One of Stata’s incredibly useful abilities is to temporarily store calculations from commands.
Why is this so useful? (more…)
OptinMon
Using Stored Calculations in Stata to Center Predictors: an Example
July 9th, 2015 by Jeff Meyer
Measures of Predictive Models: Sensitivity and Specificity
June 5th, 2015 by Karen Grace-Martin
A few years ago, I was in Syracuse for a family trip to the zoo. Syracuse is about 50 miles from where I live and it has a very nice little zoo.
One year was particularly exciting because a Trader Joe’s just opened in Syracuse. We don’t have one where we live* (sadly!) so we always stock up on our favorite specialty groceries when we’re near a Trader Joe’s.
On this particular trip, though, we had an unwelcome surprise. My credit card card company believed my Trader Joe’s spree was fraudulent and declined the transaction. I got a notice on my phone and was able to fix it right away, so it wasn’t the big inconvenience it could have been.
But this led us to wonder what it was about the transaction that led the bank to believe it was fraudulent. Do credit card thieves often skip town and go grocery shopping?
The bank was clearly betting so. It must have a statistical model for aspects of a transaction that are likely enough to be fraudulent that it shuts it down. (more…)
Effect Size Statistics in Logistic Regression
May 18th, 2015 by Karen Grace-Martin
Effect size statistics are expected by many journal editors these days.
If you’re running an ANOVA, t-test, or linear regression model, it’s pretty straightforward which ones to report.
Things get trickier, though, once you venture into other types of models.
What is a Logit Function and Why Use Logistic Regression?
May 11th, 2015 by Karen Grace-Martin
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.
Linear Models in R: Improving Our Regression Model
April 23rd, 2015 by David Lillis
Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command.
Just as we did last time, we perform the regression using lm(). This time we store it as an object M. (more…)
Linear Models in R: Diagnosing Our Regression Model
April 21st, 2015 by David Lillis