01542oam 2200457I 450 991071354590332120200522143134.0(CKB)5470000002502205(OCoLC)1127388900(EXLCZ)99547000000250220520191113d1926 ua 0engurbn|||||||||txtrdacontentcrdamediacrrdacarrierThe resistance to the steady motion of small spheres in fluids /by R.A. Castleman[Washington, D.C.] :National Advisory Committee for Aeronautics,1926.1 online resource (12 pages, 5 unnumbered pages) illustrationsTechnical notes / National Advisory Committee for Aeronautics ;No. 231"February 1926."No Federal Depository Library Program (FDLP) item number.Includes bibliographical references (page 12).Fluid dynamicsSphereAerodynamicsFluid dynamicsfastFluid dynamics.SphereAerodynamics.Fluid dynamics.Castleman R. A.1388222United States.National Advisory Committee for Aeronautics,TRAALTRAALTRAALOCLCOOCLCFGPOBOOK9910713545903321The resistance to the steady motion of small spheres in fluids3438635UNINA03798nam 22006135 450 991025407460332120250409133512.0981-10-0889-210.1007/978-981-10-0889-4(CKB)3710000000717756(DE-He213)978-981-10-0889-4(MiAaPQ)EBC6310530(MiAaPQ)EBC5555637(Au-PeEL)EBL5555637(OCoLC)953613210(PPN)194075672(EXLCZ)99371000000071775620160520d2016 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierExamples in Parametric Inference with R /by Ulhas Jayram Dixit1st ed. 2016.Singapore :Springer Nature Singapore :Imprint: Springer,2016.1 online resource (LVIII, 423 p. 26 illus.)981-10-0888-4 Includes bibliographical references.Prerequisite -- Chapter 1. Sufficiency and Completeness -- Chapter 2. Unbiased Estimation -- Chapter 3. Moment and Maximum Likelihood Estimators -- Chapter 4. Bound for the Variance -- Chapter 5. Consistent Estimator -- Chapter 6. Bayes Estimator -- Chapter 7. Most Powerful Test -- Chapter 8. Unbiased and Other Tests -- Bibliography.This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.StatisticsMathematical statisticsData processingComputer scienceMathematicsMathematical statisticsStatistical Theory and MethodsStatistics and ComputingProbability and Statistics in Computer ScienceStatistics.Mathematical statisticsData processing.Computer scienceMathematics.Mathematical statistics.Statistical Theory and Methods.Statistics and Computing.Probability and Statistics in Computer Science.519.5Dixit Ulhas Jayramauthttp://id.loc.gov/vocabulary/relators/aut755913MiAaPQMiAaPQMiAaPQBOOK9910254074603321Examples in parametric inference with R1523316UNINA