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Logistic Regression Using the SAS System : Theory and Application
 
 
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Logistic Regression Using the SAS System : Theory and Application [Paperback]

Paul D. Allison (Author)
4.6 out of 5 stars  See all reviews (8 customer reviews)

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Book Description

March 23, 1999 1580253520 978-1580253529 1
If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, this book is for you! Informal and nontechnical, Paul Allison's Logistic Regression Using SAS: Theory and Application both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using SAS. Several social science real-world examples are included in full detail. The book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis with the PHREG procedure, and Poisson regression. Other highlights include discussions of how to use the GENMOD procedure to do log-linear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed.

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Editorial Reviews

Review

The book treats logistic regression as the next topic in the regression methodology, so one is expected to have learned regression analysis already. Consistent with the author's social-science background, he begins with models for dichotomous responses. The logic model is treated as the solution to the problems one would encounter with using linear regression for binary response data. Both PROC LOGISTIC and PROC GENMOD are introduced. PROC REG is cleverly included, though, as a diagnostic for collinearity. The excellent binary data diagnostics in PROC LOGISTIC and PROC GENMOD are covered extensively. The probit model is also considered. --Eric Ziegel, Technometrics, Book Review Editor

Users of other software and other books are probably tired of my hype for these inexpensive books that integrate applied statistics and SAS procedures. This is another great applied-statistics and SAS-application guidebook, which should be essential to the library of anyone who uses SAS. --Eric Ziegel, Technometrics, Book Review Editor

About the Author

Paul D. Allison

Paul D. Allison is a Professor of Sociology and Epidemiology at the

University of Pennsylvaniawhere he teaches graduate courses in survival

analysis and categorical data analysis. Every summer he teaches a

five-day workshop about logistic regression that is attended by researchers

from around the United States and Canada. Besides his numerous statistical

papers, he has also published extensively on the subject of scientists'

careers.


Product Details

  • Paperback: 302 pages
  • Publisher: SAS Publishing; 1 edition (March 23, 1999)
  • Language: English
  • ISBN-10: 1580253520
  • ISBN-13: 978-1580253529
  • Product Dimensions: 10.9 x 8.5 x 0.6 inches
  • Shipping Weight: 8.8 ounces (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #453,591 in Books (See Top 100 in Books)

More About the Author

Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania, and President of Statistical Horizons LLC. You can visit his website at www.StatisticalHorizons.com. After completing doctoral work 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, logit analysis, probit analysis, measurement error, inequality measures, missing data, Markov processes, and event history analysis. Much of his earlier research was focused on career patterns of academic scientists. At present, his principal methodological research is focused on the analysis of longitudinal data, especially with determining the causes and consequences of events, and on methods for handling missing data. Each summer he teaches 5-day workshops on survival analysis and logistic regression analysis that draw about 90 doctorate-level researchers from around the U.S. At Penn, he teaches advanced graduate courses on event history analysis, categorical data analysis, and structural equation models with latent variables. A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010, he was elected as a Fellow of the American Statistical Association.

 

Customer Reviews

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Average Customer Review
4.6 out of 5 stars (8 customer reviews)
 
 
 
 
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Most Helpful Customer Reviews

18 of 19 people found the following review helpful:
5.0 out of 5 stars Incredibly Helpful, February 6, 2000
By A Customer
This review is from: Logistic Regression Using the SAS System : Theory and Application (Paperback)
I had used this author's SAS book on survival analysis before, so I was expecting another virtuoso performance when this book came out. And I was not disappointed. The book is incredibly useful, provides clear examples, makes you feel like you really understand the statistics, provides voluminous SAS code to illustrate how to implement analyses, and even teaches you a few tricks about how to handle unusual data problems along the way. Highly recommended, even if you don't use SAS!
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11 of 14 people found the following review helpful:
4.0 out of 5 stars Show me how, August 8, 2001
This review is from: Logistic Regression Using the SAS System : Theory and Application (Paperback)
It is always nice to have an example. This is what this book gives you. It also gives you options. SAS commands are written clearly. It still can not be your only logistic regression book. You will need other books to have a better understanding of logistic regression.
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1 of 1 people found the following review helpful:
5.0 out of 5 stars Must have book for the SAS user, September 14, 2007
Amazon Verified Purchase(What's this?)
In the first place, I am a pathologist writing a review on a book on statistics. That, by itself, will raise a few eyebrows. Few pathologists, in my opinion, understand statistical jargon. Yet, I would recommend this $40 book to even a novice in pathology who wants to do logistic regression modeling. While this books does explain logistic regression like most other books, what sets this book apart, are the real-world examples and the simple SAS code included with each example?
I have specifically been able understand GEE modeling only in this book, as compared to the numerous other books, where I find the statistical jargon intimidating; so much so that, I have ended up getting confused even after a couple of hours worth of intense reading. If you are devoted SAS user, you have probably read this book already. But if you are a novice pathologist and want to start learning a new program, I would advise Stata rather than SAS. SAS, in my opinion is code heavy. Yet, this book will be very useful to understand the varied uses of logistic regression (from exact to GEE analyses, especially from a pathologist's perspective). All discussions are to the point without too much jargon. For $40 this book is great value. I am just waiting to see if a newer version comes up in the near future.
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Inside This Book (learn more)
First Sentence:
When I began graduate study at the University of Wisconsin in 1970, categorical data analysis consisted of chi-square tests for cross-tabulated data, a technique introduced around the turn of the century by the great Karl Pearson. Read the first page
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Estimate Std Err, Analysis Of Parameter Estimates Parameter, Value Value, Scaled Deviance, Scaled Pearson, Estimate Error Chi-Square Chi-Square Estimate Ratio, Standardized Odds Variable, Estimate Error Chi-Square Chi-Square Ratio, Risk Variable, Testing Global Null Hypothesis, Analysis Source, Statistics For Type, Chi-Square Deviance, Consider Table, Effect Parameter Estimate Error Square Prob, Number Value, Comparing Output, Observations Used, User's Guide, Doctorate Model, Frequency Percent Cumulative Frequency Cumulative Percent, Karl Pearson, Pearson's Chi-Squared
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