### The Truth About Linear Regression (Advanced Data Analysis from an Elementary Point of View)

Multiple linear regression: general formula for the optimal linear
predictor. Using Taylor's theorem to justify linear regression locally.
Collinearity. Consistency of ordinary least squares estimates under weak
conditions. Linear regression coefficients will change with the distribution
of the input variables: examples. Why R^{2} is usually a distraction.
Linear regression coefficients will change with the distribution of unobserved
variables (omitted variable problems). Errors in variables. Transformations of
inputs and of outputs. Utility of probabilistic assumptions; the importance of
looking at the residuals. What "controlled for in a linear regression" really
means.

*Reading*: Notes,
chapter 2
(R for
examples and figures); Faraway, chapter 1 (continued).

Advanced Data Analysis from an Elementary Point of View

Posted by crshalizi at January 24, 2012 10:15 | permanent link