# logistic regression

### Member Training: Linear Model Assumption Violations: What’s Next?

June 30th, 2023 by What do you do if the assumptions of linear models are violated?
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### Member Training: Interactions in Poisson and Logistic Regression

May 1st, 2023 by 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…)

### The Difference Between the Bernoulli and Binomial Distributions

February 8th, 2023 by 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 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

Odds 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

Updated 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…)