The concept of a statistical interaction is one of those things that seems very abstract. If you’re like me, you’re wondering: What in the world is meant by “the relationship among three or more variables”?

When you have data measuring the time to an event, you can examine the relationship between various predictor variables and the time to the event using a Cox proportional hazards model. In this webinar, you will see what a hazard function is and describe the interpretations of increasing, decreasing, and constant hazard. Then you will examine the log rank test, a simple test closely tied to the Kaplan-Meier curve, and the Cox proportional hazards model.

In the past few months, I've gotten the same question from a few clients about using linear mixed models for repeated measures data. They want to take advantage of its ability to give unbiased results in the presence of missing data. In each case the study has two groups complete a pre-test and a post-test measure. Both of these have a lot of missing data...

Despite modern concerns about how to handle big data, there persists an age-old question: What can we do with small samples?

Odds Ratios and Relative Risks are often confused despite being unique concepts. Why?

This webinar will discuss what truncated and censored data is and how to identify it...

This webinar will explore two ways of modeling zero-inflated data: the Zero Inflated model and the Hurdle model. Both assume there are two different processes: one that affects the probability of a zero and one that affects the actual values, and both allow different sets of predictors for each process.

In our upcoming Linear Models in Stata workshop, we will explore ways to find observations that influence the model. This is done in Stata via post-estimation commands. As the name implies, all post-estimation commands are run after running the model (regression, logit, mixed, etc)...

There are many steps to analyzing a dataset. One of the first steps is to create tables and graphs of your variables in order to understand what is behind the thousands of numbers on your screen. But the type of table and graph you create depends upon the type of variable you are looking at...

In a previous blog post we examined how to use the same sample when comparing the differences among regression models. Using different samples in our models could lead to erroneous conclusions when interpreting our models. But excluding observations can also result in inaccurate results...