Handbook Of Statistical Distributions With Applications

Author: K. Krishnamoorthy
Publisher: CRC Press
ISBN: 9781420011371
Size: 39.77 MB
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In the area of applied statistics, scientists use statistical distributions to model a wide range of practical problems, from modeling the size grade distribution of onions to modeling global positioning data. To apply these probability models successfully, practitioners and researchers must have a thorough understanding of the theory as well as a familiarity with the practical situations. The Handbook of Statistical Distributions with Applications is the first reference to combine popular probability distribution models, formulas, applications, and software to assist you in computing probabilities, percentiles, moments, and other statistics. Presenting both common and specialized probability distribution models, as well as providing applications with practical examples, this handbook offers comprehensive coverage of plots of probability density functions, methods of computing probability and percentiles, algorithms for random number generation, and inference, including point estimation, hypothesis tests, and sample size determination. The book discusses specialized distributions, some nonparametric distributions, tolerance factors for a multivariate normal distribution, and the distribution of the sample correlation coefficient, among others. Developed by the author, the StatCal software (available for download at www.crcpress.com), along with the text, offers a useful reference for computing various table values. By using the software, you can compute probabilities, parameters, and moments; find exact tests; and obtain exact confidence intervals for distributions, such as binomial, hypergeometric, Poisson, negative binomial, normal, lognormal, inverse Gaussian, and correlation coefficient. In the applied statistics world, the Handbook of Statistical Distributions with Applications is now the reference for examining distribution functions - including univariate, bivariate normal, and multivariate - their definitions, their use in statistical inference, and their algorithms for random number generation.

Handbook Of Statistical Distributions With Applications Second Edition

Author: K. Krishnamoorthy
Publisher: CRC Press
ISBN: 1498741509
Size: 48.74 MB
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Easy-to-Use Reference and Software for Statistical Modeling and Testing Handbook of Statistical Distributions with Applications, Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results, this edition includes both the author’s StatCalc software and R codes to accurately and easily carry out computations. New to the Second Edition Major changes in binomial, Poisson, normal, gamma, Weibull, exponential, logistic, Laplace, and Pareto distributions Updated statistical tests and intervals based on recent publications in statistical journals Enhanced PC calculator StatCalc with electronic help manuals R functions for cases where StatCalc is not applicable, with the codes available online This highly praised handbook integrates popular probability distribution models, formulas, applications, and software to help you compute a variety of statistical intervals. It covers probability and percentiles, algorithms for random number generation, hypothesis tests, confidence intervals, tolerance intervals, prediction intervals, sample size determination, and much more.

Handbook Of Beta Distribution And Its Applications

Author: Arjun K. Gupta
Publisher: CRC Press
ISBN: 9780824753962
Size: 73.31 MB
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A milestone in the published literature on the subject, this first-ever Handbook of Beta Distribution and Its Applications clearly enumerates the properties of beta distributions and related mathematical notions. It summarizes modern applications in a variety of fields, reviews up-and-coming progress from the front lines of statistical research and practice, and demonstrates the applicability of beta distributions in fields such as economics, quality control, soil science, and biomedicine. The book discusses the centrality of beta distributions in Bayesian inference, the beta-binomial model and applications of the beta-binomial distribution, and applications of Dirichlet integrals.

Univariate And Multivariate General Linear Models

Author: Kevin Kim
Publisher: CRC Press
ISBN: 1420011367
Size: 18.89 MB
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Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.

Handbook Of The Normal Distribution Second Edition

Author: Jagdish K. Patel
Publisher: CRC Press
ISBN: 9780824793425
Size: 77.74 MB
Format: PDF, Kindle
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"Traces the historical development of the normal law. Second Edition offers a comprehensive treatment of the bivariate normal distribution--presenting entirely new material on normal integrals, asymptotic normality, the asymptotic properties of order statistics, and point estimation and statistical intervals."

Applied Time Series Analysis

Author: Wayne A. Woodward
Publisher: CRC Press
ISBN: 1439818371
Size: 78.39 MB
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Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor. One of the unique features of Applied Time Series Analysis is the associated software, GW-WINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring S-plus) are available on an associated website for performing computations related to the material in the final four chapters.

Advanced Risk Analysis In Engineering Enterprise Systems

Author: Cesar Ariel Pinto
Publisher: CRC Press
ISBN: 1439826153
Size: 51.74 MB
Format: PDF, Mobi
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Since the emerging discipline of engineering enterprise systems extends traditional systems engineering to develop webs of systems and systems-of-systems, the engineering management and management science communities need new approaches for analyzing and managing risk in engineering enterprise systems. Advanced Risk Analysis in Engineering Enterprise Systems presents innovative methods to address these needs. With a focus on engineering management, the book explains how to represent, model, and measure risk in large-scale, complex systems that are engineered to function in enterprise-wide environments. Along with an analytical framework and computational model, the authors introduce new protocols: the risk co-relationship (RCR) index and the functional dependency network analysis (FDNA) approach. These protocols capture dependency risks and risk co-relationships that may exist in an enterprise. Moving on to extreme and rare event risks, the text discusses how uncertainties in system behavior are intensified in highly networked, globally connected environments. It also describes how the risk of extreme latencies in delivering time-critical data, applications, or services can have catastrophic consequences and explains how to avoid these events. With more and more communication, transportation, and financial systems connected across domains and interfaced with an infinite number of users, information repositories, applications, and services, there has never been a greater need for analyzing risk in engineering enterprise systems. This book gives you advanced methods for tackling risk problems at the enterprise level.