Stated Preference Methods Using R

Author: Hideo Aizaki
Publisher: CRC Press
ISBN: 1439890471
Format: PDF, ePub, Docs
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Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people’s preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and best–worst scaling (BWS). Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors’ website, CRAN, and R-Forge, allowing readers to easily reproduce working examples. Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

Nonparametric Statistical Methods Using R

Author: John Kloke
Publisher: CRC Press
ISBN: 1498787274
Format: PDF, ePub, Docs
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A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters cover more advanced material, including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples, it shows readers how to apply these methods using R.

Analyzing Baseball Data with R

Author: Max Marchi
Publisher: CRC Press
ISBN: 1315360594
Format: PDF, ePub
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With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online. This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.

Best Worst Scaling

Author: Jordan J. Louviere
Publisher: Cambridge University Press
ISBN: 1107043158
Format: PDF, Mobi
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First systematic treatment of best-worst scaling, explaining how to implement, analyze, and apply the theory across a range of disciplines.

Computational Statistics Handbook with MATLAB Third Edition

Author: Wendy L. Martinez
Publisher: CRC Press
ISBN: 1466592745
Format: PDF
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A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB®, Third Edition covers today’s most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third Edition This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web Resource The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

Analytical Methods for Risk Management

Author: Paul R. Garvey
Publisher: CRC Press
ISBN: 9781420011395
Format: PDF, Kindle
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A Text on the Foundation Processes, Analytical Principles, and Implementation Practices of Engineering Risk Management Drawing from the author’s many years of hands-on experience in the field, Analytical Methods for Risk Management: A Systems Engineering Perspectivepresents the foundation processes and analytical practices for identifying, analyzing, measuring, and managing risk in traditional systems, systems-of-systems, and enterprise systems. Balances Risk and Decision Theory with Case Studies and Exercises After an introduction to engineering risk management, the book covers the fundamental axioms and properties of probability as well as key aspects of decision analysis, such as preference theory and risk/utility functions. It concludes with a series of essays on major analytical topics, including how to identify, write, and represent risks; prioritize risks in terms of their potential impacts on a systems project; and monitor progress when mitigating a risk’s potential adverse effects. The author also examines technical performance measures and how they can combine into an index to track an engineering system’s overall performance risk. In addition, he discusses risk management in the context of engineering complex, large-scale enterprise systems. Applies Various Methods to Risk Engineering and Analysis Problems This practical guide enables an understanding of which processes and analytical techniques are valid and how they are best applied to specific systems engineering environments. After reading this book, you will be on your way to managing risk on both traditional and advanced engineering systems.

bookdown

Author: Yihui Xie
Publisher: CRC Press
ISBN: 1351792601
Format: PDF, Mobi
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bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.

Reproducible Research with R and R Studio Second Edition

Author: Christopher Gandrud
Publisher: CRC Press
ISBN: 1498715389
Format: PDF, Mobi
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All the Tools for Gathering and Analyzing Data and Presenting Results Reproducible Research with R and RStudio, Second Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. New to the Second Edition The rmarkdown package that allows you to create reproducible research documents in PDF, HTML, and Microsoft Word formats using the simple and intuitive Markdown syntax Improvements to RStudio’s interface and capabilities, such as its new tools for handling R Markdown documents Expanded knitr R code chunk capabilities The kable function in the knitr package and the texreg package for dynamically creating tables to present your data and statistical results An improved discussion of file organization, enabling you to take full advantage of relative file paths so that your documents are more easily reproducible across computers and systems The dplyr, magrittr, and tidyr packages for fast data manipulation Numerous modifications to R syntax in user-created packages Changes to GitHub’s and Dropbox’s interfaces Create Dynamic and Highly Reproducible Research This updated book provides all the tools to combine your research with the presentation of your findings. It saves you time searching for information so that you can spend more time actually addressing your research questions. Supplementary files used for the examples and a reproducible research project are available on the author’s website.

Using R for Introductory Statistics Second Edition

Author: John Verzani
Publisher: CRC Press
ISBN: 1466590734
Format: PDF, ePub
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The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

The Analysis of Time Series

Author: Chris Chatfield
Publisher: CRC Press
ISBN: 9780203491683
Format: PDF, ePub, Mobi
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Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc. Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.