LEADER 03382nam 22004815 450 001 996418272503316 005 20200705192841.0 010 $a3-030-32097-9 024 7 $a10.1007/978-3-030-32097-3 035 $a(CKB)4100000010770854 035 $a(DE-He213)978-3-030-32097-3 035 $a(MiAaPQ)EBC6133774 035 $a(PPN)243225881 035 $a(EXLCZ)994100000010770854 100 $a20200313d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPlane Answers to Complex Questions$b[electronic resource] $eThe Theory of Linear Models /$fby Ronald Christensen 205 $a5th ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXII, 529 p. 33 illus.) 225 1 $aSpringer Texts in Statistics,$x1431-875X 311 $a3-030-32096-0 320 $aIncludes bibliographical references and indexes. 327 $a1. 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. 330 $aThis textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: best linear and best linear unbiased prediction, split plot models, balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, diagnostics, collinearity, and variable selection. This new edition includes new sections on alternatives to least squares estimation and the variance-bias tradeoff, expanded discussion of variable selection, new material on characterizing the interaction space in an unbalanced two-way ANOVA, Freedman's critique of the sandwich estimator, and much more. 410 0$aSpringer Texts in Statistics,$x1431-875X 606 $aStatistics  606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aStatistics . 615 14$aStatistical Theory and Methods. 676 $a519.535 700 $aChristensen$b Ronald$4aut$4http://id.loc.gov/vocabulary/relators/aut$066381 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418272503316 996 $aPlane answers to complex questions$9615781 997 $aUNISA