Articles authored by Patrick J. Whitcomb.

• Design Of Experiments
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  The statistical design of experiments is an essential ingredient of successful product development and improvement, and provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional vary one-factor-at-a-time (OFAT) experimentation, variables are changed together, permitting evaluation of interactions. Standard texts give details about the construction of specific test plans, such full and fractional factorial, and response surface designs, and the analysis of the resulting data. This article gives a brief overview. The focus here is on the fundamental elements of experimental design: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage-wise experimentation. Brief discussions dealing with the statistical analysis tools, multiple response variables, and some historical background are also provided.
  Kirk-Othmer Encyclopedia of Chemical Technology September 2010

• Making Use of Mixture Design to Optimize Olive Oil - A Case Study
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  Olive oil, an important commodity of the Mediterranean region and a main ingredient of their world-renowned diet (see sidebar), must meet stringent European guidelines to achieve the coveted status of "extra virgin." Oils made from single cultivars (a particular cultivated variety of the olive tree) will at times fall into the lower "virgin" category due to seasonal variation. Then it becomes advantageous to blend in one or more superior oils based on a mixture design for optimal formulation.
  CPID Newsletter August 2009

• Response Surface Methods (RSM) for Peak Process Performance at the Most-Robust Operating Conditions
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  Response Surface methods (RSM) provide superb statistical tools for design and analysis of experiments aimed at process optimization. At the final stages of process development, RSM illuminates the sweet spot where high yield of in-specification products can be achieved at lowest possible cost. It produces statistically-validated predictive models and with the aid of specialized software, response surface maps that point the way to pinnacles of process performance.
  Unknown June 2008

• How to Plan and Analyze a Verification DOE
  Authors: Kraber, Shari L.; Whitcomb, Patrick J.
  Power point presentation given at ASQ 2008 Lean Six Sigma Conference.
  ASQ Lean Six Sigma Conference February 2008

• Graphical Selection of Effects in General Factorials
  Authors: Oehlert, Gary W.; Whitcomb, Patrick J.
  Power point presentation demonstrates equivalency of new method with Daniel's half-normal plot of effects for two-level factorials. Demonstrates the general method: two replicates of a 3x2 factorial, two replicates of a 3x2x2 factoriall, single replicate of a 3x4x4 factorial.
  Fall Technical Conference October 2007

• Using Graphical Diagnostics to Deal with Bad Data
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  This article deals with thorny issues that confront every experimenter, i.e., how to handle individual results that do not appear to fit with the rest of the data - damaging outliers and/or a need for transformation. The trick is to maintain a reasonable balance between two types of errors: (1) deleting data that very only due to common causes, thus introducing bias to the conclusions. (2) not detecting true ouliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to break-through improvements (making discoveries more or less by accident).
  
  Two real life data sets are reviewed. Neither reveals its secrets at first glance. However, with the aid of various diagnostic plots (readily available in off-the-shelf statistical software), it becomes much clearer what needs to be done. Armed with this knowledge, quality engineers will be much more likely to draw the proper conclusions from experiments that produce bad (discrepant) data.
  Quality Engineering 2007

• Automated Optimization of a Multiplex PCR Using Sagian AAO Software for the Biomek FX Liquid Handling System
  Authors: Campbell, Dana; Fan, Lisa; Roby, Keith; Threadgill, Graham; Whitcomb, Patrick J.
  Optimizing biological assays conditions is often a challenging process facing scientists. The demand to produce quality and robust assays that work across a range of biological conditions is often strived for along with a short development timeframe. In addition, automated systems are often required to enable scientists to screen in a high-throughput environment.
  Beckman Coulter 2007

• Optimisation of EMD Fast Hole Drilling through an Evaluation of Electrode Geometry
  Authors: Cuttell, Martyn; Leao, Fabio; Lord, Peter; Pashby, Ian; Whitcomb, Patrick J.
  Submitted to the "Journal of Materials Processing Technology"
  
  EDM fast hole drilling is one of the most important variations of eletrical discharge machining. The process plays an important role in the aerospace industry as it is one of the few that can be applied to the drilling of precision small holes in a number of parts, including turbine blades. One of the most important factors affecting the speed of EDM fast hole drilling is the high pressure dielectric fluid, which is usually supplied to the gap through the bore to tubular eletrodes. Thus, it can be expected that the bore size and geometry have a great impact on the process performance. However, there has not been much research on this topic. By employing statistical methods to optimise the process performance, this work shows that drilling time and electrode wear can decrease 165% and 25% respectively, depending on the type of electrode geometry used.
  Unknown 2006

• Design of Experiments for Coatings
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  The traditional approach to experimentation changes only one process factor at a time (OFAT) or one component in a formulation. However, the OFAT approach does not provide data on interactions of factors (or components), a likely occurrence with coating formulations and processes. Statistically-based design of experiments (DOE) provides validated models, including any significant interactions, that allow you to confidently predict response measures as a function of inputs. The payoff is the identification of "sweet spots," where you can achieve all product specifications and processing objectives.
  Coatings Technology Handbook 2006

• Rethink Experiment Design
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  The traditional approach to experimentation, often referred to as the "scientific method" requires changing only one factor at a time (OFAT), but this method only allows one to see things one dimension at a time. By varying factors only at two levels each, but simultaneously rather than one at a time, experimenters can uncover important interactions.
  Chemical Processing 2006