Rachel Johnson on Test Science Research Document Library https://research.testscience.org/researchers/rachel-johnson/ Recent content in Rachel Johnson on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Tue, 01 Jan 2013 00:00:00 +0000 A Tutorial on the Planning of Experiments https://research.testscience.org/post/2013-a-tutorial-on-the-planning-of-experiments/ Tue, 01 Jan 2013 00:00:00 +0000 https://research.testscience.org/post/2013-a-tutorial-on-the-planning-of-experiments/ 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. 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. Two case studies summarize the applications of the methodology.

Suggested Citation

Freeman, Laura J., Anne G. Ryan, Jennifer L. K. Kensler, Rebecca M. Dickinson, and G. Geoffrey Vining. “A Tutorial on the Planning of Experiments.” Quality Engineering 25, no. 4 (October 1, 2013): 315–32. https://doi.org/10.1080/08982112.2013.817013.

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]]> Designed Experiments for the Defense Community https://research.testscience.org/post/2012-designed-experiments-for-the-defense-community/ Sun, 01 Jan 2012 00:00:00 +0000 https://research.testscience.org/post/2012-designed-experiments-for-the-defense-community/ 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. 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.’’ Design of experiments is fundamental to statistical engineering and is receiving increased attention within large government agencies such as the National Aeronautics and Space Administration (NASA) and the Department of Defense. Because test policy is intended to shape test programs, numerous test agencies have experimented with policy wording since about 2001. The Director of Operational Test & Evaluation has recently (2010) published guidelines to mold test programs into a sequence of well-designed and statistically defensible experiments. Specifically, the guidelines require, for the first time, that test programs report statistical power as one proof of sound test design. This article presents the underlying tenets of design of experiments, as applied in the Department of Defense, focusing on factorial, fractional factorial, and response surface design and analyses. The concepts of statistical modeling and sequential experimentation are also emphasized. Military applications are presented for testing and evaluation of weapon system acquisition, including force-on-force tactics, weapons employment and maritime search, identification, and intercept.

Suggested Citation

Johnson, Rachel T., Gregory T. Hutto, James R. Simpson, and Douglas C. Montgomery. “Designed Experiments for the Defense Community.” Quality Engineering 24, no. 1 (January 2012): 60–79. https://doi.org/10.1080/08982112.2012.627288.

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]]> An Expository Paper on Optimal Design https://research.testscience.org/post/2011-an-expository-paper-on-optimal-design/ Sat, 01 Jan 2011 00:00:00 +0000 https://research.testscience.org/post/2011-an-expository-paper-on-optimal-design/ 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. 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.” Quality Engineering 23, no. 3 (July 2011): 287–301. https://doi.org/10.1080/08982112.2011.576203.

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]]> Choice of Second-Order Response Surface Designs for Logistic and Poisson Regression Models https://research.testscience.org/post/2009-choice-of-second-order-response-surface-designs-for-logistic-and-poisson-regression-models/ Thu, 01 Jan 2009 00:00:00 +0000 https://research.testscience.org/post/2009-choice-of-second-order-response-surface-designs-for-logistic-and-poisson-regression-models/ 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: 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.

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]]> Designing Experiments for Nonlinear Models—an Introduction https://research.testscience.org/post/2009-designing-experiments-for-nonlinear-models-an-introduction/ Thu, 01 Jan 2009 00:00:00 +0000 https://research.testscience.org/post/2009-designing-experiments-for-nonlinear-models-an-introduction/ 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. 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.

Suggested Citation

Johnson, Rachel T., and Douglas C. Montgomery. “Designing Experiments for Nonlinear Models—an Introduction.” Quality and Reliability Engineering International 26, no. 5 (July 2010): 431–41. https://doi.org/10.1002/qre.1063.

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