Interpreting Multiple Regression Output for Business Statistics

Building on our regression model, we add more predictors and interpret the output to see how they improve the model. We begin by checking the assumptions for multiple regression - outliers, normality, homoscedasticity, independence of observations, and multicollinearity - using the output and checks like standardized residuals, Q-Q plot, Durbin-Watson, tolerance, and VIF. Then we add two more predictor to our original model and watch how the parameters change and whether the r-squared change justifies keeping them.
This lecture was recorded on Monday, November 2, 2020 at Missouri State University for QBA 337 - Applied Business Statistics
Music
50 Ways to Clean Your Data - The Spurious Correlations, from the unreleased album of statistical parodies Dark Side of the Mu
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[@peterkateris10]

Excellent videos! Hopefully you can upload a logistic regression video soon

[@ResearchByDesign]

Thanks for the request. I am working on an example of logistic regression. Hope to have it posted this semester as part of an advanced stats class that I am teaching. Cheers

[@chunpongcheung465]

Thanks for your interpretation. It's great. Is there an example of multi regression in JASP in the future? I am confused about how to use it and write it up in APA7. Thanks

[@geoxatzi3966]

I need this song.

[@danab9865]

Yes pleaseeee

[@ShahidIqbal-xv6fo]

sir you are simply excellent have u any video prepared for logistic regression analysis ? plz upload thanks sir

[@ResearchByDesign]

I don't have a logistic regression video yet. If you are in a hurry, I have four several excellent videos about LR on Coursera. I hope to have some LR videos eventually, but not anytime soon. Good luck with your search.

[@khadramuhumed3597]

thank you

[@ResearchByDesign]

You're welcome

[@roslyndougan8046]

I thought SPSS was bad JASP i wouldn't like