Applied Linear Statistical Models / Edition 3 by John Neter, William Wasserman, Michael H. Kutner | | 9780256083385 | Hardcover | Barnes & Noble
Applied Linear Statistical Models / Edition 3

Applied Linear Statistical Models / Edition 3

by John Neter, William Wasserman, Michael H. Kutner
     
 

ISBN-10: 025608338X

ISBN-13: 9780256083385

Pub. Date: 06/28/1990

Publisher: McGraw-Hill Higher Education

There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health,

Overview

There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities, and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.

Product Details

ISBN-13:
9780256083385
Publisher:
McGraw-Hill Higher Education
Publication date:
06/28/1990
Edition description:
Older Edition
Pages:
1184

Table of Contents

1 Linear Regression with One Independent Variable2 Inferences in Regression Analysis 3 Diagnostic and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis 6 Multiple Regression I 7 Multiple Regression II 8 Building the Regression Model I: Selection of Predictor Variables 9 Building the Regression Model II: Diagnostics 10 Building the Regression Model III: Remedial Measures and Validation 11 Qualitative Predictor Variables 12 Autocorrelation in Time Series Data 13 Introduction to Nonlinear Regression 14 Logistic Regression, Poisson Regression, and Generalized Linear Models 15 Normal Correlation Models 16 Analysis of Variance 17 Analysis of Factor-Level Effects 18 ANOVA Diagnostics and Remedial Measures 19 Two-Factor Analysis of VarianceßEqual Sample Sizes 20 Analysis of Factor Effects in Two-Factor StudiesßEqual Sample Sizes 21 Two-Factor StudiesßOne Case per Treatment (and more...)

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