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…)
logistic regression
Member Training: Interactions in Poisson and Logistic Regression
May 1st, 2023 by TAF SupportThe Difference Between the Bernoulli and Binomial Distributions
February 8th, 2023 by Kim Love
You might already be familiar with the binomial distribution. It describes the scenario where the result of an observation is binary—it can be one of two outcomes. You might label the outcomes as “success” and “failure” (or not!). (more…)
Member Training: Multinomial Logistic Regression
December 30th, 2022 by TAF Support
Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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Guidelines for writing up three types of odds ratios
December 6th, 2021 by Karen Grace-MartinOdds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care. (more…)
Logistic Regression Analysis: Understanding Odds and Probability
November 22nd, 2021 by Karen Grace-MartinUpdated 11/22/2021
Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome.
People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent.
How Odds and Probability Differ
They measure the same thing on different scales. Imagine how confusing it would be if people used degrees Celsius and degrees Fahrenheit interchangeably. “It’s going to be 35 degrees today” could really make you dress the wrong way.
In measuring the likelihood of any outcome, we need to know (more…)
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…)