LEADER 04083nam 22007095 450 001 9910647786003321 005 20250610122441.0 010 $a9783031142758$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 $a20230204d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTen Projects in Applied Statistics /$fby Peter McCullagh 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 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 $aStatistics 606 $aBiometry 606 $aSampling (Statistics) 606 $aQuantitative research 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aMethodology of Data Collection and Processing 606 $aData Analysis and Big Data 606 $aEstadística$2thub 606 $aInvestigació quantitativa$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aBiometry. 615 0$aSampling (Statistics) 615 0$aQuantitative research. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aMethodology of Data Collection and Processing. 615 24$aData Analysis and Big Data. 615 7$aEstadística 615 7$aInvestigació quantitativa 676 $a519.5 676 $a519 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