Student's Partial Solutions Manual T-A Applied Regression Analysis PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Student's Partial Solutions Manual T-A Applied Regression Analysis PDF full book. Access full book title Student's Partial Solutions Manual T-A Applied Regression Analysis by Hafner. Download full books in PDF and EPUB format.
Author: Hafner
Publisher: PWS Publishing Company
ISBN: 9780534915131
Category : Multivariate analysis
Languages : en
Pages : 112
Get Book
Book Description
* An introductory text for undergraduates, graduates, and working professionals; emphasizes applications in public health, biology, and the social and behavioral sciences.
Author: Hafner
Publisher: PWS Publishing Company
ISBN: 9780534915131
Category : Multivariate analysis
Languages : en
Pages : 112
Get Book
Book Description
* An introductory text for undergraduates, graduates, and working professionals; emphasizes applications in public health, biology, and the social and behavioral sciences.
Author: Terry Dielman
Publisher: South-Western College
ISBN: 9780534465506
Category : Commercial statistics
Languages : en
Pages : 176
Get Book
Book Description
Provides worked-out solutions to odd-numbered problems in the text.
Author: David G. Kleinbaum
Publisher: Cengage Learning
ISBN: 9781285175072
Category : Mathematics
Languages : en
Pages : 0
Get Book
Book Description
The SSM features worked solutions to select problems in Applied Regression Analysis and Other Multivariable Methods, 5. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: Michael Kutner
Publisher: McGraw-Hill Education
ISBN: 9780072918397
Category : Education
Languages : en
Pages :
Get Book
Book Description
Author: Terry E. Dielman
Publisher:
ISBN: 9780534380458
Category :
Languages : en
Pages :
Get Book
Book Description
Author: Douglas C. Montgomery
Publisher: John Wiley & Sons
ISBN: 1118548507
Category : Mathematics
Languages : en
Pages : 112
Get Book
Book Description
As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.
Author: David W. Hosmer, Jr.
Publisher: Wiley-Interscience
ISBN: 9780471208266
Category : Mathematics
Languages : en
Pages : 280
Get Book
Book Description
Presenting information on logistic regression models, this work explains difficult concepts through illustrative examples. This is a solutions manual to accompany applied Logistic Regression, 2nd Edition.
Author: John Neter
Publisher:
ISBN: 9780256117363
Category : Analysis of variance
Languages : en
Pages : 0
Get Book
Book Description
Author: Michael H. Kutner
Publisher: McGraw-Hill Education
ISBN: 9780071122214
Category : Analysis of variance
Languages : en
Pages : 1396
Get Book
Book Description
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.
Author: Trevor Hastie
Publisher: Springer Science & Business Media
ISBN: 0387216065
Category : Mathematics
Languages : en
Pages : 545
Get Book
Book Description
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
