Statistical Design and Analysis of Experiments: With Applications to Engineering and Science / Edition 2

Statistical Design and Analysis of Experiments: With Applications to Engineering and Science / Edition 2

by Robert L. Mason, Richard F. Gunst, James L. Hess
     
 

Praise for the First Edition
Statistical Design and Analysis of Experiments

"A very useful book for self study and reference."
Journal of Quality Technology

"Very well written. It is concise and really packs a lot of material in a valuable reference book."
Technometrics

"An informative and

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Overview

Praise for the First Edition
Statistical Design and Analysis of Experiments

"A very useful book for self study and reference."
Journal of Quality Technology

"Very well written. It is concise and really packs a lot of material in a valuable reference book."
Technometrics

"An informative and well-written book . . . presented in an easy-to-understand style with many illustrative numerical examples taken from engineering and scientific studies."
Choice (American Library Association)

Practicing engineers and scientists often have a need to utilize statistical approaches to solving problems in an experimental setting. Yet many have little formal training in statistics. Statistical Design and Analysis of Experiments gives such readers a carefully selected, practical background in the statistical techniques that are most useful to experimenters and data analysts who collect, analyze, and interpret data.

The First Edition of this now-classic book garnered praise in the field. Now its authors update and revise their text, incorporating readers’ suggestions as well as a number of new developments. Statistical Design and Analysis of Experiments, Second Edition emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results, presenting statistics as an integral component of experimentation from the planning stage to the presentation of conclusions.

Giving an overview of the conceptual foundations of modern statistical practice, the revised text features discussions of:

  • The distinctions between populations or processes and samples; parameters and statistics; and mathematical and statistical modeling
  • The design and analysis of experiments with factorial structures, unbalanced experiments, crossed and nested factors, and random factor effects
  • Confidence-interval and hypothesis-testing procedures for single-factor and multifactor experiments
  • Quantitative predictors and factors, including linear regression modeling using least-squares estimators, with diagnostic techniques for assessing model assumptions

Ideal for both students and professionals, this focused and cogent reference has proven to be an excellent classroom textbook with numerous examples. It deserves a place among the tools of every engineer and scientist working in an experimental setting.

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Product Details

ISBN-13:
9780471372165
Publisher:
Wiley
Publication date:
02/14/2003
Series:
Wiley Series in Probability and Statistics Series, #356
Edition description:
REV
Pages:
760
Product dimensions:
6.46(w) x 9.39(h) x 1.66(d)

Table of Contents

Preface
Pt. IFundamental Statistical Concepts1
1Statistics in Engineering and Science3
2Fundamentals of Statistical Inference33
3Inference on Means and Standard Deviation69
Pt. IIDesign and Analysis with Factorial Structure107
4Statistical Principles in Experimental Design109
5Factorial Experiments in Completely Randomized Designs140
6Analysis of Completely Randomized Designs170
7Fractional Factorial Experiments228
8Analysis of Fractional Factorial Experiments271
Pt. IIIDesign and Analysis with Random Effects309
9Experiments in Randomized Block Designs311
10Analysis of Designs with Random Factor Levels347
11Nested Designs378
12Special Designs for Process Improvement400
13Analysis of Nested Designs and Designs for Process Improvement423
Pt. IVDesign and Analysis with Quantitative Predictors and Factors459
14Linear Regression with One Predictor Variable461
15Linear Regression with Several Predictor Variables496
16Linear Regression with Factors and Covariates as Predictors535
17Designs and Analyses for Fitting Response Surfaces568
18Model Assessment614
19Variable Selection Techniques659
App.: Statistical Tables689
Index723

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