I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
I just posted brief multicollinearity tutorial on my other blog (loosely based on the material from the Serious Stats book). You can read it here.Filed under: serious stats, stats advice Tagged: correlation and covariance, general linear model, messy d...
Like some of the other terms in our list--level and beta--GLM has two different meanings. It's a little different than the others, though, because it's an abbreviation for two different terms: General Linear Model and Generalized Linear Model. It's extra confusing because their names are so similar on top of having the same abbreviation.
The assumptions are exactly the same for ANOVA and regression models. The normality assumption is that residuals follow a normal distribution. You usually see it like this: ε~ i.i.d. N(0, σ²) But what it's really getting at is the distribution of Y|X.
Generalized linear models, linear mixed models, generalized linear mixed models, marginal models, GEE models. You’ve probably heard of more than one of them and you’ve probably also heard that each one is an extension of our old friend, the general linear model. This is true, and they extend our old friend in different ways, particularly in regard to the measurement level of the dependent variable and the independence of the…
Here is a list of the contents by chapter with quick notes on chapter content … 0. Preface (About the book; notes on software, mathematics and types of boxed sections) 1. Data, Samples and Statistics (A gentle review of measures … Continue reading →
The conceptual difference between within-subject and between-subject effects is something I am asked about quite often. So often in fact, I thought a blog posting was warranted! As a quick disclaimer, I know this is a complex issue and the description ...
