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…)
Ah, logarithms. They were frustrating enough back in high school. (If you even got that far in high school math.)
And they haven’t improved with age, now that you can barely remember what you learned in high school.
And yet… they show up so often in data analysis.
If you don’t quite remember what they are and how they work, they can make the statistical methods that use them seem that much more obtuse.
So we’re going to take away that fog of confusion about exponents and logs and how they work. (more…)
One question that seems to come up pretty often is:
What is the difference between logistic and probit regression?
Well, let’s start with how they’re the same:
Both are types of generalized linear models. This means they have this form:
One of the big assumptions of linear models is that the residuals are normally distributed. This doesn’t mean that Y, the response variable, has to also be normally distributed, but it does have to be continuous, unbounded and measured on an interval or ratio scale.
Unfortunately, categorical response variables are none of these.