Applied Linear Statistical Models.

KUTNER, Michael H., Christopher J. NACHTSHEIM, John NETER & William LI

ISBN 10: 007310874X / ISBN 13: 9780073108742

Editore: McGraw-Hill, Singapore, 2005

Riguardo questo articolo

5th International ed. Singapore, McGraw-Hill (2005). gr.8°. XXVIII, 1396 p. Pbck. Name on half title.

Codice articolo 95139

Dati bibliografici

• Titolo: Applied Linear Statistical Models.
• Editore: McGraw-Hill, Singapore
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 007310874X
• ISBN 13: 9780073108742
• Rilegatura: Softcover
• Edizione: quinta edizione o successive
• Condizione: Sehr gut

Descrizione articolo

Riassunto

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. 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. 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.

Contenuti

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

29 Exploratory 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