Regression is a family of curve-fitting methods for (1) predicting average response performance for new combinations of factors and (2) understanding which factor changes cause changes in average outputs. In this chapter, the uses of regression for prediction and performing hypothesis tests are described. Regression methods are perhaps the most widely used statistics or operations research techniques. Also, even though some people think of regression as merely the “curve fitting method” in Excel, the methods are surprisingly subtle with much potential for misuse (and benefit).
- Chantarat N (2003) Modern design of experiments for screening and experimentations with mixture and qualitative variables. PhD dissertation, Industrial & Systems Engineering, The Ohio State University, ColumbusGoogle Scholar
- Chantarat N, Zheng N, Allen TT, Huang D (2003) Optimal experimental design for systems involving both quantitative and qualitative factors. In: Ferrin RDM, Sanchez P (eds) Proceedings of the winter simulation conferenceGoogle Scholar
- Montgomery DC (2012) Statistical quality control, 7th edn. New York, WileyGoogle Scholar
- Piepel GF, Cornell JA (1991) A catalogue of mixture experiments. In: Proceedings of the joint statistical meetings (Aug 19), AtlantaGoogle Scholar