Rachel Johnson

This tutorial outlines the basic procedures for planning experiments within the context of the scientific method. Too often quality practitioners fail to appreciate how subject-matter expertise must interact with statistical expertise to generate efficient and effective experimental programs. This tutorial guides the quality practitioner through the basic steps, demonstrated by extensive past experience, that consistently lead to successful results. This tutorial makes extensive use of flowcharts to illustrate the basic process....

The areas of application for design of experiments principles have evolved, mimicking the growth of U.S. industries over the last century, from agriculture to manufacturing to chemical and process industries to the services and government sectors. In addition, statistically based quality programs adopted by businesses morphed from total quality management to Six Sigma and, most recently, statistical engineering (see Hoerl and Snee 2010). The good news about these transformations is that each evolution contains more technical substance, embedding the methodologies as core competencies, and is less of a ‘‘program....

There are many situations where the requirements of a standard experimental design do not fit the research requirements of the problem. Three such situations occur when the problem requires unusual resource restrictions, when there are constraints on the design region, and when a non-standard model is expected to be required to adequately explain the response. Suggested Citation Johnson, Rachel T., Douglas C. Montgomery, and Bradley A. Jones. “An Expository Paper on Optimal Design....

This paper illustrates the construction of D-optimal second order designs for situations when the response is either binomial (pass/fail) or Poisson (count data). Suggested Citation Johnson, Rachel T., and Douglas C. Montgomery. “Choice of Second-Order Response Surface Designs for Logistic and Poisson Regression Models.” International Journal of Experimental Design and Process Optimisation 1, no. 1 (2009): 2. https://doi.org/10.1504/IJEDPO.2009.028954. Paper:

We illustrate the construction of Bayesian D-optimal designs for nonlinear models and compare the relative efficiency of standard designs with these designs for several models and prior distributions on the parameters. Through a relative efficiency analysis, we show that standard designs can perform well in situations where the nonlinear model is intrinsically linear. However, if the model is nonlinear and its expectation function cannot be linearized by simple transformations, the nonlinear optimal design is considerably more efficient than the standard design....