deviance

What Are Nested Models?

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
Linear Mixed Models
Generalized Linear Mixed Models
Parametric Survival Analysis models, like Weibull models
Structural Equation Models

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require [Read more…] about What Are Nested Models?

Member Training: The Multi-Faceted World of Residuals

by Karen Grace-Martin 1 Comment

Most analysts’ primary focus is to check the distributional assumptions with regards to residuals. They must be independent and identically distributed (i.i.d.) with a mean of zero and constant variance.

Residuals can also give us insight into the quality of our models.

In this webinar, we’ll review and compare what residuals are in linear regression, ANOVA, and generalized linear models. Jeff will cover:

Which residuals — standardized, studentized, Pearson, deviance, etc. — we use and why
How to determine if distributional assumptions have been met
How to use graphs to discover issues like non-linearity, omitted variables, and heteroskedasticity

Knowing how to piece this information together will improve your statistical modeling skills.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output

by guest 3 Comments

by David Lillis, Ph.D.

In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set [Read more…] about Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output