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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.

Not a Member? Join!

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.

Not a Member Yet?

It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking. You'll get access to this training webinar and 85+ other stats trainings — plus the expert guidance you need to progress with live Q&A sessions and an ask-a-mentor forum.

 

Tagged With: ANOVA, deviance, generalized linear models, linear regression, Pearson Correlation, residuals, standardized, studentized

Related Posts

  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables
  • April Member Training: Statistical Contrasts
  • Same Statistical Models, Different (and Confusing) Output Terms

Reader Interactions

Comments

  1. APeaddy says

    July 27, 2017 at 11:20 am

    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.

    Reply

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