The basics of S-PLUS written in a clear style at a level suitable for people with little computing or statistical knowledge. Unlike the S-PLUS manuals, this is not comprehensive, but instead introduces the most important ideas of S-PLUS by way of many examples. Each chapter also includes a collection of exercises which are accompanied by fully worked-out solutions and detailed comments. The whole is rounded off with practical hints on how efficient work can be performed in S-PLUS, and is thus well-suited for self-study and as a textbook.

To encourage effective self-study and hands-on experimentation, the author accompanies his presentation of every concept with an example, either using data sets that are distributed with S or easily created from them. It should not be necessary to enter any data to recreate the examples in the book.

R, linear models, random, fixed, data, analysis, fit.

In a clear style the most important ideas of S-PLUS are introduced through the use of many examples. Each chapter includes a collection of exercises, fully worked-out solutions and detailed comments.

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.

The field of financial econometrics has exploded since the early 1990s. This book represents an integration of theory, methods and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It shows the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts.

This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and to practitioners with financial data analysis concerns.

Many statistical modelling and data analysis techniques can be difficult to grasp and apply, and it is often necessary to use computer software to aid the implementation of large data sets and to obtain useful results. S-Plus is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply a number of statistical methods, ranging from simple regression to time series or multivariate analysis. This text offers extensive coverage of many basic and more advanced statistical methods, concentrating on graphical inspection, and features step-by-step instructions to help the non-statistician to understand fully the methodology. * Extensive coverage of basic, intermediate and advanced statistical methods * Uses S-Plus, which is recognised globally as one of the most powerful and flexible statistical software packages * Emphasis is on graphical data inspection, parameter estimation and model criticism * Features hundreds of worked examples to illustrate the techniques described * Accessible to scientists from a large number of disciplines with minimal statistical knowledge * Written by a leading figure in the field, who runs a number of successful international short courses * Accompanied by a Web site featuring worked examples, data sets, exercises and solutions A valuable reference resource for researchers, professionals, lecturers and students from statistics, the life sciences, medicine, engineering, economics and the social sciences.

The Handbook of Software for Engineers and Scientists is a single-volume, ready reference for the practicing engineer and scientist in industry, government, and academia as well as the novice computer user. It provides the most up-to-date information in a variety of areas such as common platforms and operating systems, applications programs, networking, and many other problem-solving tools necessary to effectively use computers on a daily basis. Specific platforms and environments thoroughly discussed include MS-DOS®, Microsoft® WindowsTM, the Macintosh® and its various systems, UNIXTM, DEC VAXTM, IBM® mainframes, OS/2®, WindowsTM NT, and NeXTSTEPTM. Word processing, desktop publishing, spreadsheets, databases, integrated packages, computer presentation systems, groupware, and a number of useful utilities are also covered. Several extensive sections in the book are devoted to mathematical and statistical software. Information is provided on circuits and control simulation programs, finite element tools, and solid modeling tools. Additional coverage is included on data communications and networking. Many appendices at the end of the book provide useful supplemental information, such as ASCII codes, RS-232 parallel port and pinout information, and ANSI escape sequences. This valuable resource handbook brings together a wide variety of topics and offers a wealth of information at the reader's fingertips.

The first comprehensive, object-oriented package for the analysis of spatial data. Providing a whole new set of analysis tools, S+SPATIALSTATS was created specifically for the exploration and modelling of spatially correlated data, and, as such, can be used to analyse data in such areas as environmental, mining, and petroleum engineering, natural resources, geography, epidemiology, demography, and others where data is sampled spatially.