An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications. It is further enhanced through real-life examples drawn from many disciplines, showing the difficulties typically encountered in the practice of regression analysis. Consequently, this book provides a sound foundation in the theory of this important subject.
Regression Analysis: Theory, Methods, and Applications
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Editorial Reviews
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"I found this to be the most complete and up-to-date regression text I have come across...this text has much to offer." -Journal of the American Statistical Association "The material is presented in a lucid and easy-to-understand style...can be ranked as one of the best textbooks on regression in the market." -mathermatical Reviews "...a successful mix of theory and practice...It will serve nicely to teach both the logic behind regression and the data-analytic use of regression." -SIAM Review
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5.0 out of 5 stars
Rgression Analysis,
By Marc Buslik (Chicago, Il USA) - See all my reviews
This review is from: Regression Analysis: Theory, Methods, and Applications (Springer Texts in Statistics) (Hardcover)
This is the consummate regression book. It is ideal for grad work in stats. In particular the examples used are relevant and on target. A must for every social scientist's library.
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Inside This Book
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First Sentence:
The first question that comes to mind at this stage is: How well does our line fit the observed points? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
supervisor rating data, broken line regression, rankit plot, component plus residual plot, mean square error matrix, ridge estimate, counted variable, abbreviated model, studentized residuals, subsets search, independent variable values, influential points, estimated generalized least squares, variable selection procedures, deleting variables, ith case, idempotent matrix, ridge regression, forward selection procedure, shrinkage estimator, variance proportions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Continuation of Example, University of Illinois, Problems Exercise, Ann Arbor, Examples Example, Department of Geography, Ivory Coast, Saudi Arabia, Age Depr, Encyclopaedia Britannica, Rankit Plot of Standardized Residuals, Rent Model, Sum of Mean Source New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The first question that comes to mind at this stage is: How well does our line fit the observed points? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
supervisor rating data, broken line regression, rankit plot, component plus residual plot, mean square error matrix, ridge estimate, counted variable, abbreviated model, studentized residuals, subsets search, independent variable values, influential points, estimated generalized least squares, variable selection procedures, deleting variables, ith case, idempotent matrix, ridge regression, forward selection procedure, shrinkage estimator, variance proportions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Continuation of Example, University of Illinois, Problems Exercise, Ann Arbor, Examples Example, Department of Geography, Ivory Coast, Saudi Arabia, Age Depr, Encyclopaedia Britannica, Rankit Plot of Standardized Residuals, Rent Model, Sum of Mean Source New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 74
books:
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47
books
cite this book:
See all 47 books citing this book
- Prescriptions for Working Statisticians (Springer Texts in Statistics) by Albert Madansky in Front Matter, and Back Matter
- Methods of Correlation and Regression Analysis: Linear and Curvilinear by Mordecai Ezekiel on page 112, and Back Matter
- Psychological Statistics by Quinn McNemar on page 130, and Back Matter
- Applying Generalized Linear Models (Springer Texts in Statistics) by James K. Lindsey in Front Matter
- Elements of Large-Sample Theory (Springer Texts in Statistics) by E.L. Lehmann in Front Matter
See all 74 books this book cites
- Monte Carlo Statistical Methods (Springer Texts in Statistics) by Christian Robert in Back Matter
- An Introduction to Bayesian Analysis: Theory and Methods (Springer Texts in Statistics) by Jayanta K. Ghosh in Back Matter
- Statistical Analysis of Financial Data in S-PLUS by Rene A. Carmona in Back Matter
- An R and S-Plus® Companion to Multivariate Analysis (Springer Texts in Statistics) by Brian S. Everitt in Back Matter
- Essentials of Stochastic Processes by Richard Durrett in Back Matter
See all 47 books citing this book
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