LEADER 03454nam 2200505 450 001 9910647786003321 005 20230513070803.0 010 $a9783031142758$b(electronic bk.) 010 $z9783031142741 024 7 $a10.1007/978-3-031-14275-8 035 $a(MiAaPQ)EBC7191448 035 $a(Au-PeEL)EBL7191448 035 $a(CKB)26089876900041 035 $a(DE-He213)978-3-031-14275-8 035 $a(PPN)268204969 035 $a(EXLCZ)9926089876900041 100 $a20230513d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTen projects in applied statistics /$fPeter McCullagh 205 $a1st ed. 2022. 210 1$aCham, Switzerland :$cSpringer International Publishing,$d[2023] 210 4$dİ2023 215 $a1 online resource (415 pages) 225 1 $aSpringer Series in Statistics,$x2197-568X 311 08$aPrint version: McCullagh, Peter Ten Projects in Applied Statistics Cham : Springer International Publishing AG,c2023 9783031142741 320 $aIncludes bibliographical references and index. 327 $a1. Rat Surgery -- 2. Chain Saws -- 3. Fruit Flies -- 4. Growth Curves -- 5. Louse Evolution -- 6. Time Series I -- 7. Time Series II -- 8. Out of Africa -- 9. Environmental Projects -- 10. Fulmar Fitness -- 11. Basic Concepts -- 12. Principles -- 13. Initial Values -- 14. Probability Distributions -- 15. Gaussian Distributions -- 16. Space-Time Processes -- 17. Likelihood -- 18. Residual Likelihood -- 19. Response Transformation -- 20. Presentations and Reports -- 21. Q & A. . 330 $aThe first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring theme, so variance-components models feature prominently. Statistical pitfalls are illustrated via examples taken from the recent scientific literature. Chapter 11 sets the scene, not just for the second half of the book, but for the book as a whole. It begins by defining fundamental concepts such as baseline, observational unit, experimental unit, covariates and relationships, randomization, treatment assignment, and the role that these play in model formulation. Compatibility of the model with the randomization scheme is crucial. The effect of treatment is invariably modelled as a group action on probability distributions. Technical matters connected with space-time covariance functions, residual likelihood, likelihood ratios, and transformations are discussed in later chapters. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aMathematical statistics 606 $aMathematical statistics$xAsymptotic theory 615 0$aMathematical statistics. 615 0$aMathematical statistics$xAsymptotic theory. 676 $a519.5 700 $aMcCullagh$b P. J$g(Peter John),$0479401 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910647786003321 996 $aTen projects in applied statistics$93362300 997 $aUNINA