Regression with Categorical Explanatory Variables

When running a regression model with a categorical explanatory variable, a set of dummy variables are created to represent the possible levels of this categorical variable aside from the reference level that receives no dummy variable. A multiple regression model is fit. The intercept represents the expected response for the reference level while the remaining coefficients are the difference in expected response between the other levels and the reference level.

Probability playlist: https://www.youtube.com/playlist?list=PLFHD4aOUZFp1FxJs9BG5Sbsy6NvCO3Qb1 Inference playlist: https://www.youtube.com/playlist?list=PLFHD4aOUZFp1PZC6SgtuS-ESq4ti1GEFj Regression playlist: https://www.youtube.com/playlist?list=PLFHD4aOUZFp2xijRVpW7ucwSbToEAGBzq

STAT 587: https://www.jarad.me/courses/stat587Eng/ STAT 587 Videos: https://www.jarad.me/courses/stat587Eng/slides/ Slides: https://www.jarad.me/courses/stat587Eng/slides/Regression/R04-Categorical_explanatory_variables/R04-Categorical_explanatory_variables.pdf

Amazon affiliate links to props: Tetris light: https://amzn.to/3g8BXde Dragonwood: https://amzn.to/3k96IS7 Jumbo penny: https://amzn.to/3g8BXde Jumbo quarter: ?? DCI Science Quiz Wal Clock: https://amzn.to/3g8BXde Foam dice: https://amzn.to/3g8BXde TIX LED Clock: https://www.geekalerts.com/tix-led-clock/ Bicycle Playing Cards: https://amzn.to/2E36Na8 Recording in progress door hang sign: https://amzn.to/3la32Av ... https://www.youtube.com/watch?v=fDFyG_q2xQs