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Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from ...

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10.3 Nonparametric Statistics. 10.1 The Wilcoxon Test and Estimator. 10.2 One Sample Methods. 10.3 The Kolmogorov-Smirnov Tests. 11. Linear Models. 11.1 The Principle of Least Squares. 11.2 Linear Models. 11.3 F-Tests for H0. 11.4 Two-Way Analysis of Variance.. 12. Frequency Data. 12.1 Logistic Regression. 12.2 Two-Way Frequency Tables.

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2.4 Linear Model 18. 2.5 Least Squares Method 19. 2.6 Maximum Likelihood Estimator 31. 2.7 Sufficient Statistics 34. 3 Basic Test Theory 41. 3.1 Normal Mean 41. 3.2 Normal Variance 53. 3.3 Confidence Interval 56. 3.4 Test Theory in the Linear Model 58. 3.5 Likelihood Ratio Test and Efficient Score Test 62

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Linear models and estimation by least squares --12. Considerations in designing experiments --13. The analysis of variance --14. Analysis of categorical data --15. Nonparametric statistics --16. Introduction to Bayesian methods. Responsibility: Dennis D. Wackerly, William Mendenhall III, Richard L. Scheaffer. More information: Table of contents

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