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"This book contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with how computational statistics became the backbone of modern data science (Ch.1) : an overview of the field of computational statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like bioinformatics, medical imaging, finance, econometrics and network intrusion detection highlight the usefulness of computational statistics in real-world applications."

"Summary:Engaging and accessible, this comprehensive introduction to statistics integrates Stata commands with numerous examples based on real data."

"Illustrates by example and step-by-step instruction how to use Minitab 16 to explore and analyze data. This book introduces readers to Minitab software while understanding that they may also be learning statistics at the same time. It also illustrates how statistical concepts apply to data analysis through examples."

"This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics."

"Mathematical statistics with applications, second edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces many modern statistical computational and simulation concepts that are not covered in other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data."

"This is book to show the capabilities of microsoft excel to teach educational and psychological statistics effectively. It is a step-by-step exercise-driven guide who need to master excel to solve practical problems using statistics. Excel, a widely available computer program for students and practitioners, is also an effective teaching and learning tool for quantitative analyses in courses in education and psychology. Its powerful computational ability and graphical functions make learning statistics. Each chapter explains statistical formulas and directs the reader to use excel commands to solve specific, easy-to-understand statistics problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned. "