Helps any serious data analyst with a computer to recognize the strengths and limitations of data, to test the assumptions implicit in the least squares methods used to fit the data, to select appropriate forms of the variables, to judge which combinations of variables are most influential, and to state the conditions under which the fitted equations are applicable. This edition includes numerous extensions and new devices such as component and component-plus-residual plots, cross verification with a second sample, and an index of required x-precision; also, the search for better subset equations is enlarged to cover 262,144 alternatives. The methods described have been applied in agricultural, environmental, management, marketing, medical, physical, and social sciences. Mathematics is kept to the level of college algebra.
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"...a grand historical document for industrial statistics in its glory days, as its selection for the Classics Library implies." ––Technometrics Vol. 42, No. 4 May 2001
This book provides an excellent insight into the minds of two master craftsmen at work. I very much applaud the decision to include this in a "classics library" and would encourage more authors to produce statistics books in the same vein, i.e. focused on the practical application of the subject rather than methodology development. Anyone involved in the analysis of unbalanced multifactor dtaa will find this book an extremely useful source of practical advice. ––The Statistician 50 (1) 2001.
"...a grand historical document for industrial statistics in its glory days, as its selection for the Classics Library implies." ––Technometrics Vol. 42, No. 4 May 2001
Anyone involved in the analysis of unbalanced multifactor data will find this book an extremely useful source of practical advice.––The Statistician 50 (1) 2001.
Other volumes in the Wiley Series in Probability and Mathematical Statistics Applications of Statistics to Industrial Experimentation Cuthbert Daniel Here is a lucid examination of statistical aids and the planning, analysis, and interpretation of industrial experiments. The author scrutinizes the design of multifactor experiments and the methodology, employed in data analysis, with particular focus on time and budget restrictions, and nearness to final decision on marketability. Valuable to experimenters who have some knowledge of elementary statistics, and to statisticians who seek simple explanations, detailed examples, and documentation of the many results that can be expected. 1976 294 pp. Methods for Statistical Data Analysis of Multivariate Observations R. Gnanadesikan Brings together a variety of statistical techniques used and needed in the problems of multivariate data analysis. Concentrates on methods of analysis and the conceptual underpinnings, rather than on statistical theory. The book provides five general objectives for analyzing multivariate data: (1) reduction of dimensionality, (2) development and study of multivariate dependencies, (3) classification and clustering, (4) assessment of specific aspects of models, and (5) summarization and exposure. The author uses several examples of both computer-simulated and real data throughout the book to illustrate the techniques, 1977 311 pp. Introduction to Statistical Time Series Wayne A. Fuller Provides an introduction to representations for statistical time series and the estimation of time series models. Developing both the time and frequency domain approaches, the book concentrates on univariate time series and also discusses vector time series. "...well organized, well written, and fairly easy to follow... [with] many illustrative worked numerical examples throughout the text as well as a selected bibliography and a useful index. A valuable addition to the market. Recommended for all universities with graduate programs in statistics and economics." --Choice 1976 470 pp.
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