Applied linear statistical models
Kutner, Michael H et. al.
Applied linear statistical models - 5 - New Delhi McGraw Hill Education(India) Pvt. Ltd. 2005 - xxviii, 1396p.
Includes bibliographical references (p. 1374-1384) and index
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: Single Factor Studies ; 15. Introduction to the Design of Experiments -- 16. Analysis of Single-Factor Studies -- 17. Analysis of Factor Level Effects in Single Factor Studies -- 18. ANOVA Diagnostics and Remedial Measures
Part 5: Two -Factor Studies and Blocking ; 19. Two -Factor Studies- Equal Sample Sizes -- 20. Two -Factor Studies-One Case per Cell -- 21. Randomized Complete Block Designs and the Analysis of Covariance -- 22. Two -Factor Studies-Unequal Sample Sizes and Unequal Treatment Importance
Part 6: Multifactor Studies -- 23. Multifactor Studies -- 24. Random and Mixed-Effects Models -- 25. Nested Designs, Subsampling, and Partially Nested Designs -- 26. Repeated Measures and Related Designs -- 27. Latin Square, Balanced Incomplete Block, and Related Designs -- 28. Exploratory Experiments-Two-Level Factorial and Fractional Factorial Designs -- 29. Response Surface Experiments
9781259064746
Linear models
Regression analysis
Applied linear statistical models - 5 - New Delhi McGraw Hill Education(India) Pvt. Ltd. 2005 - xxviii, 1396p.
Includes bibliographical references (p. 1374-1384) and index
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: Single Factor Studies ; 15. Introduction to the Design of Experiments -- 16. Analysis of Single-Factor Studies -- 17. Analysis of Factor Level Effects in Single Factor Studies -- 18. ANOVA Diagnostics and Remedial Measures
Part 5: Two -Factor Studies and Blocking ; 19. Two -Factor Studies- Equal Sample Sizes -- 20. Two -Factor Studies-One Case per Cell -- 21. Randomized Complete Block Designs and the Analysis of Covariance -- 22. Two -Factor Studies-Unequal Sample Sizes and Unequal Treatment Importance
Part 6: Multifactor Studies -- 23. Multifactor Studies -- 24. Random and Mixed-Effects Models -- 25. Nested Designs, Subsampling, and Partially Nested Designs -- 26. Repeated Measures and Related Designs -- 27. Latin Square, Balanced Incomplete Block, and Related Designs -- 28. Exploratory Experiments-Two-Level Factorial and Fractional Factorial Designs -- 29. Response Surface Experiments
9781259064746
Linear models
Regression analysis