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06-19-2009, 11:08 AM #1
Applied Linear Statistical Models
Michael H Kutner, John Neter, Christopher J. Nachtsheim, William Wasserman, "Applied Linear Statistical Models"
McGraw-Hill Higher Education; 5 ed | 2004 | ISBN: 0071122214 | 1424 pages | Djvu | 9,7 MB
Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
From the Publisher
Applied Linear Regression Models assumes the use of computers. Thus, while the basic mathematical steps are given, the text does not dwell on computational details. This allows instructors to eliminate complex formulas and focus on basic principles.
Multiple linear regression analysis discussion starts the text.
Polynomial regression in now woven into the discussion of multiple linear regression.
Qualitative predictor variables now follows discussion of multiple regression model building and diagnostics.
There is an expanded discussion of diagnostics and remedial measures.
New topics added include: robust tests for constancy of the error variance, smoothing techniques to explore the shape of the regression function, robust regression and nonparametric regression techniques, bootstrapping methods for evaluating the precision of sample estimates for complex situations, and estimation of the variance and standard derivation functions to obtain weights for weighted least squares.
Chapter 14 has been revised and expanded to include introduction to polytomous logistic regression, Poisson regression, and generalized linear models.
A disk containing data sets for all examples, problems, exercises, and projects as well as data in Appendix C is packaged with each text.
Applied Linear Regression Models contains several new case studies at strategic places to aid understanding of the methods discussed.
A check in the margin of the problems section indicates the Student Solutions Manual provides immediate and final answers for self-checking.
The expanded use of graphs includes scatter plot matrices, three-dimensional rotating plots, and conditional effects plots.
The comprehensive use of computer and graphic plots helps focus the text on analysis and models.
A chapter on binary dependent variables reflects the trend to the increasing importance of logistic regression models for binary dependent variables in many areas of application.
Model building is thoroughly examined to allow students to see how the model-building process integrates many of the elements considered in earlier chapters.
The discussion of regression diagnostics includes the DFBETAS, DFFITS, and PRESS measures.