Rigby 2000: Multiplication Pack 2
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Published: 1998
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DOWNLOAD EBOOKRead and Download eBook Full
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Published: 1998
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DOWNLOAD EBOOKAuthor: Joseph K. Blitzstein
Publisher: CRC Press
Published: 2014-07-24
Total Pages: 599
ISBN-13: 1466575573
DOWNLOAD EBOOKDeveloped from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
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Published: 1998
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DOWNLOAD EBOOKAuthor: edited by Sally Green
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DOWNLOAD EBOOKAuthor: Calvin J. Irons
Publisher:
Published: 1992
Total Pages: 95
ISBN-13: 9780731206520
DOWNLOAD EBOOKAuthor: Dora Scales
Publisher:
Published: 1994-01-01
Total Pages: 103
ISBN-13: 9780731218752
DOWNLOAD EBOOKThis activity book for primary students covers a wide range of mathematical skills, including analysing data, measurement and problem solving. A Core Book, Teachers' Resource Package, Calculator Cards and Investigations Cards are also available. Includes an index.
Author: Wayne W. Daniel
Publisher: Wiley
Published: 2018-11-13
Total Pages: 720
ISBN-13: 1119282373
DOWNLOAD EBOOKThe ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understand—and appropriately use—probability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference.
Author: Simon Wood
Publisher: CRC Press
Published: 2006-02-27
Total Pages: 412
ISBN-13: 1584884746
DOWNLOAD EBOOKNow in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.