Fortunately in Stata it is not a difficult process to use the same sample for multiple models..

An “estimation command” in Stata is a generic term used for statistical models. Examples of statistical models are linear regression, ANOVA, poisson, logit, and mixed. Stata has more than 100 estimation commands to analyze data...

How to Benefit from Stata’s Bountiful Help Resources..

This webinar will present the steps to apply a type of latent class analysis on longitudinal data commonly known as growth mixture model (GMM)..

I mentioned in my last post that R Commander can do a LOT of data manipulation, data analyses, and graphs in R without you ever having to program anything. Here I want to give you some examples, so you can see how truly useful this is. Let’s start with a simple scatter plot between Time […]

I received a question recently about R Commander, a free R package. R Commander overlays a menu-based interface to R, so just like SPSS or JMP, you can run analyses using menus.  Nice, huh? The question was whether R Commander does everything R does, or just a small subset. Unfortunately, R Commander can’t do everything […]

Correspondence analysis is a powerful exploratory multivariate technique for categorical variables with many levels. It is a data analysis tool that characterizes associations between levels of two or more categorical variables using..

Smoothing can assist data analysis by highlighting important trends and revealing long term movements in time series that otherwise can be hard to see. This presentation is pitched towards those who may use smoothing techniques during the course of their analytic work, but who have little familiarity with the techniques themselves.

In my last blog we fitted a generalised linear model to count data using a Poisson error structure. We found, however, that there was overdispersion in the data – the variance was larger than the mean in our dependent variable. One way to deal with overdispersion is to run a quasipoisson model, which fits an extra dispersion parameter to account for that extra variance..

In my last couple articles, I demonstrated a logistic regression model with binomial errors on binary data in R’s glm() function. But one of wonderful things about glm() is that it is so flexible. It can run so much more than logistic regression models. The flexibility, of course, also means that you have to tell it exactly which model you want to run, and how..