Applied Linear Statistical Models

Michael H. Kutner

ISBN 10: 0071122214 / ISBN 13: 9780071122214

Editore: McGraw-Hill Education, 2004

Riguardo questo articolo

Codice articolo 9780071122214-2-2

Dati bibliografici

• Titolo: Applied Linear Statistical Models
• Editore: McGraw-Hill Education
• Data di pubblicazione: 2004
• Lingua: Inglese
• ISBN 10: 0071122214
• ISBN 13: 9780071122214
• Rilegatura: Brossura
• Edizione: quinta edizione o successive
• Condizione: Very good

Descrizione articolo

Riassunto

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. 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. 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 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 where methods can be automated within software without loss of understanding, it is so done.

Contenuti

Part1 Simple Linear Regression

1 Linear Regression with One Predictor Variable

2 Inferences in Regression 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 Regression Models for Quantitative and Qualitative Predictors

9 Building the Regression Model I: Model Selection and Validation

10Building the Regression Model II: Diagnostics

11Building the Regression Model III: Remedial Measures

12Autocorrelation in Time Series Data

Part 3NonLinear Regression

13Introduction to NonLinear Regression and Neural Networks

14Logistic Regression, Poisson Regression, and Generalized Linear Models

Part 4 Single Factor Studies

15Introduction to the Design of Experiments

16Analysis of Single-Factor Studies

17Analysis of Factor Level Effects in Single Factor Studies

18ANOVA Diagnostics and Remedial Measures

Part 5 Two -Factor Studies and Blocking

19Two -Factor Studies- Equal Sample Sizes

20 Two -Factor Studies-One Case per Cell

21 Randomized Complete Block Designs and the Analysis of Covariance

22Two -Factor Studies-Unequal Sample Sizes and Unequal Treatment Importance

Part 6 Multifactor Studies

23 Multifactor Studies

24Random and Mixed-Effects Models

25Nested Designs, Subsampling, and Partially Nested Designs

26Repeated Measures and Related Designs

27Latin Square, Balanced Incomplete Block, and Related Designs

28Exploratory Experiments-Two-Level Factorial and Fractional Factorial Designs

29Response Surface Experiments