George C. Derringer

Bio: George C. Derringer is an academic researcher from Battelle Memorial Institute. The author has contributed to research in topics: Regression analysis & Replication (statistics). The author has an hindex of 7, co-authored 9 publications receiving 3787 citations.

Simultaneous Optimization of Several Response Variables

Abstract: A problem facing the product development community is the selection of a set of conditions which will result in a product with a desirable combination of properties. This essentially is a problem i...

The design of experiments: Statistical principles for practical applications.

Abstract: Preface Part I. Overture: 1. Introduction 2. Elementary ideas of blocking: the randomised block design 3. Elementary ideas of treatment structure 4. General principles of linear models for the analysis of experimental data 5. Computers for analysing experimental data Part II. First Subject: 6. Replication 7. Blocking 8. Multiple blocking systems and cross-over designs 9. Randomisation 10. Covariance - extension of linear models 11. Model assumptions and more general models Part III. Second Subject: 12. Experimental objectives, treatments and treatment structures 13. Factorial structure and particular forms of effects 14. Split unit designs and repeated measurements 15. Incomplete bloxk size for factorial experiments 16. Some mathematical theory for comfounding and fractional replication 17. Quantitative factors and response functions 18. Response surface exploration Part IV. Coda: 19. Designing useful experiments References Index.

A computer-based methodology for matching polymer structures with required properties

Abstract: Methodologie dans laquelle un ordinateur genere des structures polymeres et evalue leurs proprietes en utilisant des relations empiriques. Une methode d'optimisation est ensuite employee pour selectionner les polymeres candidats qui repondent le mieux a une serie de specifications

Is the Regression Equation Adequate?-One Criterion

Abstract: A decision rule often used for the acceptance or rejection of a fitted regression equation is whether or not the regression F ratio exceeds the critical F value. In fact, all this tells us is whether the fitted equation is better than the mean as a predictor. Many times the experimenter's primary interest is how well the fitted equation represents the true model. This paper presents a numerical criterion to help the experimenter evaluate the adequacy of the regression model, in light of both the range of values to be estimated by the equation and the size of the error term. This criterion can be tested utilizing the ordinary regression F ratio but referring to special critical values. An example from the rubber industry illustrates the use of the criterion.

Statistical Methods in Rubber Research and Development

Abstract: As this article has demonstrated, statistical methods have considerable utility in rubber research and development. Such methods are cost effective and perhaps, more important, help the us...

Applied Predictive Modeling

Abstract: General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

Statistical difficulties of detecting interactions and moderator effects

Abstract: Although interaction effects are frequently found in experimental studies, field researchers report considerable difficulty in finding theorized moderator effects. Previous discussions of this discrepancy have considered responsible factors including differences in measurement error and use of nonlinear scales. In this article we demonstrate that the differential efficiency of experimental and field tests of interactions is also attributable to the differential residual variances of such interactions once the component main effects have been partialed out. We derive an expression for this residual variance in terms of the joint distribution of the component variables and explore how properties of the distribution affect the efficiency of tests of moderator effects. We show that tests of interactions in field studies will often have less than 20% of the efficiency of optimal experimental tests, and we discuss implications for the design of field studies.

Quantifying the chemical beauty of drugs

Abstract: Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

Response Surface Methodology.

Abstract: The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.