This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed ...
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This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques. The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may be skipped by readers not interested in S-Plus.
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Add this copy of Applied Smoothing Techniques for Data Analysis: the to cart. $28.87, good condition, Sold by 4everBAM rated 4.0 out of 5 stars, ships from parker, CO, UNITED STATES, published 1997 by Oxford University Press.
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Very Good. Size: 9x6x0; ***VERY GOOD*** Contains one or two pages of minor writing or highlights, and Underlines. Legible and in good shape. Minor to slightly heavy wears on cover from warehouse shelves. (Used so may not contain codes/CDs/Inserts that is included with the book.
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