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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# Logistic Regression

## When Linear Models Don’t Fit Your Data, Now What?

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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## Guidelines for writing up three types of odds ratios

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. [Read more…] about Guidelines for writing up three types of odds ratios

## Logistic Regression Analysis: Understanding Odds and Probability

*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 [Read more…] about Logistic Regression Analysis: Understanding Odds and Probability

## Member Training: Goodness of Fit Statistics

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?

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## Member Training: Explaining Logistic Regression Results to Non-Researchers

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?

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