Applications of Statistics to Industrial Experimentation Hardcover - 1976 - 1st Edition

From the rear cover

Other volumes in the Wiley Series in Probability and Mathematical Statistics, Ralph A. Bradley, J. Stuart Hunter, David G. Kendall, & Geoffrey S. Watson, Advisory Editors Statistical Models in Applied Science Karl V. Bury Of direct interest to engineers and applied scientists, this book presents general principles of statistics and specific distribution methods and models. Prominent distribution properties and methods that are useful over a wide range of applications are covered in detail. The strengths and weaknesses of the distributional models are fully described, giving the reader a firm, intuitive approach to the selection of the model most appropriate to the problem at hand. 1975 656 pp. Fitting Equations To Data Computer Analysis of Multifactor Data for Scientists and Engineers Cuthbert Daniel & Fred S. Wood With the assistance of John W. Gorman The purpose of this book is to help the serious data analyst, scientist, or engineer with a computer to: recognize the strengths and limitations of his data; test the assumptions implicit in the least squares methods used to fit the data; select appropriate forms of the variables; judge which combinations of variables are most influential; and state the conditions under which the fitted equations are applicable. Throughout, mathematics is kept at the level of college algebra. 1971 342 pp. Methods for Statistical Analysis of Reliability And Life Data Nancy R. Mann, Ray E. Schafer & Nozer D. Singpurwalla This book introduces failure models commonly used in reliability analysis, and presents the most useful methods for analyzing the life data of these models. Highlights include: material on accelerated life testing; a comprehensive treatment of estimation and hypothesis testing; a critical survey of methods for system-reliability confidence bonds; and methods for simulation of life data and for testing fit. 1974 564 pp.

From the jacket flap

Mr. Daniel's book concerns itself with statistical aids available for the planning of industrial experiments and with the analysis and interpretation of the data collected from such experiments. In it, he questions some standard designs and procedures in analysis, contending that many can be checked only after the data are in. There are many detailed examples. Chapters 1-3: Design of multifactor industrial experiments has become routinized. In addition, analysis of resulting data is often handled by computer programs long-since outdated--programs still being taught on the university level and still part of most statistical texts. Chapter 4: Here the study of data from simple two-factor three-level designs is carried further than in other available works. Spotting and interpretation of irregular results are feasible even in such small data sets. Chapters 5-8: Size of industrial experiments is rarely determined by standard statistical criteria of power or by shortness of confidence intervals. The real criteria are usually time- and budget-restrictions, and nearness to final decision on marketability. Chapter 10: Blocking of factorial and fractional factorial plans is considered. Chapters 11-13: The rationale of fractionation of factorial designs is given in new detail. Chapter 14: Clearing up residual ambiguities after completion of a fractional design is often proposed by way of doubling the amount of work really needed. Daniel points out that much smaller augmentations are often feasible, and their selection is detailed for many cases. Chapter 15: When smooth time trends (linear and quadratic) are known to occur, more efficient and compact plans than the usual blocked designs are possible. Here they are offered. Chapter 16: Serious examples of nested designs require special care--especially in analysis. Here is a volume that should be valuable to experimenters who have some knowledge of elementary statistics (at least one year of undergraduate-level statistics), and to the statistician who seeks simple explanations, detailed examples, and documentation of the many outcomes that can be expected to occur.