Monthly Archives: August 2013

Regression with a binary response

Predicting a yes/no type (binary) response given a continuous predictor.

The instance below illustrates regression several common generalized linear regression models (GLMs) with a continuous predictor $X$ and binary response $Y$ . The most important example is that of logistic regression, which uses the logit link function in the GLM. The logit function is defined $\mbox{logit}(x) = \log\left(\frac{x}{1-x}\right)$ .

Facilitated elicitation of the beta distribution

This module was presented at the 2013 Joint Statistical Meetings.

You can download the module here.

The module above is intended to help statisticians and experts communicate better. It is specifically designed with the problem of the prior elicitation of a population proportion in mind (e.g. the binomial parameter π, or p), using the mode/percentile method. This module is still in its design stage. Like all BaylorISMs modules, it comes with absolutely no guarantee.

The slides from the 2013 JSM talk concerning this module are now posted! You can find them either below OR here for higher resolution.