### The Truth About Linear Regression (Advanced Data Analysis from an Elementary Point of View, Lecture 2)

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.

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Advanced Data Analysis from an Elementary Point of View

Posted at February 04, 2011 01:31 | permanent link