I recently held a free webinar in our The Craft of Statistical Analysis program about Binary, Ordinal, and Nominal Logistic Regression.
It was a record crowd and we didn’t get through everyone’s questions, so I’m answering some here on the site. They’re grouped by topic, and you will probably get more out of it if you watch the webinar recording. It’s free.
The following questions refer to this logistic regression model: (more…)
Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.
This includes favorites like:
That’s a lot of models.
If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require (more…)
One question that seems to come up pretty often is:
Well, let’s start with how they’re the same:
Both are types of generalized linear models. This means they have this form:
An incredibly useful tool in evaluating and comparing predictive models is the ROC curve.
Its name is indeed strange. ROC stands for Receiver Operating Characteristic. Its origin is from sonar back in the 1940s. ROCs were used to measure how well a sonar signal (e.g., from an enemy submarine) could be detected from noise (a school of fish).
ROC curves are a nice way to see how any predictive model can distinguish between the true positives and negatives. (more…)
Relative Risk and Odds Ratios are often confused despite being unique concepts. Why?
Well, both measure association between a binary outcome variable and a continuous or binary predictor variable. (more…)
There are not a lot of statistical methods designed just for ordinal variables.
But that doesn’t mean that you’re stuck with few options. There are more than you’d think. (more…)
