» Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics)
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Rating: 3.5 / 5.00 (3 reviews)
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Manufacturer: Wiley-Interscience
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Rating: 3.5 / 5.00 (3 reviews)
Usually ships in 7 to 11 days
Manufacturer: Wiley-Interscience
Click to Buy
Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics) Details
Binding: HardcoverDewey Decimal Number: 519.536
EAN: 9780471754954
ISBN: 0471754951
Label: Wiley-Interscience
Manufacturer: Wiley-Interscience
Number Of Items: 1
Number Of Pages: 640
Publication Date: 2006-07-21
Publisher: Wiley-Interscience
Studio: Wiley-Interscience
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Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics) Reviews
Customer Rating:Summary: A good book with industrial applications
Comment: very useful for industrial applications. There are quite a few printing mistakes and that would be a problem for those reader they are not very strong in statistics.
Customer Rating:
Summary: Frustrated
Comment: Firstly, I am not stats guru, a mere mortal trying to get through a top-10 MBA program. I picked this book (and the companion text) to extend my knowledge, and it is helping. Tough for the gifted amateur, but interesting and the depth of the analysis truly helps.
Major negative comment : The manual often provides one sentence answers to mathematical questions. For example : "....slight improvement in the model." But this is not helpful. It lacks truly worked answers to the questions; which is essential to help those who are struggling to get to the right level.
This is not a limited observation, it is the trend and considerably more work could be done to explain how answers were derived, as it also helps find out why an answer might not be correct.
As for the up side. All chapter questions are answered and the FTP site provides raw data files. This is greatly appreciated. In balance, the weaknesses are significant.
Customer Rating:
Summary: Excellent introduction to linear regression
Comment: If you have a desire or need to develop regression models, whether for prediction or classification, this is a great place to start climbing the learning curve. The book covers all the essentials, such as how to fit a model to a set of data, how to evaluate the quality of the fit, and how to detect influential data points. It also does a good job with some of the issues involved in fitting a regression (most notably colinearity, overfitting, outliers, and deviations from normality) and discusses ridge regression, principal components regression, and other so-called "robust" methods for dealing with such issues. Even if you plan to use nonlinear modelling techniques like polynomial regression or feed-forward neural networks, this book is worth reading: many of the same issues that are involved when developing linear regression models arise in the context of nonlinear models. I use multivariate polynomial regression models for pricing options, and cite this book in my own recent work on that subject--"Advanced Option Pricing Models" (McGraw Hill, Feb 2005).
Jeffrey Owen Katz, Ph.D.
Author (with Donna L. McCormick) of "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000).
Editorial Review for Introduction to Linear Regression Analysis (Wiley Series in Probability and Statistics):
A comprehensive and up-to-date introduction to the fundamentals of regression analysisThe Fourth Edition of Introduction to Linear Regression Analysis describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. This popular book blends both theory and application to equip the reader with an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. It assumes a working knowledge of basic statistics and a familiarity with hypothesis testing and confidence intervals, as well as the normal, t, x2, and F distributions.
Illustrating all of the major procedures employed by the contemporary software packages MINITAB(r), SAS(r), and S-PLUS(r), the Fourth Edition begins with a general introduction to regression modeling, including typical applications. A host of technical tools are outlined, such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. Subsequent chapters discuss:
* Indicator variables and the connection between regression and analysis-of-variance models
* Variable selection and model-building techniques and strategies
* The multicollinearity problem--its sources, effects, diagnostics, and remedial measures
* Robust regression techniques such as M-estimators, and properties of robust estimators
* The basics of nonlinear regression
* Generalized linear models
* Using SAS(r) for regression problems
This book is a robust resource that offers solid methodology for statistical practitioners and professionals in the fields of engineering, physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Both the accompanying FTP site, which contains data sets, extensive problem solutions, software hints, and PowerPoint(r) slides, as well as the book's revised presentation of topics in increasing order of complexity, facilitate its use in a classroom setting.
With its new exercises and structure, this book is highly recommended for upper-undergraduate and beginning graduate students in mathematics, engineering, and natural sciences. Scientists and engineers will find the book to be an excellent choice for reference and self-study.
