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 R2 is usually a distraction. Linear regression coefficients will change with the distribution of unobserved variables (omitted variable effects). 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.
Posted at February 04, 2011 01:31 | permanent link