Statistical Literacy: A Beginner`s Guide, Rhys Jones

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh
Название: Introduction to statistical learning
ISBN: 1071614177 ISBN-13(EAN): 9781071614174
Издательство: Springer
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Цена: 55890.00 T
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Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.

An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 33790.00 T
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Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Автор: Durbin, James; Koopman, Siem Jan
Название: Time Series Analysis by State Space Methods
ISBN: 019964117X ISBN-13(EAN): 9780199641178
Издательство: Oxford Academ
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Цена: 126720.00 T
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Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.

Автор: Dirk P. Kroese; Joshua C.C. Chan
Название: Statistical Modeling and Computation
ISBN: 149395332X ISBN-13(EAN): 9781493953325
Издательство: Springer
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Цена: 93150.00 T
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Описание: This book provides an introduction to modern statistics. It also offers an integrated treatment of mathematical statistics and statistical computation, emphasizing statistical modeling, computational techniques, and applications.

Автор: McElreath, Richard
Название: Statistical Rethinking
ISBN: 036713991X ISBN-13(EAN): 9780367139919
Издательство: Taylor&Francis
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Цена: 83690.00 T
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Описание: Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach.

Автор: Christian Robert; George Casella
Название: Monte Carlo Statistical Methods
ISBN: 1441919392 ISBN-13(EAN): 9781441919397
Издательство: Springer
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Цена: 111790.00 T
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Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Автор: Kroese Dirk P
Название: Statistical Modeling and Computation
ISBN: 1461487749 ISBN-13(EAN): 9781461487746
Издательство: Springer
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Цена: 234490.00 T
Наличие на складе: Нет в наличии.
Описание: This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models.

Автор: Rhys Jones
Название: Statistical Literacy A Beginner`s Guide
ISBN: 1529754801 ISBN-13(EAN): 9781529754803
Издательство: Sage Publications
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Цена: 101370.00 T
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Описание:

In an increasingly data-centric world, we all need to know how to read and interpret statistics. But where do we begin?

This book breaks statistical terms and concepts down in a clear, straightforward way. From understanding what data are telling you to exploring the value of good storytelling with numbers, it equips you with the information and skills you need to become statistically literate.

It also:

• Dispels misconceptions about the nature of statistics to help you avoid common traps.
• Helps you put your learning into practice with over 60 Tasks and Develop Your Skills activities.
• Draws on real-world research to demonstrate the messiness of data – and show you a path through it.

Approachable and down to earth, this guide is aimed at undergraduates across the social sciences, psychology, business and beyond who want to engage confidently with quantitative methods or statistics. It forms a reassuring aid for anyone looking to understand the foundations of statistics before their course advances, or as a refresher on key content.

Автор: Rizzo
Название: Statistical Computing With R 2E
ISBN: 1466553324 ISBN-13(EAN): 9781466553323
Издательство: Taylor&Francis
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Цена: 74510.00 T
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Описание: Praise for the First Edition:". . .

the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." - Tzvetan Semerdjiev, Zentralblatt MathComputational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach.

The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory.

Explores key topics in statistical computing including Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. Includes new sections, exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio, the tidyverse, knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics.

Suitable for an introductory course in computational statistics or for self-study, Statistical Computing with R, Second Edition provides a balanced, accessible introduction to computational statistics and statistical computing. About the AuthorMaria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green, Ohio, where she teaches statistics, actuarial science, computational statistics, statistical programming and data science. Prior to joining the faculty at BGSU in 2006, she was Assistant Professor in the Department of Mathematics at Ohio University in Athens, Ohio.

Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 97150.00 T
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.

Автор: Theodosia Prodromou
Название: Data visualization and statistical literacy for open and big data /
ISBN: 1522525122 ISBN-13(EAN): 9781522525127
Издательство: Turpin
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Цена: 202050.00 T
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Описание: Data visualization has emerged as a serious scholarly topic, and a wide range of tools have recently been developed at an accelerated pace to aid in this research area. Examining different ways of analyzing big data can result in increased efficiency for many corporations and organizations.Data Visualization and Statistical Literacy for Open and Big Data highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this publication is geared toward teachers, academicians, students, engineers, professionals, and researchers that are interested in expanding their knowledge of data examination and analysis.