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Applied Linear Statistical Models by Michael H. Kutner, John Neter, William Wasserman and Chris J. Nachtsheim (1996, Hardcover)

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Pages: 1408.

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Product Information

This text uses an applied approach, with an emphasis on the understanding of concepts and exposition by means of examples. Sufficient theoretical information is provided to enable applications of regression analysis to be carried out. Case studies are used to illustrate many of the statistical methods. There is coverage of composite designs for response surface studies and an introduction to the use of computer-generated optimal designs. The Holm procedure is featured, as well as the analysis of means of identifying important effects. This edition includes an expanded use of graphics: scatter plot matrices, three-dimensional rotating plots, paired comparison plots, three-dimensional response surface and contour plots, and conditional effects plots. An accompanying Student Solutions Manual works out problems in the text.

Product Identifiers

Publisher
McGraw-Hill Higher Education
ISBN-10
0256117365
ISBN-13
9780256117363
eBay Product ID (ePID)
80779

Product Key Features

Author
John Neter, Michael H. Kutner, William Wasserman, Chris J. Nachtsheim
Publication Name
Applied Linear Statistical Models
Format
Hardcover
Language
English
Publication Year
1996
Type
Textbook
Number of Pages
1408 Pages

Dimensions

Item Length
10.3in
Item Height
2in
Item Width
8.3in
Item Weight
96.2 Oz

Additional Product Features

Lc Classification Number
Qa278.2.A66 1996
Edition Number
4
Table of Content
1 Linear Regression with One Independent Variable 2 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 22 Two Factor StudiesßUnequal Sample Sizes and Unequal Treatment Importance 23 Multi-Factor Studies 24 Random and Mixed-Effect Models 25 Analysis of Covariance 26 Design of Experiments, Randomization, and Sample Size Planning 27 Randomized Block Designs 28 Nested Designs, Subsampling, and Partially Nested Designs 29 Repeated Measure Designs 30 Latin Square and Related Designs 31 Explanatory Experiments--Two-level Factorial and Fractional Factorial Designs 32 Response Surface Methodology Appendixes
Copyright Date
1996
Target Audience
College Audience
Topic
Probability & Statistics / General, Probability & Statistics / Regression Analysis, Statistics, Research & Methodology
Lccn
95-037447
Dewey Decimal
519.5
Dewey Edition
20
Illustrated
Yes
Genre
Business & Economics, Science, Mathematics

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Picture 1 of 1

Applied Linear Statistical Models by Michael H. Kutner, John Neter, William Wasserman and Chris J. Nachtsheim (1996, Hardcover)

About this product

Product Information

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

All listings for this product

item 1 Applied Linear Statistical Models Hardcover

item 2 Applied Linear Statistical Models, Neter, John

item 3 Applied Linear Statistical Models by Neter; Neter, John; Kutner, Michael H.

item 4 Applied Linear Statistical Models

item 5 Applied Linear Statistical Models by Neter; Neter, John; Kutner, Michael H.

item 6 Applied Linear Statistical Models by Neter; Neter, John; Kutner, Michael H.

item 7 Applied Linear Statistical Models - hardcover Neter,John

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