Management Articles

• A Useful Method For Model-Building by George E. P. Box, William G. Hunter
  "The object of much experimentation is to build or discover a suitable model. This is done by an iterative procedure in which a particular model is tentatively entertained, strained in various ways over the region of applicat.ion, and its defects found. The nature of the defects interacting with the experimenter’s technical knowledge can suggest changes and remedies leading to a new model which, in turn, is tentatively entertained, and submitted to a similar straining process."

• Response surface methods and sequential exploration by Ron Kenett, David M. Steinberg
  "A typical response surface study begins with a screening experiment to identify the most important factors. Small, orthogonal experimental plans and simple regression models are usually used for screening (see our second and third blog posts in this series). Subsequent experiments will depend on the results of the screening experiment. For example, factors that had small effects might be dropped from further consideration. Other factors might be added. The team might decide to shift the levels of some of the factors to get better results for the critical quality attributes (CQA’s). If the results suggest that a first-order model is no longer a good fit to the data, the team expands the design to permit fitting a second-degree regression model."

• The Scientific Context of Quality Improvement by George E. P. Box, Soren Bisgaard
  Scientific method is a key ingredient in the new philosophy of quality and productivity improvement. This paper provides an overview. A discussion of new ideas of how to design quality into products and processes is provided and Taguchi's work is evaluated.

• A useful method for model-building II: Synthesizing response functions from individual components by William G. Hunter
  "There is a vast difference between quality control and quality improvement, passive statistical tools such as Shewhart control charts are useful for quality control, to determine whether the process under surveillance shows any signs of going out of its state of statistical control. On the other hand, more active tools are needed for quality and productivity improvement. In order to improve a process or a product, it is often helpful to use experimental designs in developing a mathematical equation or set of equations to relate the response(s) of interest to important process and environmental variables. Such models can aid in understanding how the relevant processes work so they can be modified in desirable ways. This report contains a practical suggestion that model-builders may find helpful. It involves synthesizing response functions of interest by starting with the simpler task of constructing models for component responses or subsets of them."

• How to Get Lucky by George E. P. Box
  "Some principles for success in quality improvement projects discuss, in particular, how to encourage die discovery of useful phenomena not initially being sought. A graphical version of the analysis of variance which can help show up the unexpected is illustrated with two examples."

• American Statistical Association (ASA) Statement on Statistical Significance and P-Values
  "Practices that reduce data analysis or scientific inference to mechanical “bright-line” rules (such as “p < 0.05”) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision making. A conclusion does not immediately become “true” on one side of the divide and “false” on the other. Researchers should bring many contextual factors into play to derive scientific inferences"

• Statistics for Discovery by George E. P. Box
  This report explores why investigators in engineering and the physical sciences rarely use statistics. It is argued that statistics has been overly influenced by mathematical methods rather than the scientific method and consequently the subject has been greatly skewed towards testing rather than discovery.

• Teaching Engineers Experimental Design with a Paper Helicopter by George E. P. Box
  "How a paper 'helicopter' made in a minute or so from 8 1/2' x 11' sheet of paper can be used to teach principles of experimental design including - conditions for validity of experimentation, randomization, blocking, the use of factorial and fractional factorial designs, and the management of experimentation."

• Robustness in the Strategy of Scientific Model Building by George E. P. Box
  "All models are wrong but some are useful ... The iterative building process for scientific models can take place over short or long periods of time. ... It should be remembered that just as the Declaration of Independance promises the pursuit of happiness rather than happiness itself, so the iterative scientific model building process offers only the pursuit of the perfect model."

• What Can You Find Out From 12 Experimental Runs? by George E. P. Box, Soren Bisgaard
  Examines the possibilities of factorially designed experiments using many variables at once.

• Ohno’s Problem-Solving Methods by Bob Emiliani, Taiichi Ohno
  "leaders have to respect people and make it safe for people to understand problems and correct them by both trial and error and experiments."

• A manifesto for reproducible science
  "Here we propose a series of measures that we believe will improve research efficiency and robustness of scientific findings by directly targeting specific threats to reproducible science. We argue for the adoption, evaluation and ongoing improvement of these measures to optimize the pace and efficiency of knowledge accumulation. The measures are organized into the following categories methods, reporting and dissemination, reproducibility, evaluation and incentives. They are not intended to be exhaustive, but provide a broad, practical and evidence-based set of actions that can be implemented by researchers, institutions, journals and funders."