Applied Regression Analysis: A Research Tool (Springer Texts in Statistics)

Rawlings, John O.; Pantula, Sastry G.; Dickey, David A.

ISBN 10: 0387984542 / ISBN 13: 9780387984544

Editore: Springer, 1998

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Good condition. 2nd edition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains. NOT AVAILABLE FOR SHIPMENT OUTSIDE OF THE UNITED STATES.

Codice articolo P01B-02688

Dati bibliografici

• Titolo: Applied Regression Analysis: A Research Tool (Springer Texts in Statistics)
• Editore: Springer
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 0387984542
• ISBN 13: 9780387984544
• Rilegatura: Rilegato
• Edizione: seconda edizione
• Condizione: Good

Descrizione articolo

Riassunto

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool.<br><b>Applied Regression Analysis</b> is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. <b>Applied Regression </b> <b>Analysis</b> serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course.<br><b>Applied Regression Analysis</b> emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet.<br><div><br></div>

Recensione

From the reviews:

IEEE ELECTRICAL INSULATION MAGAZINE

"Virtually all data taken require some form of modeling and curve fitting. This excellent book will give the reader a very clear understanding of the techniques used for fitting most types of data; and, because it covers all the significant areas, it can serve as a reference source. Students and especially researchers involved with data taking and modeling will greatly benefit from this book."