by David Lillis, Ph.D.

In my last blog post we fitted a generalized linear model to count data using a Poisson error structure.

We found, however, that there was over-dispersion in the data – the variance was larger than the mean in our dependent variable.

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Generalized Linear Models in R, Part 6: Poisson Regression for Count Variables

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..

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Generalized Linear Models in R, Part 5: Graphs for Logistic Regression

In my last post I used the glm() command to fit a logistic model with binomial errors to investigate the relationships between the numeracy and anxiety scores and their eventual success. Now we will create a plot for each predictor. This can be very helpful for helping us understand the effect of each predictor on the probability of a 1 response on our dependent variable…

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Generalized Linear Models (GLMs) in R, Part 4: Options, Link Functions, and Interpretation

Last year I wrote several articles that provided an introduction to Generalized Linear Models (GLMs) in R. As a reminder, Generalized Linear Models are an extension of linear regression models that allow the dependent variable to be non-normal. In our example for this week we fit a GLM to a set of education-related data…

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Stata Loops and Macros for Large Data Sets: Quickly Finding Needles in the Hay Stack

I recently opened a very large data set titled “1998 California Work and Health Survey” compiled by the Institute for Health Policy Studies at the University of California, San Francisco. There are 1,771 observations and 345 variables…

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August 2015 Membership Webinar: Latent Class Analysis

Latent Class Analysis is a method for finding and measuring unobserved latent subgroups in a population based on responses to a set of observed categorical variables.

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Random Intercept and Random Slope Models

This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t […]

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Using the Collapse Command in Stata

Have you ever worked with a data set that had so many observations and/or variables that you couldn’t see the forest for the trees? You would like to extract some simple information but you can’t quite figure out how to do it. Get to know Stata’s collapse command…

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Five Tips and Tricks: How to Make Stata Easier to Use

Stata allows you to describe, graph, manipulate and analyze your data in countless ways. But at times (many times) it can be very frustrating trying to create even the simplest results. Join us and learn how to reduce your future frustrations.This one hour demonstration is for new and intermediate users of Stata. If you’re a […]

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Using Stored Calculations in Stata to Center Predictors: an Example

One of Stata’s incredibly useful abilities is to temporarily store calculations from commands. Why is this so useful?

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