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Multiple Regression: A Primer (Research Methods and Statistics), by Paul D. Allison
Free PDF Multiple Regression: A Primer (Research Methods and Statistics), by Paul D. Allison
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Presenting topics in the form of questions and answers, this popular supplemental text offers a brief introduction on multiple regression on a conceptual level. Author Paul D. Allison answers the most essential questions (such as how to read and interpret
- Sales Rank: #295589 in Books
- Published on: 1998-12-29
- Released on: 1998-12-29
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .51" w x 6.00" l, .68 pounds
- Binding: Paperback
- 202 pages
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."
(Barbara Heyl)
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.”
Most helpful customer reviews
1 of 1 people found the following review helpful.
Good explanations; Very little practice problems.
By Jaewoo Kim
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.
2 of 2 people found the following review helpful.
Good introduction to regression
By not a natural
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.
1 of 1 people found the following review helpful.
good buy
By Hongyu Zhang
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.
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