How to do Simple Linear Regression in JASP (14-7) 

Exploring our data about burnout and job satisfaction, we predict an outcome with a single variable using simple linear regression in JASP. I explain how regression works, then open an SPSS dataset in JASP. We explore the assumptions of homoscedasticity and linearity with a scatterplot and normality using a Shapiro-Wilk test. Later we examine the residual statistics for outliers and use the Durbin-Watson test to check for independence of observations. Assumptions met, we conduct the simple linear regression in JASP, interpret the results, and write up the findings in APA style.
I also answer the question: “What is the difference between capital R and lower case r, for reporting?”
Download the Friendly, Free, Flexible, Functional JASP software from the official JASP statistics website: jasp-stats.org
This video teaches the following commands and techniques in JASP:
Importing a .SAV into JASP
Simple Linear Regression
Assumptions for regression
Interpreting regression coefficients
Using a regression equation for prediction
This video uses the dataset Job Satisfaction.SAV and JASP version 12.0
Bass Walker - Film Noir by Kevin MacLeod is licensed under a Creative Commons Attribution license (creativecommons.org/licenses/...)
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Link to a Google Drive folder with all of the files that I use in the videos including spreadsheets, the Bear Handout, and the Job Satisfaction.SAV dataset. As I add new files, they will appear here, as well.
drive.google.com/drive/folder...
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