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...

Can we ignore the fact that a variable is bounded and just run our analysis as if the data wasn’t bounded?

In this webinar, we will discuss when tables and graphs are (and are not) appropriate and how people tend to engage with each of these media...

In this talk, you will see a simple example of this using fruit fly data, and learn how to interpret the Kaplan-Meier curve to estimate survival probabilities and survival percentiles..

Like logistic and Poisson regression, beta regression is a type of generalized linear model. It works nicely for proportion data because the values of a variable with a beta distribution must fall between 0 and 1. It's a bit of a funky distribution in that it's shape can change a lot depending on the values of the mean and dispersion parameters. Here are a few examples of the possible shapes of a beta distribution, with different means and variances...

By Manolo Romero Escobar If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM). As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and […]