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 webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.
About the Instructor
Jeff Meyer is a statistical consultant, instructor and writer for The Analysis Factor.
Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.
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Related Posts
- May 2013 Member Webinar: Using Excel to Graph Predicted Values from Regression Models
- Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables
- Member Training: Non-Parametric Analyses
- Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
{ 1 comment… read it below or add one }
Bonjour
heureux de vous rejoindre.
Je ne sais pas si je poste au bon endroit, je n’ai pas l’habitude des forums.
Je suis plutôt habitué à simplement lire sans participer.
Mais certains sujets m’ont tellement motivé et apporté que j’ai décidé de m’inscrire.