the different types of count models, how to understand their results, how to apply them to rates, and how to choose among them.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and is a combination of watching recorded trainings and live events.
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What do you do if the assumptions of linear models are violated?
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Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)
When your dependent variable is not continuous, unbounded, and measured on
an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
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Even if you’ve never heard the term Generalized Linear Model, you may have run one. It’s a term for a family of models that includes logistic and Poisson regression, among others.
It’s a small leap to generalized linear models, if you already understand linear models. Many, many concepts are the same in both types of models.
But one thing that’s perplexing to many is why generalized linear models have no error term, like linear models do. (more…)
When our outcome variable is the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. A few examples are: Poisson, negative binomial, zero-inflated Poisson and truncated negative binomial.
There are specific requirements for which count model to use. The models are not interchangeable. But regardless of the model we use, there is a very important prerequisite that they all share.
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