Overview of this Lesson
In this lesson we'll look at some of the main things that can go wrong with a multiple linear regression model. We'll also consider methods for overcoming some of these pitfalls. In particular, we'll cover:
 Nonconstant variance and weighted least squares
 Autocorrelation and time series methods
 Multicollinearity, which exists when two or more of the predictors in a regression model are moderately or highly correlated with one another.
 Overfitting
 Excluding important predictor variables
 Extrapolation
 Missing data
 Power and sample size
Key Learning Goals for this Lesson: 
 Know the main issues surrounding nonconstant variance.
 Use weighted least squares to mitigate some nonconstant variance problems.
 Know the main issues surrounding autocorrelation.
 Use time series methods to mitigate autocorrelation problems.
 Know what multicollinearity means.
 Distinguish between structural multicollinearity and databased multicollinearity.
 Understand the effects of uncorrelated predictors on various aspects of regression analyses.
 Understand the effects of multicollinearity on various aspects of regression analyses.
 Understand variance inflation factors and how to use them to help detect multicollinearity.
 Know the two ways of reducing databased multicollinearity.
 Understand how centering the predictors in a polynomial regression model helps to reduce structural multicollinearity.
 Know the main issues surrounding other regression pitfalls, including overfitting, excluding important predictor variables, extrapolation, missing data, and power and sample size.
