This practitioner´s guide to statistical methods for designing and analyzing experiments is for engineers and scientists who utilize statistical approaches to solving problems in an experimental setting. Now in its Second Edition, the book gathers together the statistical techniques most useful to experimenters and data analysts who must either collect, analyze, or interpret data. The material is of value to managers, supervisors, and other administrators who must make decisions based in part on the analyses of data that may have been performed by others.
This practitioner´s guide to statistical methods for designing and analyzing experiments is for engineers and scientists who utilize statistical approaches to solving problems in an experimental setting. Now in its Second Edition, the book gathers together the statistical techniques most useful to experimenters and data analysts who must either collect, analyze, or interpret data. The material is of value to managers, supervisors, and other administrators who must make decisions based in part on the analyses of data that may have been performed by others. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
ROBERT L. MASON, PhD, is Institute Analyst at Southwest Research Institute in San Antonio, Texas. RICHARD F. GUNST, PhD, is a professor in the Department of Statistical Science at Southern Methodist University in Dallas, Texas. JAMES L. HESS, PhD, is Staff Vice President, Operations, at Leggett & Platt Inc. in Carthage, Missouri.
Inhaltsangabe
Preface. PART I: FUNDAMENTAL STATISTICAL CONCEPTS. Statistics in Engineering and Science. Fundamentals of Statistical Inference. Inferences on Means and Standard Deviations. PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE. Statistical Principles in Experimental Design. Factorial Experiments in Completely Randomized Designs. Analysis of Completely Randomized Designs. Fractional Factorial Experiments. Analysis of Fractional Factorial Experiments. PART III: DESIGN AND ANALYSIS WITH RANDOM EFFECTS. Experiments in Randomized Block Designs. Analysis of Designs with Random Factor Levels. Nested Designs. Special Designs for Process Improvement. Analysis of Nested Designs and Designs for Process Improvement. PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND FACTORS. Linear Regression with One Predicator Variables. Linear Regression with Several Predicator Variables. Linear Regression with Factors and Covariates as Predictors. Designs and Analyses for Fitting Re sponse Surfaces. Model Assessment. Variable Selection Techniques. Appendix: Statistical Tables. Index.
Preface. PART I: FUNDAMENTAL STATISTICAL CONCEPTS. Statistics in Engineering and Science. Fundamentals of Statistical Inference. Inferences on Means and Standard Deviations. PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE. Statistical Principles in Experimental Design. Factorial Experiments in Completely Randomized Designs. Analysis of Completely Randomized Designs. Fractional Factorial Experiments. Analysis of Fractional Factorial Experiments. PART III: DESIGN AND ANALYSIS WITH RANDOM EFFECTS. Experiments in Randomized Block Designs. Analysis of Designs with Random Factor Levels. Nested Designs. Special Designs for Process Improvement. Analysis of Nested Designs and Designs for Process Improvement. PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND FACTORS. Linear Regression with One Predicator Variables. Linear Regression with Several Predicator Variables. Linear Regression with Factors and Covariates as Predictors. Designs and Analyses for Fitting Re sponse Surfaces. Model Assessment. Variable Selection Techniques. Appendix: Statistical Tables. Index.
Rezensionen
"With an excellent presentation, this is suitable as a textbook in a graduate level course in design of experiments." (Journal of Statistical Computation and Simulation, April 2005) "...can really provide useful information for the intended audience..." (Zentralblatt Math, Vol. 1029, 2004) "...a practitioner's guide to statistical methods for designing and analyzing experiments..." (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003) "...a perfect desktop reference..." (Technometrics, Vol. 45, No. 3, August 2003)
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