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Published: Wednesday, April 5, 2017 - 12:00 (Wiley: Hoboken, NJ) -- Illuminating Statistical Analysis Using Scenarios and Simulations, by Jeffrey E. Kottemann (Wiley, 2017), presents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. It features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario, each with their own evidence, while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods, and the author does not simply explain the linkages, but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition, induction and deduction are repeatedly interwoven, which fosters a natural “need to know basis” for ordering the topic coverage. Examining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics, the role of chance, and to (re)invent corresponding principles of statistical inference. In addition, the simulation results foreshadow the various mathematical formulas that underlie statistical analysis. In addition, this book: Illuminating Statistical Analysis Using Scenarios and Simulations is an ideal textbook for courses, seminars, and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers, scientists, managers, technicians, and others with a keen interest in statistical analysis. Author Jeffrey E. Kottemann is a professor in the Perdue School at Salisbury University. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in systems and quantitative methods from the University of Arizona. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. So please consider turning off your ad blocker for our site. Thanks, Wiley is a global research and learning company. Through the research segment, the company provides scientific, technical, medical, and scholarly journals, as well as related content and services, for academic, corporate, and government libraries, learned societies, and individual researchers and other professionals. The publishing segment provides scientific, professional development, and education books and related content, as well as test preparation services and course workflow tools, to libraries, corporations, students, professionals, and researchers. In solutions, Wiley provides online program management services for higher education institutions, and learning, development, and assessment services for businesses and professionals. Illuminating Statistical Analysis Using Scenarios and Simulations
This approach helps readers develop key intuitions and deep understandings of statistical analysis
• Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis
• Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions, the effects of sample size and variance on uncertainty, analysis of proportion, mean and rank differences, covariance, correlation, and regression
• Introduces advanced topics such as Bayesian statistics, data mining, model cross-validation, robust regression, and resampling
• Contains numerous example problems in each chapter with detailed solutions, as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft Office Excel
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