Applied Linear Regression Models
McGraw Hill Higher Education (Publisher)
4th Edition
Published on 1. December 2003
Book
Paperback/Softback
978-0-07-123252-4 (ISBN)
Description
Kutner, Neter, Nachtsheim, Wasserman, "Applied Linear Regression Models, 4/e" (ALRM4e) is the long established leading authoritative text and reference on regression (previously Neter was lead author.) For students in most any discipline where statistical analysis or interpretation is used, "ALRM" has served as the industry standard. The text includes brief introductory and review material, and then proceeds through regression and modeling. 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 any discipline. "ALRM 4e" provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor by using larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
More details
Edition
4th Revised edition
Language
English
Place of publication
London
United States
Publishing group
McGraw-Hill Education - Europe
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 300 mm
Width: 900 mm
Thickness: 250 mm
Weight
1073 gr
ISBN-13
978-0-07-123252-4 (9780071232524)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
John Neter | etc. | Michael Kutner
Applied Linear Regression Models
Book
02/1996
McGraw-Hill Education (ISE Editions)
€49.51
Shipment within 15-20 days
Content
Part1 Simple Linear Regression 1 Linear Regression with One Predictor Variable 2 Inferences in Regression and Correlation Analysis 3 Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis Part 2 Multiple Linear Regression 6 Multiple Regression I 7 Multiple Regression II 8 Building the Regression Model I: Models for Quantitative and Qualitative Predictors 9 Building the Regression Model II: Model Selection and Validation 10 Building the Regression Model III: Diagnostics 11 Remedial Measures and Alternative Regression Techniques 12 Autocorrelation in Time Series Data Part 3 Nonlinear Regression 13 Introduction to Nonlinear Regression and Neural Networks 14 Logistic Regression, Poisson Regression, and Generalized Linear Models