Nonlinear Statistical Methods A. Ronald Gallant Describes the recent advances in statistical and probability theory that have removed obstacles to an adequate theory of estimation and inference for nonlinear models. Thoroughly explains theory, methods, computations, and applications. Covers the three major categories of statistical models that relate dependent variables to explanatory variables: univariate regression models, multivariate regression models, and simultaneous equations models. Includes many figures which ...
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Nonlinear Statistical Methods A. Ronald Gallant Describes the recent advances in statistical and probability theory that have removed obstacles to an adequate theory of estimation and inference for nonlinear models. Thoroughly explains theory, methods, computations, and applications. Covers the three major categories of statistical models that relate dependent variables to explanatory variables: univariate regression models, multivariate regression models, and simultaneous equations models. Includes many figures which illustrate computations with SAS(R) code and resulting output. 1987 (0 471-80260-3) 610 pp. Exploring Data Tables, Trends, and Shapes Edited by David C. Hoaglin, Frederick Mosteller, and John W. Tukey Together with its companion volume, Understanding Robust and Exploratory Data Analysis, this work provides a definitive account of exploratory and robust/resistant statistics. It presents a variety of more advanced techniques and extensions of basic exploratory tools, explains why these further developments are valuable, and provides insight into how and why they were invented. In addition to illustrating these techniques, the book traces aspects of their development from classical statistical theory. 1985 (0 471-09776-4) 672 pp. Robust Regression & Outlier Detection Peter J. Rousseeuw and Annick M. Leroy An introduction to robust statistical techniques that have been developed to isolate or identify outliers. Emphasizes simple, intuitive ideas and their application in actual use. No prior knowledge of the field is required. Discusses robustness in regression, simple regression, robust multiple regression, the special case of one-dimensional location, and outlier diagnostics. Also presents an outlook of robustness in related fields such as time series analysis. Emphasizes "high-breakdown" methods that can cope with a sizable fraction of contamination. Focuses on the least median of squares method, which appeals to the intuition and is easy to use. 1987 (0 471-85233-3) 329 pp.
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Add this copy of Applied Linear Regression Fourth Edition to cart. $162.45, new condition, Sold by Books2anywhere rated 5.0 out of 5 stars, ships from Fairford, GLOUCESTERSHIRE, UNITED KINGDOM, published 2014 by Wiley-Blackwell.
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This is an ex-library book and may have the usual library/used-book markings inside. This book has hardback covers. Book contains highlighter markings. In poor condition, suitable as a reading copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item, 650grams, ISBN: 9780471663799.
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This is an ex-library book and may have the usual library/used-book markings inside. This book has hardback covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item, 650grams, ISBN: 9780471663799.
Add this copy of Applied Linear Regression to cart. $28.84, fair condition, Sold by Anybook rated 5.0 out of 5 stars, ships from Lincoln, UNITED KINGDOM, published 2005 by Wiley Interscience.
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This is an ex-library book and may have the usual library/used-book markings inside. This book has hardback covers. Clean from markings. In fair condition, suitable as a study copy. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item, 650grams, ISBN: 9780471663799.
Concise, clear overview of application of the method of multiple linear regression, including guidance for interpreting the quality and suitability of analysis results. Much of the exposition is framed in terms of linear algebra, which is extremely helpful for converting into a programmatic implementation in Excel. Highly recommended for those grappling with multidimensional datasets.
