Multiple Linear Regression

Multiple linear regression is an extension of bivariate regression. It is used when we want to predict a continuous (interval or ratio) variable's value based on the value of more than one predictor variable, which can be any measurement scale (e.g., nominal, ordinal, interval, or ratio). In this session, Dr. Taylor discusses (1) the four scales of measurement, (2) describes the conditions for using multiple linear regression (with all continuous predictor variables), (3) identifies data assumptions surrounding multiple linear regression, (4) shows how to conduct multiple linear regression using SPSS, (5) explains SPSS output/results, and (6) shows how to write an APA-compliant results section based on the SPSS output, including appropriate tables and figures.

[@communitystressmanagementp4140]

Thank you Dr, Taylor. This is Maxine Duncan. I just want to say this helps me very much.

[@taylorphd07]

you're welcome Maxine!

[@mari083180]

Your videos were always so helpful Dr. Reg.

[@taylorphd07]

Thank you

[@bc6480]

thank you! watched to prepare for tonights presentation, really helped to break it down for me in Layman's terms.

[@taylorphd07]

👍🏽I'm glad they are helping

[@KaraSMcCoy]

I am watching this after the fact and do not have access to the original chat- may I please have the link that you referenced that explains how to test each of the 8 assumptions?

[@mocktailswithmason]

👑💯

[@taylorphd07]

👍🏽