Multiple Regression II

Week 6

Concepts

Multiple Regression

• review: a linear model with multiple predictor variables
• how to find the best single predictor variable
• stepwise procedure for how to find the best set of predictors
• how to assess whether a variable adds significantly to the predictive power
• p-value ; R^2 adj ; AIC; the step() procedure
• Assumptions of regression and how to assess them for your data/model
• normality, heteroscedasticity, nonlinearity

Required Readings

• 15 Linear regression (includes multiple regression) in Learning Statistics with R (pdf version)
• Chapter 9 “Multiple and logistic regression” of OpenIntro Statistics (but you can ignore section 9.5 “Introduction to logistic regression”)

Additional supporting materials

• Introduction to Multiple Regression (YouTube)
• Model Selection in Multiple Regression (YouTube)
• Checking Multiple Regression Diagnostics Using Graphs (YouTube)