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.

Why is it we can model non-linear effects in linear regression? What the heck does it mean for a model to be “linear in the parameters?” In this webinar we will explore a number of ways of using a linear regression to model a non-linear effect between X and Y.

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..

Last time we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again..

Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people.

Lately, I've gotten a lot of questions about learning how to run models for repeated measures data that isn't continuous. Mostly categorical. But once in a while discrete counts. A typical study is in linguistics or psychology where..

A Science News article from July 2014 was titled “Scientists’ grasp of confidence intervals doesn’t inspire confidence.” Perhaps that is why only 11% of the articles in the 10 leading psychology journals in 2006 reported confidence intervals in their statistical analysis. How important is it to be able to create and interpret confidence intervals?

If you’ve ever worked on a large data analysis project, you know that just keeping track of everything is a battle in itself. Every data analysis project is unique, but here are a few strategies I used in a recent project that you may find helpful. They didn’t make the project easy, but they helped keep it from spiraling into overwhelm...

Just last week, a colleague mentioned that while he does a lot of study design these days, he no longer does much data analysis. His main reason was that 80% of the work in data analysis is preparing the data for analysis. Data preparation is s-l-o-w and he found that few colleagues and clients understood this...

In this webinar, we’ll discuss the different model options for count data, including how to figure out which one works best. We’ll go into detail about how the models are set up, some key statistics, and how to interpret parameter estimates.