statistical computing second edition di rizzo maria (27 risultati)

hardcover. Condizione: Good. 2nd Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

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hardcover. Condizione: New. 2nd Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Condizione: New.

Condizione: New. In.

Hardcover. Condizione: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardback. Condizione: New. 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.

Hardcover. Condizione: New. ISBN:9781466553323.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Hardback. Condizione: New. 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.

Hardcover. Condizione: New. New. book.

Condizione: New. 2019. 2nd Edition. Hardcover. . . . . .

Condizione: New. 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 .

Hardback. Condizione: New. 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.

Condizione: New. 2019. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Condizione: New. 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.

Hardcover. Condizione: new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. FeaturesProvides 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 ggplot2Accompanied 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. 492 pp. Englisch.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. FeaturesProvides 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 ggplot2Accompanied 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.

Buch. Condizione: Neu. Statistical Computing with R, Second Edition | Maria L. Rizzo | Buch | Einband - fest (Hardcover) | Englisch | 2019 | Chapman and Hall/CRC | EAN 9781466553323 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.