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Multiple Regression: A Primer (Research Methods and Statistics)
Free Download Multiple Regression: A Primer (Research Methods and Statistics)
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Review
"I find the concept for this approach to teaching undergraduate research methods and statistics innovative and exciting. The idea of ‘core’ texts supplemented by satellite volumes covering specific, important topics makes absolute sense." Author: Barbara Heyl
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About the Author
Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics.After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data.A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.â€Â
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Product details
Series: Research Methods and Statistics
Paperback: 202 pages
Publisher: Pine Forge Press; 1st edition (December 29, 1998)
Language: English
ISBN-10: 9780761985334
ISBN-13: 978-0761985334
ASIN: 0761985336
Product Dimensions:
6 x 0.5 x 9 inches
Shipping Weight: 8.8 ounces (View shipping rates and policies)
Average Customer Review:
3.9 out of 5 stars
17 customer reviews
Amazon Best Sellers Rank:
#107,303 in Books (See Top 100 in Books)
This book provides a good conceptual explanation of regression. Mind you, it is still very dry, but that's the nature of the subject matter.The book, unfortunately, uses minimal examples to explain regression and has little to no practice problems. As with all mathematical subjects, I believe the key to understanding is practice and application of the mathematical principles to problems. In that regard, the book left much wanting.To be fair, the author uses few key examples to explain and apply regression principles. But I felt there needed to be much more.This is a paperback and a short book. It is rather skimpy. For that, I felt the price of the book was rather high. But then again, books on regression analysis aren't exactly best-sellers and I suppose the price needs to be high to make-up for the lack of demand. Still, I can't say the book was worth the price.If you want to understand regression better, then this is a worthwhile book. If you want to supplement your good understanding of regression, then I recommend this book. If you want a book that will allow you to understand AND learn regression through practice problems, then look elsewhere.
This easy-to-understand text is useful as preliminary or ancillary reading in a first course in regression analysis. For students having trouble with Gujarati, Wooldridge, Chatterjee and Hadi, or other standard introductory texts, the material contained in this brief account will almost certainly be helpful.Allison's text provides the sort of overview that enables students to place detailed treatments of specific topics in a coherent context. This helps them understand just what they are trying to accomplish with all the tests, correctives, and relatively advanced analytical procedures that make up the bulk of usual textbooks on this widely used and increasingly technical topic.The underlying logic of regression analysis can be understood by readers who lack what is sometimes referred to as mathematical maturity. This is especially pertinent with regard to multiple regression and the crucial issue of statistical control. The author does a fine job of making this about as clear as it can be.
This book positions itself as a gentler introduction to multiple regression which is both good and bad. The prose makes it easy to read, but in the end this book should have used formatting that better outlines the step-by-step approach needed in multiple regression problems. I swear I'm not kidding here; they do examples where they insert math steps in the middle of sentences in the middle of paragraphs. That doesn't help anyone. Put the examples in clearly outlined boxes like a "real" textbook and outline the steps. The author has written a really decent book - but in the next edition he shouldn't be shy about giving readers enough step-by-step outlines to try some problems. He seems to back off of that for fear of seeming too "technical." Don't worry about it - readers know how to skip ahead. We've been doing it for years.
Of all the regression books I was assigned, this one made the process easy to understand. By using real-world examples and descriptive words, readers are able to digest the information easily.
Really good book for a rough overview of MR
I hesitated when I saw "undergraduate" since it is actually listed as a textbook in my graduate class. I ended up buying anyway, and I really like it! For me, the first half is a quick over view and the second half is a nice summary of what I have learned before and quick making-up of something I was not aware of. After all, I would say it is a keeper.
This book is easy to read and follow. I would recommend this for anyone who is diving into the world of research and trying to better understand multiple regression.
Good book to read for new student learning regression
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