Applied Linear Statistical Models with Student CD / Edition 5

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More About This Textbook

Overview

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear 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 "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies 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 the use of automated software without loss of understanding.

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Product Details

  • ISBN-13: 9780073108742
  • Publisher: McGraw-Hill Companies, The
  • Publication date: 8/10/2004
  • Series: The McGraw-Hill/Irwin Series
  • Edition description: New Edition
  • Edition number: 5
  • Pages: 1396
  • Product dimensions: 7.80 (w) x 9.40 (h) x 2.30 (d)

Table of Contents

Part 1 Simple Linear Regression
1 Linear Regression with One Predictor Variable
2 Inferences in Regression and Correlation Analysis
3 Diagnostic 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 Regression Models for Quantitative and Qualitative Predictors
9 Building the Regression Model I: Model Selection and Validation
10 Building the Regression Model II: Diagnostics
11 Building the Regression Model III: Remedial Measures
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
Part 4 Design and Analysis of Single-Factor Studies
15 Introduction to the Design of Experimental and Observational Studies
16 Single Factor Studies
17 Analysis of Factor-Level Means
18 ANOVA Diagnostics and Remedial Measures
Part 5 Multi-Factor Studies
19 Two Factor Studies with Equal Sample Sizes
20 Two Factor Studies-One Case per Treatment
21 Randomized Complete Block Designs
22 Analysis of Covariance
23 Two Factor Studies with Unequal Sample Sizes
24 MultiFactor Studies
25 Random and Mixed Effects Models
Part 6 Specialized Study Designs
26 Nested Designs, Subsampling, and Partially Nested Designs
27 Repeated Measures and Related Designs
28 Balanced Incomplete Block, Latin Square, and Related Designs
29Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs
30 Response Surface Methodology
Appendix A: Some Basic Results in Probability and Statistics
Appendix B: Tables
Appendix C: Data Sets
Appendix D: Rules for Develping ANOVA Models and Tables for Balanced Designs
Appendix E: Selected Bibliography

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