R for Statistics

Author: Pierre-Andre Cornillon
Publisher: CRC Press
ISBN: 9781439881460
Release Date: 2012-03-21
Genre: Mathematics

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. Focusing on the R software, the first section covers: Basic elements of the R software and data processing Clear, concise visualization of results, using simple and complex graphs Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: Regression methods Analyses of variance and covariance Classification methods Exploratory multivariate analysis Clustering methods Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist. Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat

Methods for Statistical Data Analysis of Multivariate Observations

Author: R. Gnanadesikan
Publisher: John Wiley & Sons
ISBN: 0471161195
Release Date: 1997-02-04
Genre: Mathematics

A practical guide for multivariate statistical techniques—now updated and revised In recent years, innovations in computer technology andstatistical methodologies have dramatically altered the landscapeof multivariate data analysis. This new edition of Methods forStatistical Data Analysis of Multivariate Observations explorescurrent multivariate concepts and techniques while retaining thesame practical focus of its predecessor. It integrates methods anddata-based interpretations relevant to multivariate analysis in away that addresses real-world problems arising in many areas ofinterest. Greatly revised and updated, this Second Edition provideshelpful examples, graphical orientation, numerous illustrations,and an appendix detailing statistical software, including the S (orSplus) and SAS systems. It also offers An expanded chapter on cluster analysis that covers advances inpattern recognition New sections on inputs to clustering algorithms and aids forinterpreting the results of cluster analysis An exploration of some new techniques of summarization andexposure New graphical methods for assessing the separations among theeigenvalues of a correlation matrix and for comparing sets ofeigenvectors Knowledge gained from advances in robust estimation anddistributional models that are slightly broader than themultivariate normal This Second Edition is invaluable for graduate students, appliedstatisticians, engineers, and scientists wishing to usemultivariate techniques in a variety of disciplines.

Statistical Intervals

Author: Gerald J. Hahn
Publisher: John Wiley & Sons
ISBN: 9780470317440
Release Date: 2011-09-28
Genre: Mathematics

Presents a detailed exposition of statistical intervals and emphasizes applications in industry. The discussion differentiates at an elementary level among different kinds of statistical intervals and gives instruction with numerous examples and simple math on how to construct such intervals from sample data. This includes confidence intervals to contain a population percentile, confidence intervals on probability of meeting specified threshold value, and prediction intervals to include observation in a future sample. Also has an appendix containing computer subroutines for nonparametric statistical intervals.

Fundamentals of Exploratory Analysis of Variance

Author: David C. Hoaglin
Publisher: John Wiley & Sons
ISBN: 0471527351
Release Date: 1991-09-05
Genre: Mathematics

The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions.

A User s Guide to Principal Components

Author: J. Edward Jackson
Publisher: John Wiley & Sons
ISBN: 9780471725329
Release Date: 2005-01-21
Genre: Mathematics

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of A User’s Guide to Principal Components "The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA." –Technometrics "I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results." –Mathematical Geology

Elements of Applied Stochastic Processes

Author: U. Narayan Bhat
Publisher: John Wiley & Sons Incorporated
ISBN: MINN:31951000009757B
Release Date: 1984-10-25
Genre: Mathematics

Fundamentals of Queueing Theory, 2nd Edition Donald Gross and Carl M. Harris A graduate text and reference treating queueing theory from the development of standard models to applications. The emphasis is on real analysis of queueing systems, applications, and problem solving. It has been brought up-to-date by modernizing older treatments. 1985 (0 471-89067-7) 475 pp. Multivariate Descriptive Analysis Correspondence Analysis and Related Techniques for Large Matrices Ludovic Lebart, Alain Morineau and Kenneth M. Warwick Presents a set of statistical methods for exploratory analysis of large date sets and categorical data. This unique approach uses graphical aspects of multidimensional scaling techniques within the context of exploratory data analysis. 1984 (0 471-86743-8) 231 pp. Introduction to Linear Regression Analysis Douglas C. Montgomery and Elizabeth A. Peck A definitive introduction to linear regression analysis covering basic topics as well as recent approaches in the field. It blends theory and application in a way that enables readers to apply regression methodology in a variety of practical settings. Many detailed examples drawn directly from various fields of engineering, physical science, and the management sciences provide clear guidance to the use of the techniques. The interface with widely available computer programs for regression analysis is illustrated throughout with numerous actual computer printouts. 1982 (0 471-05850-5) 504 pp.

Methods of Multivariate Analysis

Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 9781118391679
Release Date: 2012-08-15
Genre: Mathematics

Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." —IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor Analysis Classification Trees Dynamic Graphics Transformations to Normality Prediction for Multivariate Multiple Regression Kronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.

Regression analysis by example

Author: Samprit Chatterjee
Publisher: Wiley-Interscience
ISBN: 0471884790
Release Date: 1991
Genre: Mathematics

A variety of ideas and methods of regression analysis are explored with the aid of realistic examples that highlight the analysis of data and include irregularities similar to those encountered in practice. Recent advances in regression diagnostics are covered with emphasis on plots such as component plus residual, added variable, sequence, along with index plots for leverage and function. The authors utilize standard and some not so standard summary statistics on the basis of their intuitive appeal to demonstrate concepts. The majority of analyses described are available in software packages on the market today.

Statistical Inference for Branching Processes

Author: Peter Guttorp
Publisher: Wiley-Interscience
ISBN: UOM:39015021518686
Release Date: 1991-08-19
Genre: Mathematics

An examination of the difficulties that statistical theory and, in particular, estimation theory can encounter within the area of dependent data. This is achieved through the study of the theory of branching processes starting with the demographic question: what is the probability that a family name becomes extinct? Contains observations on the generation sizes of the Bienaym?-Galton-Watson (BGW) process. Various parameters are estimated and branching process theory is contrasted to a Bayesian approach. Illustrations of branching process theory applications are shown for particular problems.

Forecasting with Dynamic Regression Models

Author: Alan Pankratz
Publisher: Wiley-Interscience
ISBN: UOM:39015024799069
Release Date: 1991-10-24
Genre: Mathematics

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

Statistics for Imaging Optics and Photonics

Author: Peter Bajorski
Publisher: John Wiley & Sons
ISBN: 9781118121948
Release Date: 2011-09-26
Genre: Mathematics

A vivid, hands-on discussion of the statistical methods in imaging, optics, and photonics applications In the field of imaging science, there is a growing need for students and practitioners to be equipped with the necessary knowledge and tools to carry out quantitative analysis of data. Providing a self-contained approach that is not too heavily statistical in nature, Statistics for Imaging, Optics, and Photonics presents necessary analytical techniques in the context of real examples from various areas within the field, including remote sensing, color science, printing, and astronomy. Bridging the gap between imaging, optics, photonics, and statistical data analysis, the author uniquely concentrates on statistical inference, providing a wide range of relevant methods. Brief introductions to key probabilistic terms are provided at the beginning of the book in order to present the notation used, followed by discussions on multivariate techniques such as: Linear regression models, vector and matrix algebra, and random vectors and matrices Multivariate statistical inference, including inferences about both mean vectors and covariance matrices Principal components analysis Canonical correlation analysis Discrimination and classification analysis for two or more populations and spatial smoothing Cluster analysis, including similarity and dissimilarity measures and hierarchical and nonhierarchical clustering methods Intuitive and geometric understanding of concepts is emphasized, and all examples are relatively simple and include background explanations. Computational results and graphs are presented using the freely available R software, and can be replicated by using a variety of software packages. Throughout the book, problem sets and solutions contain partial numerical results, allowing readers to confirm the accuracy of their approach; and a related website features additional resources including the book's datasets and figures. Statistics for Imaging, Optics, and Photonics is an excellent book for courses on multivariate statistics for imaging science, optics, and photonics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for professionals working in imaging, optics, and photonics who carry out data analyses in their everyday work.


ISBN: STANFORD:36105117254685
Release Date: 1984
Genre: American literature

Books in Print

ISBN: STANFORD:36105015915866
Release Date: 1989
Genre: American literature