A Bayesian Approach to the Analysis of Industrial Experiments: An Illustration with Binomial Count Data

In recent years, Bayesian methods have become increasingly popular for solving industrial statistical problems. In this article, we illustrate the use of Bayesian methods to analyze binomial count data collected from a designed experiment. In addition to fitting an appropriate logistic regression model, we show how the Bayesian approach provides an integrated framework to address model goodness-of-fit, model selection, response surface estimation, optimization, and prediction.