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Introduction to Regression Modeling (with CD-ROM) (Duxbury Applied) 1st Edition

by Bovas Abraham (Author), Johannes Ledolter (Author)
4.7 4.7 out of 5 stars 19 ratings
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Looking for an easy-to-understand text to guide you through the tough topic of regression modeling? INTRODUCTION TO REGRESSION MODELING (WITH CD-ROM) offers a blend of theory and regression applications and will give you the practice you need to tackle this subject through exercises, case studies. and projects that have you identify a problem of interest and collect data relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.
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  1. ISBN-10
    0534420753
  2. ISBN-13
    978-0534420758
  3. Edition
    1st
  4. Publisher
    Cengage Learning
  5. Publication date
    January 20, 2005
  6. Language
    English
  7. Dimensions
    8.75 x 1 x 9.75 inches
  8. Print length
    448 pages
  9. See all details
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Editorial Reviews

About the Author

Bovas Abraham is the former Director of the Institute for Improvement in Quality and Productivity, and is also a professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Bovas received his Ph.D. from the University of Wisconsin, Madison. He has held visiting positions at the University of Wisconsin, the University of Iowa, and the University of Western Australia. He is the author of the book STATISTICAL METHODS FOR FORECASTING (with Johannes Ledolter) published by Wiley in 1983, and the editor of the volume QUALITY IMPROVEMENT THROUGH STATISTICAL METHODS published by Birkhauser in 1998.

Johannes Ledolter is the John F. Murray Professor of Management Sciences at the University of Iowa, and a Professor at the Vienna University of Economics and Business Administration. His graduate degrees are in Statistics (M.S. and Ph.D. from the University of Wisconsin, and M.S. from the University of Vienna). He has held visiting positions at Princeton University and Yale University. He is the author of four books: STATISTICAL METHODS FOR FORECASTING (with Bovas Abraham) published by Wiley in 1983, STATISTICS FOR ENGINEERS AND PHYSICAL SCIENTISTS (2nd edition, with Robert V. Hogg) published by Macmillan in 1991, STATISTICAL QUALITY CONTROL (with Claude W. Burrill) published by Wiley in 1999, and ACHIEVING QUALITY THROUGH CONTINUAL IMPROVEMENT (with Claude W. Burrill) published by Wiley in 1999.

Product details

  • Publisher ‏ : ‎ Cengage Learning; 1st edition (January 20, 2005)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 448 pages
  • ISBN-10 ‏ : ‎ 0534420753
  • ISBN-13 ‏ : ‎ 978-0534420758
  • Item Weight ‏ : ‎ 1.97 pounds
  • Dimensions ‏ : ‎ 8.75 x 1 x 9.75 inches
  • Customer Reviews:
    4.7 4.7 out of 5 stars 19 ratings

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  1. Johannes Ledolter

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Customer reviews

4.7 out of 5 stars
4.7 out of 5
19 global ratings

Top reviews from the United States

MathNStats
5.0 out of 5 stars My Favorite Regression Text
Reviewed in the United States on May 30, 2010
Verified Purchase
Easily the best book written at this level (which I'd say is somewhere around the Master's Level).

Pros:
1. Good selection of topics. I like that the authors include a chapter on model selection, logistic/poisson regression, time series, etc...
2. This book definitely doesn't skimp on the theory. While an undergrad course may want to skip some of the details, especially without knowledge of Linear Algebra, I found it extremely helpful that everything is carefully explained and proofs to all theorems are provided.
3. Good selection of problems. I like that the problem sets include a nice mixture of applied and theoretical problems. Also, some solutions are provided, which may or may not be a good thing.

Cons:
1. No software code or explanations. It would be very helpful if authors included at least some of the commands in the text. Or even an online reference which provides the code to the book's examples would have been great.
2. Thin pages and low-quality binding. Also, this book is a little on the pricey side.
11 people found this helpful
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Paul Jimenez
5.0 out of 5 stars Awesome textbook
Reviewed in the United States on June 10, 2015
Verified Purchase
I used this textbook for an undergraduate class. It is by far one of my favorite textbooks I have used. It is very thorough in its explanations and paints a clear picture for anyone to understand. The progression of the book is very easy to follow. There are even a couple sections dedicated to linear algebra for review.
2 people found this helpful
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YIFAN ZHANG
5.0 out of 5 stars nice
Reviewed in the United States on September 20, 2013
Verified Purchase
nice,Introduction to Regression Modeling (with CD-ROM) (Duxbury Applied)
Bovas Abraham,nice,Introduction to Regression Modeling (with CD-ROM) (Duxbury Applied)
Bovas Abraham,nice .
One person found this helpful
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Tyler Smith
5.0 out of 5 stars loved
Reviewed in the United States on August 20, 2016
Verified Purchase
New book! loved it
One person found this helpful
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Amazon Customer
5.0 out of 5 stars Five Stars
Reviewed in the United States on November 13, 2015
Verified Purchase
Useful book
One person found this helpful
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Alexander C. Zorach
5.0 out of 5 stars Overpriced, but an excellent book: very thorough and sound.
Reviewed in the United States on December 13, 2007
This is an accessible and very well-written introductory book on linear regression that goes much deeper than most books written at its level. Compared with other similar books, it is much more thoughtful and critical. This is one of the few introductory books on regression that gets people to actually think about the material and does not set up bad habits or encourage people to proceed blindly.

The prerequisites for this book are modest. It does require some math skills, particularly, ability to manipulate summation notation effectively. Some parts of the book require linear algebra. The book has a short review of linear algebra, which is not enough to teach the subject on its own, but might be useful review for someone with prior exposure. A few sections are genuinely hard (such as the implementation of nonlinear regression) and require more math skills, but these are not central to the flow of the book and can be omitted. In terms of other background, some general life experience and familiarity with the social sciences or natural sciences would be helpful in order to appreciate the motivation behind and applications of the material.

This book offers a remarkably exhaustive a coverage of the conventional topics in regression. The choice and order of topics is natural. I particularly like the way the book includes chapters on model selection and model checking, and explores these issues in depth. These are issues that are often glossed over.

Perhaps the best aspect of this book is the interplay between mathematical theory and applications. The mathematical details are expanded in rich discussion which helps the reader to develop mathematical intuition, and these details are presented in ways that enrich the discussion of applications. This is one of the few books that presents both the mathematical and practical sides of the subject well and integrates them fully. It takes skill and thought to create a book as well-executed as this one, and I think the authors deserve credit for this!

This book would make an excellent textbook for a first course in regression. It would remain a useful reference for more advanced students. People looking for a more advanced book on the same subject might want to check out the book "Statistical Models" by Freedman, or the "Regression Analysis: A Constructive Critique" by Berk.

Lastly, I must mention, the price of this book is absurd! This book should be less than half the cost. It's not that long and it has the usual cheap glued binding of a textbook. At $122, the glued binding for this book should constitute a criminal offense.
11 people found this helpful
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AGW
5.0 out of 5 stars Excellent book, both to learn from, and teach out of.
Reviewed in the United States on April 10, 2019
Quite simply one of the best books on regression and modeling ever written. Combines excellent choice of interesting real life data sets with notably clear mathematical exposition, particularly in regard to matrices.
One person found this helpful
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