*An Example-Based Approach*

Author: John Maindonald,W. John Braun

Publisher: Cambridge University Press

ISBN: 1139486675

Category: Computers

Page: N.A

View: 7282

*An Example-Based Approach*

Author: John Maindonald,W. John Braun

Publisher: Cambridge University Press

ISBN: 1139486675

Category: Computers

Page: N.A

View: 7282

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

*An Example-based Approach*

Author: John Maindonald,John Braun

Publisher: Cambridge University Press

ISBN: 1139460536

Category: Computers

Page: N.A

View: 4001

Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.

*An Example-based Approach*

Author: John Maindonald,John Braun

Publisher: Cambridge University Press

ISBN: 9780521813365

Category: Computers

Page: 362

View: 8693

Modern statistical software systems provide sophisticated tools for researchers who need to manipulate and display their data. Using such systems requires training both in the software itself and in the statistical methods that it relies on. Concentrating on the freely available R system, this book demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from the authors' experience, both as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate statistics, whether an upper-graduate student, a researcher, or a practising scientist or statistician. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.

Author: Kevin J. Keen

Publisher: CRC Press

ISBN: 0429632215

Category: Mathematics

Page: 590

View: 4938

Praise for the First Edition "The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers’ understanding of the graphical techniques discussed in the book. ... It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display." -Han Lin Shang,?Journal of Applied Statistics Graphics for Statistics and Data Analysis with R, Second Edition, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print. Features Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data Provides downloadable R code and data for figures at www.graphicsforstatistics.com Kevin J. Keen is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional StatisticianTM by the Statistical Society of Canada and the American Statistical Association.

Author: A. C. Davison,D. V. Hinkley

Publisher: Cambridge University Press

ISBN: 9780521574716

Category: Computers

Page: 582

View: 7960

This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems. Special features of the book include: extensive discussion of significance tests and confidence intervals; material on various diagnostic methods; and methods for efficient computation, including improved Monte Carlo simulation. Each chapter includes both practical and theoretical exercises. Included with the book is a disk of purpose-written S-Plus programs for implementing the methods described in the text. Computer algorithms are clearly described, and computer code is included on a 3-inch, 1.4M disk for use with IBM computers and compatible machines. Users must have the S-Plus computer application. Author resource page: http://statwww.epfl.ch/davison/BMA/

Author: Alain Zuur,Elena N. Ieno,Erik Meesters

Publisher: Springer Science & Business Media

ISBN: 9780387938370

Category: Computers

Page: 220

View: 5528

Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R.

*Analysis and Inference beyond Models*

Author: Bertrand S. Clarke,Jennifer L. Clarke

Publisher: Cambridge University Press

ISBN: 1107028280

Category: Business & Economics

Page: 652

View: 4455

A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.

Author: Jim Albert,Maria Rizzo

Publisher: Springer Science & Business Media

ISBN: 1461413656

Category: Mathematics

Page: 359

View: 1519

R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.

*An Analysis for Statisticians and Interdisciplinary Researchers*

Author: Thomas Leonard,John S. J. Hsu

Publisher: Cambridge University Press

ISBN: 9780521004145

Category: Mathematics

Page: 333

View: 8369

This book describes the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students. It is unusual in presenting Bayesian statistics with a practical flavor and an emphasis on mainstream statistics, showing how to infer scientific, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and describe many statistical methods, not readily available elsewhere. A first chapter on Fisherian methods, together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. The other chapters contain important sections relating to many areas of statistics such as the linear model, categorical data analysis, time series and forecasting, mixture models, survival analysis, Bayesian smoothing, and non-linear random effects models. The text includes a large number of practical examples, worked examples, and exercises. It will be essential reading for all statisticians, statistics students, and related interdisciplinary researchers.

Author: Johannes Ledolter

Publisher: John Wiley & Sons

ISBN: 1118572157

Category: Computers

Page: 368

View: 4204

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: • A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools • Illustrations of how to use the outlined concepts in real-world situations • Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials • Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Author: John F. Monahan

Publisher: CRC Press

ISBN: 9781420062045

Category: Mathematics

Page: 304

View: 9715

A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact, finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity, the text first provides examples of the general linear model, including multiple regression models, one-way ANOVA, mixed-effects models, and time series models. It then introduces the basic algebra and geometry of the linear least squares problem, before delving into estimability and the Gauss–Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals, the author analyzes mixed models, such as two-way mixed ANOVA, and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general, unifying approach to the theory, fundamentals, and exact results of linear models.

Author: Donald B. Percival,Andrew T. Walden

Publisher: Cambridge University Press

ISBN: 9780521685085

Category: Computers

Page: 594

View: 9278

This book contains detailed descriptions of the theory and algorithms needed to understand and implement discrete wavelet transforms.

Author: Gerhard Tutz

Publisher: Cambridge University Press

ISBN: 1139499580

Category: Mathematics

Page: N.A

View: 8156

This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

*An Introduction Using R*

Author: Victor Bloomfield

Publisher: Springer Science & Business Media

ISBN: 1441900837

Category: Science

Page: 321

View: 6863

This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.

Author: Guy Nason

Publisher: Springer Science & Business Media

ISBN: 0387759611

Category: Mathematics

Page: 259

View: 6286

This book contains information on how to tackle many important problems using a multiscale statistical approach. It focuses on how to use multiscale methods and discusses methodological and applied considerations.

Author: W. John Braun,Duncan J. Murdoch

Publisher: Cambridge University Press

ISBN: 1316715248

Category: Computers

Page: N.A

View: 816

This new color edition of Braun and Murdoch's bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from the book's website. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.

*Fundamentals of Programming and Statistical Analysis*

Author: Pierre Lafaye de Micheaux,Rémy Drouilhet,Benoit Liquet

Publisher: Springer Science & Business

ISBN: 1461490200

Category: Computers

Page: 628

View: 1278

The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced. This book can serve as a textbook on R for beginners as well as more advanced users, working on Windows, MacOs or Linux OSes. The first part of the book deals with the heart of the R language and its fundamental concepts, including data organization, import and export, various manipulations, documentation, plots, programming and maintenance. The last chapter in this part deals with oriented object programming as well as interfacing R with C/C++ or Fortran, and contains a section on debugging techniques. This is followed by the second part of the book, which provides detailed explanations on how to perform many standard statistical analyses, mainly in the Biostatistics field. Topics from mathematical and statistical settings that are included are matrix operations, integration, optimization, descriptive statistics, simulations, confidence intervals and hypothesis testing, simple and multiple linear regression, and analysis of variance. Each statistical chapter in the second part relies on one or more real biomedical data sets, kindly made available by the Bordeaux School of Public Health (Institut de Santé Publique, d'Épidémiologie et de Développement - ISPED) and described at the beginning of the book. Each chapter ends with an assessment section: memorandum of most important terms, followed by a section of theoretical exercises (to be done on paper), which can be used as questions for a test. Moreover, worksheets enable the reader to check his new abilities in R. Solutions to all exercises and worksheets are included in this book.

*A Step-by-Step Function Guide to Data Analysis*

Author: Richard Cotton

Publisher: "O'Reilly Media, Inc."

ISBN: 1449357180

Category: Computers

Page: 400

View: 1425

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

Author: Michael J. Crawley

Publisher: John Wiley & Sons

ISBN: 1118448960

Category: Mathematics

Page: 1080

View: 9424

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

Author: J. R. Norris

Publisher: Cambridge University Press

ISBN: 1107393477

Category: Mathematics

Page: N.A

View: 3927

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.