Case Studies

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  1. How To Get More Learning With Less Experimentation

    January 2016
    Experimentation informs every part of the biologic lifecycle. But it is costly and time consuming — especially when you are using outdated methods. As you strive for more efficiency from your scientists and engineers, can you streamline your work processes to get more learning with less experimentation? Both multivariate analysis (MVA) and design of experiments (DoE) methods have numerous applications for simplifying the learning from large data sets and experimentation. However, many scientists and engineers still perceive these methods to be complex. Today’s newer, intuitive software applications make these techniques user-friendly for even non-statisticians. Early adopters are seeing decreased time to market, reduced development and production costs, and improved quality and reliability.
    Authors: Frank Westad
    Publication: CAMO Analytics
  2. Employing Power to "Right-Size" Design of Experiments

    March 2014

    This article provides insights on how many runs are required to make it very likely that a test will reveal any important effects. Due to the mathematical complexities of multifactor Design of Experiments (DOE) matrices, the calculations for adequate power and precision (Oehlert and Whitcomb 2002) are not practical to do by 'hand' so the focus is kept at a high level--scoping out the forest rather than detailing all the trees. By example, reader will learn the price that must be paid for an adequately-sized experiment and the penalty incurred by conveniently grouping hard-to-change factors. (The article is not available on the ITEA Journal web site without membership. Click on the Download PDF link below to view the manuscript.)

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: The ITEA Journal
  3. Optimal Experimental Design with R

    May 2012
    BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.
    Authors: Adams, Wayne F.; Anderson, Mark J.
    Publication: Technometrics
  4. What to look for in Statistical Software for the Pharmaceutical Industry

    January 2011

    This article discusses what to look for in DOE software in the pharmaceutical industry.

    Authors: Anderson, Mark J.
    Publication: Pharmaceutical Manufacturing

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