Confounding and Precision Variables in Linear Regression

Variables can serve different roles in a regression model beyond being the primary explanatory variable of interest. In this lecture we introduce confounders and precision variables. For confounding, we discuss the general concept before defining two approaches to evaluate for potential confounders: the classical and operational criteria. An example is provided for evaluating a potential confounder. Precision variables are discussed at the end, with their potential role at changing the variance estimates of our coefficients discussed. A video for the Biostatistical Methods I (BIOS 6611) course in the Department of Biostatistics and Informatics at the University of Colorado-Anschutz Medical Campus taught by Dr. Alex Kaizer. Slides and additional material available at https://www.alexkaizer.com/bios_6611. Table of Contents: 00:00 - Intro Song 00:20 - Welcome 00:37 - Confounding 03:00 - Classical Criteria for Confounding 04:03 - Operational Criterion for Confounding 05:35 - Classical and Operational Connection 06:29 - Positive Confounding 07:23 - Negative Confounding 08:10 - Accounting for Confounding 09:11 - Confounding Example 14:32 - Precision Variables