Introduction Let’s continue along the lines discussed in Omitted Variable Effects in Logistic Regression. The issue is as follows. For logistic regression, omitted variables cause parameter estimation bias. This is true even for independent variables, which is not the case for more familiar linear regression. This is a known problem […]
Nina Zumel recently gave a very clear explanation of logistic regression ( The Simpler Derivation of Logistic Regression ). In particular she called out the central role of log-odds ratios and demonstrated how the “deviance” (that mysterious quantity reported by fitting packages) is both a term in “the pseudo-R^2” (so […]
One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that.