Plane Answers to Complex Questions The Theory of Linear Models
This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other v...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2020, 2020
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| Edition: | 5th ed. 2020 |
| Series: | Springer Texts in Statistics
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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| 008 | 200401 ||| eng | ||
| 020 | |a 9783030320973 | ||
| 100 | 1 | |a Christensen, Ronald | |
| 245 | 0 | 0 | |a Plane Answers to Complex Questions |h Elektronische Ressource |b The Theory of Linear Models |c by Ronald Christensen |
| 250 | |a 5th ed. 2020 | ||
| 260 | |a Cham |b Springer International Publishing |c 2020, 2020 | ||
| 300 | |a XXII, 529 p. 33 illus |b online resource | ||
| 505 | 0 | |a 1. Introduction -- 2. Estimation -- 3. Testing -- 4. One-Way ANOVA -- 5. Multiple Comparison Techniques -- 6. Regression Analysis -- 7. Multifactor Analysis of Variance -- 8. Experimental Design Models -- 9. Analysis of Covariance -- 10. General Gauss-Markov Models -- 11. Split Plot Models -- 12. Model Diagnostics -- 13. Collinearity and Alternative Estimates -- 14. Variable Selection -- Appendix A - 6 -- References -- Index -- Author Index | |
| 653 | |a Statistical Theory and Methods | ||
| 653 | |a Statistics | ||
| 041 | 0 | 7 | |a eng |2 ISO 639-2 |
| 989 | |b Springer |a Springer eBooks 2005- | ||
| 490 | 0 | |a Springer Texts in Statistics | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-030-32097-3?nosfx=y |x Verlag |3 Volltext |
| 082 | 0 | |a 519.5 | |
| 520 | |a This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: best linear and best linear unbiased prediction, split plot models, balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, diagnostics, collinearity, and variable selection. This new edition includes new sections on alternatives to least squares estimation and the variance-bias tradeoff, expanded discussion of variable selection, new material on characterizing the interaction space in an unbalanced two-way ANOVA, Freedman's critique of the sandwich estimator, and much more | ||