What do you do if the assumptions of linear models are violated?
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Poisson and Negative Binomial Regression Models
Member Training: Linear Model Assumption Violations: What’s Next?
June 30th, 2023 by guest contributerMember Training: Interactions in Poisson and Logistic Regression
May 1st, 2023 by TAF SupportInteractions 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 Linear Models Don’t Fit Your Data, Now What?
June 20th, 2022 by Karen Grace-MartinWhen 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.
Why Generalized Linear Models Have No Error Term
June 22nd, 2021 by Karen Grace-MartinEven 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…)
Member Training: Goodness of Fit Statistics
March 4th, 2021 by TAF Support
What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?
The Importance of Including an Exposure Variable in Count Models
November 19th, 2020 by Jeff MeyerWhen 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.