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The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets, .... The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statisticians working at the forefront of statistical analysis.

Mathematical statistics -- Data processing -- Congresses. --- Mathematical statistics -- Data processing. --- Mathematical statistics. --- Mathematical statistics --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Data processing --- Database design --- Statistical methods. --- Data base design --- Mathematics. --- Data mining. --- Computer software. --- Statistics. --- Mathematical Software. --- Statistics and Computing/Statistics Programs. --- Statistical Theory and Methods. --- Data Mining and Knowledge Discovery. --- System design --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Software, Computer --- Computer systems --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics and Computing. --- Data processing.

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This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.

Social sciences --- Mathematical statistics --- Statistical methods --- Data processing --- dataverwerking --- regressie-analyse --- softwarepakketten --- wiskundige statistiek --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Statistical methods&delete& --- Enterprise guide. --- SAS (Computer file) --- Statistical analysis system --- SAS system --- Data processing. --- Mathematical Sciences --- Probability --- Social sciences - Statistical methods - Data processing --- Mathematical statistics - Data processing

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This book is an integrated treatment of applied statistical methods, presented at an intermediate level, and the SAS programming language. It serves as an advanced introduction to SAS as well as how to use SAS for the analysis of data arising from many different experimental and observational studies. While there are many introductory texts on SAS programming, statistical methods texts that solely make use of SAS as the software of choice for the analysis of data are rare. While this is understandable from a marketability point of view, clearly such texts will serve the need of many thousands of students and professionals who desire to learn how to use SAS beyond the basic introduction they usually receive from taking an introductory statistics course. More recently, several authors in statistical methodology have begun to incorporate SAS in their texts but these books are limited to more specialized subjects. Many of the standard topics covered in statistical methods texts supplemented by advanced material more suited for a second course in applied statistics are included, so that specific aspects of SAS procedures can be illustrated. Brief but instructive reviews of the statistical methodologies used are provided, and then illustrated with analysis of data sets used in well-known statistical methods texts. Particular attention is devoted to discussions of models used in each analysis because the authors believe that it is important for users to have not only an understanding of how these models are represented in SAS but also because it helps in the interpretation of the SAS output produced. Mervyn G. Marasinghe is Associate Professor of Statistics at Iowa State University where he teaches several courses in statistics and statistical computing and a course in data analysis using SAS software. A former Associate Editor of the Journal Computational and Graphical Statistics, he has used SAS software for more than 30 years. William J. Kennedy is Professor Emeritus of Statistics at Iowa State University. A Fellow of the American Statistical Association and former Editor of The American Statistician and Journal of Computational and Graphical Statistics, he is coauthor of the book entitled Statistical Computing.

Statistics. --- Statistics and Computing/Statistics Programs. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Electronic books. -- local. --- Mathematical statistics -- Data processing. --- Mathematical statistics -- Software. --- SAS (Computer file). --- SAS (Computer program language). --- Mathematical statistics --- SAS (Computer program language) --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Data processing --- Data processing. --- SAS (Computer file) --- Statistical Analysis System (Computer program language) --- Statistical inference --- Statistics, Mathematical --- Statistical methods --- Statistical analysis system --- SAS system --- Mathematics. --- Computer software. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Mathematical Software. --- Programming languages (Electronic computers) --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Software, Computer --- Computer systems --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions. Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley.

Information systems --- Mathematical statistics --- Statistics --- R (Computer program language) --- Statistique mathématique --- Statistique --- R (Langage de programmation) --- Data processing --- Informatique --- Mathematical statistics --Data processing. --- R (Computer program language). --- Statistics --Data processing. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Methoden en technieken --- Data processing. --- statistiek --- -Mathematical statistics --- -dataverwerking --- wiskundige statistiek --- 519.5 --- statistiek. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- GNU-S (Computer program language) --- Distribution (Probability theory. --- Computer science --- Computer software. --- Mathematical statistics. --- Probability Theory and Stochastic Processes. --- Computational Mathematics and Numerical Analysis. --- Mathematical Software. --- Statistics and Computing/Statistics Programs. --- Mathematics. --- Domain-specific programming languages --- Software, Computer --- Computer systems --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Distribution functions --- Frequency distribution --- Characteristic functions --- dataverwerking --- Probabilities. --- Computer mathematics. --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk

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Enrique Castillo is a leading figure in several mathematical, statistical, and engineering fields, having contributed seminal work in such areas as statistical modeling, extreme value analysis, multivariate distribution theory, Bayesian networks, neural networks, functional equations, artificial intelligence, linear algebra, optimization methods, numerical methods, reliability engineering, as well as sensitivity analysis and its applications. Organized to honor Castillo's significant contributions, this volume is an outgrowth of the International Conference on Mathematical and Statistical Modeling and covers recent advances in the field. Also presented are applications to safety, reliability and life-testing, financial modeling, quality control, general inference, as well as neural networks and computational techniques. The book is divided into nine major sections: * Distribution Theory and Applications * Probability and Statistics * Order Statistics and Analysis * Engineering Modeling * Extreme Value Theory * Business and Economics Applications * Statistical Methods * Applied Mathematics * Discrete Distributions This comprehensive reference work will appeal to a diverse audience from the statistical, applied mathematics, engineering, and economics communities. Practitioners, researchers, and graduate students in mathematical and statistical modeling, optimization, and computing will benefit from this work.

Mathematical statistics --- Economics --- Statistics --- Computer Science --- Mathematical optimization --- Data processing --- Congresses --- Distribution (Probability theory. --- Mathematical statistics. --- Statistics. --- Computer science. --- Mathematical Modeling and Industrial Mathematics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Computational Science and Engineering. --- Informatics --- Science --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Mathematical models. --- Probabilities. --- Statistics . --- Computer mathematics. --- Computer mathematics --- Electronic data processing --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Models, Mathematical --- Simulation methods --- Economics - Statistics --- Mathematical statistics - Data processing - Congresses --- Probability Theory. --- Statistics in Business, Management, Economics, Finance, Insurance. --- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Data processing. --- Castillo, Enrique,