Statistical Design And Analysis Of Experiments: With Applications To Engineering And Science, Second Edition

Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results.
Features numerous examples using actual engineering and scientific studies.
Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.
Deep and concentrated experimental design coverage, with equivalent but separate emphasis on the analysis of data from the various designs.
Topics can be implemented by practitioners and do not require a high level of training in statistics.
New edition includes new and updated material and computer output.

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.

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.