Simple Linear Regression | Statistics for Applied Epidemiology | Tutorial 1

Simple Linear Regression Explained

This tutorial reviews simple linear regression and data exploration. Interpreting regression model output, examining errors, model assumptions and checking model assumptions.

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This statistics tutorial is prepared to support SPPH 500: Analytic Methods in Applied Epidemiology course offered in the School of Population and Public Health at the University of British Columbia (UBC). These videos are created as part of #marinstatslectures video tutorial series to support some courses at UBC (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.

Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

[charleshenderson]

Thanks for an excellent introductory presentation to linear regression. Your mnemonic for the assumptions is helpful, but perhaps it could be extended to include additional assumptions that complete the term LINEAR:
Linear
relationship between predictor and response variables
Independent residuals (no autocorrelation)
Normally distributed residuals
Equal variance of residuals (homoscedasticity)
Absence of external-variable predictor correlation (tertium quid)
Removed multicollinearity issues

Chuck

[javierbeltran]

Hi...Thanks for all your magnificent work!!... Could you put a link with the "fev" data set?... All the best...