Table of contents for Applied linear statistical models.


Bibliographic record and links to related information available from the Library of Congress catalog


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 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 
 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 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
 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: ANOVA Models and Tables for Balanced Designs
Appendix E: Selected Bibliography




Library of Congress subject headings for this publication: Regression analysis, Mathematical statistics