Exploratory Data Mining and Data Cleaning


Author: Tamraparni Dasu,Theodore Johnson
Publisher: John Wiley & Sons
ISBN: 0471458643
Category: Mathematics
Page: 203
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Written for practitioners of data mining, data cleaning anddatabase management. Presents a technical treatment of data quality includingprocess, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge. Addresses methods of detecting, quantifying and correcting dataquality issues that can have a significant impact on findings anddecisions, using commercially available tools as well as newalgorithmic approaches. Uses case studies to illustrate applications in real lifescenarios. Highlights new approaches and methodologies, such as theDataSphere space partitioning and summary based analysistechniques. Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.

Statistik-Workshop für Programmierer


Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Category: Computers
Page: 160
View: 9861

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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Healthcare Knowledge Management Primer


Author: Nilmini Wickramasinghe,Rajeev K. Bali,Brian Lehaney,Jonathan Schaffer,M. Chris Gibbons
Publisher: Routledge
ISBN: 1135847436
Category: Business & Economics
Page: 224
View: 975

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Quality care of patients requires evaluating large amounts of data at the right time and place and in the correct context. With the advent of electronic health records, data warehouses now provide information at the point of care and facilitate a continuous learning environment in which lessons learned can provide updates to clinical, administrative, and financial processes. Given the advancement of the information tools and techniques of today’s knowledge economy, utilizing these resources are imperative for effective healthcare. Thus, the principles of Knowledge Management (KM) are now essential for quality healthcare management. The Healthcare Knowledge Management Primer explores and explains essential KM principles in healthcare settings in an introductory and easy to understand fashion. This concise book is ideal for both students and professionals who need to learn more about key aspects of the KM field as it pertains to effecting superior healthcare delivery. It provides readers with an understanding of approaches to KM by examining the purpose and nature of its key components and demystifies the KM field by explaining in an accessible manner the key concepts of KM tools, strategies and techniques, and their benefits to contemporary healthcare organizations.

Data Mining in Bioinformatics


Author: Jason T. L. Wang,Mohammed J. Zaki,Hannu Toivonen,Dennis Shasha
Publisher: Springer Science & Business Media
ISBN: 1846280591
Category: Computers
Page: 340
View: 5741

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Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Data Preparation for Data Mining


Author: Dorian Pyle
Publisher: Morgan Kaufmann
ISBN: 9781558605299
Category: Computers
Page: 540
View: 4282

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A guide to the importance of well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance, and provides examples of how to apply a variety of techniques in order to solve real world business problems.

Statistical Methods for Survival Data Analysis


Author: Elisa T. Lee,John Wenyu Wang
Publisher: John Wiley & Sons
ISBN: 1118593057
Category: Mathematics
Page: 512
View: 8997

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Praise for the Third Edition “. . . an easy-to read introduction to survival analysiswhich covers the major concepts and techniques of thesubject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments,Statistical Methods for Survival Data Analysis, FourthEdition continues to deliver a comprehensive introduction tothe most commonly-used methods for analyzing survival data.Authored by a uniquely well-qualified author team, the FourthEdition is a critically acclaimed guide to statistical methods withapplications in clinical trials, epidemiology, areas of business,and the social sciences. The book features many real-world examplesto illustrate applications within these various fields, althoughspecial consideration is given to the study of survival data inbiomedical sciences. Emphasizing the latest research and providing the mostup-to-date information regarding software applications in thefield, Statistical Methods for Survival Data Analysis, FourthEdition also includes: Marginal and random effect models for analyzing correlatedcensored or uncensored data Multiple types of two-sample and K-sample comparisonanalysis Updated treatment of parametric methods for regression modelfitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of thepresented material Statistical Methods for Survival Data Analysis is anideal text for upper-undergraduate and graduate-level courses onsurvival data analysis. The book is also an excellent resource forbiomedical investigators, statisticians, and epidemiologists, aswell as researchers in every field in which the analysis ofsurvival data plays a role.

The EM Algorithm and Extensions


Author: Geoffrey McLachlan,Thriyambakam Krishnan
Publisher: John Wiley & Sons
ISBN: 0470191600
Category: Mathematics
Page: 384
View: 605

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The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Nonparametric Statistics with Applications to Science and Engineering


Author: Paul H. Kvam,Brani Vidakovic
Publisher: John Wiley & Sons
ISBN: 9780470168691
Category: Mathematics
Page: 448
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A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

Theory of Preliminary Test and Stein-Type Estimation with Applications


Author: A. K. Md. Ehsanes Saleh
Publisher: John Wiley & Sons
ISBN: 0471773743
Category: Mathematics
Page: 656
View: 2987

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Theory of Preliminary Test and Stein-Type Estimation withApplications provides a com-prehensive account of the theory andmethods of estimation in a variety of standard models used inapplied statistical inference. It is an in-depth introduction tothe estimation theory for graduate students, practitioners, andresearchers in various fields, such as statistics, engineering,social sciences, and medical sciences. Coverage of the material isdesigned as a first step in improving the estimates before applyingfull Bayesian methodology, while problems at the end of eachchapter enlarge the scope of the applications. This book contains clear and detailed coverage of basic terminologyrelated to various topics, including: * Simple linear model; ANOVA; parallelism model; multipleregression model with non-stochastic and stochastic constraints;regression with autocorrelated errors; ridge regression; andmultivariate and discrete data models * Normal, non-normal, and nonparametric theory of estimation * Bayes and empirical Bayes methods * R-estimation and U-statistics * Confidence set estimation

The Analysis of Covariance and Alternatives

Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies
Author: Bradley Huitema
Publisher: John Wiley & Sons
ISBN: 9781118067468
Category: Mathematics
Page: 480
View: 8604

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A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.

Visual Statistics

Seeing Data with Dynamic Interactive Graphics
Author: Forrest W. Young,Pedro M. Valero-Mora,Michael Friendly
Publisher: John Wiley & Sons
ISBN: 1118165411
Category: Mathematics
Page: 363
View: 6347

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A visually intuitive approach to statistical data analysis Visual Statistics brings the most complex and advanced statisticalmethods within reach of those with little statistical training byusing animated graphics of the data. Using ViSta: The VisualStatistics System-developed by Forrest Young and Pedro Valero-Moraand available free of charge on the Internet-students can easilycreate fully interactive visualizations from relevant mathematicalstatistics, promoting perceptual and cognitive understanding of thedata's story. An emphasis is placed on a paradigm for understandingdata that is visual, intuitive, geometric, and active, rather thanone that relies on convoluted logic, heavy mathematics, systems ofalgebraic equations, or passive acceptance of results. A companion Web site complements the book by further demonstratingthe concept of creating interactive and dynamic graphics. The bookprovides users with the opportunity to view the graphics in adynamic way by illustrating how to analyze statistical data andexplore the concepts of visual statistics. Visual Statistics addresses and features the followingtopics: * Why use dynamic graphics? * A history of statistical graphics * Visual statistics and the graphical user interface * Visual statistics and the scientific method * Character-based statistical interface objects * Graphics-based statistical interfaces * Visualization for exploring univariate data This is an excellent textbook for undergraduate courses in dataanalysis and regression, for students majoring or minoring instatistics, mathematics, science, engineering, and computerscience, as well as for graduate-level courses in mathematics. Thebook is also ideal as a reference/self-study guide for engineers,scientists, and mathematicians. With contributions by highly regarded professionals in the field,Visual Statistics not only improves a student's understanding ofstatistics, but also builds confidence to overcome problems thatmay have previously been intimidating.

Loss Models

From Data to Decisions
Author: Stuart A. Klugman,Harry H. Panjer,Gordon E. Willmot
Publisher: John Wiley & Sons
ISBN: 0470391332
Category: Business & Economics
Page: 784
View: 1027

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The Theory of Measures and Integration


Author: Eric M. Vestrup
Publisher: John Wiley & Sons
ISBN: 0470317957
Category: Mathematics
Page: 624
View: 2939

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An accessible, clearly organized survey of the basic topics of measure theory for students and researchers in mathematics, statistics, and physics In order to fully understand and appreciate advanced probability, analysis, and advanced mathematical statistics, a rudimentary knowledge of measure theory and like subjects must first be obtained. The Theory of Measures and Integration illuminates the fundamental ideas of the subject-fascinating in their own right-for both students and researchers, providing a useful theoretical background as well as a solid foundation for further inquiry. Eric Vestrup's patient and measured text presents the major results of classical measure and integration theory in a clear and rigorous fashion. Besides offering the mainstream fare, the author also offers detailed discussions of extensions, the structure of Borel and Lebesgue sets, set-theoretic considerations, the Riesz representation theorem, and the Hardy-Littlewood theorem, among other topics, employing a clear presentation style that is both evenly paced and user-friendly. Chapters include: * Measurable Functions * The Lp Spaces * The Radon-Nikodym Theorem * Products of Two Measure Spaces * Arbitrary Products of Measure Spaces Sections conclude with exercises that range in difficulty between easy "finger exercises"and substantial and independent points of interest. These more difficult exercises are accompanied by detailed hints and outlines. They demonstrate optional side paths in the subject as well as alternative ways of presenting the mainstream topics. In writing his proofs and notation, Vestrup targets the person who wants all of the details shown up front. Ideal for graduate students in mathematics, statistics, and physics, as well as strong undergraduates in these disciplines and practicing researchers, The Theory of Measures and Integration proves both an able primary text for a real analysis sequence with a focus on measure theory and a helpful background text for advanced courses in probability and statistics.

Nonparametric Statistical Methods


Author: Myles Hollander,Douglas A. Wolfe,Eric Chicken
Publisher: John Wiley & Sons
ISBN: 1118553292
Category: Mathematics
Page: 848
View: 6162

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Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.

Linear Models

The Theory and Application of Analysis of Variance
Author: Brenton R. Clarke
Publisher: John Wiley & Sons
ISBN: 9780470377970
Category: Mathematics
Page: 288
View: 3057

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An insightful approach to the analysis of variance in the study of linear models Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from basic mathematical concepts in linear algebra. The author begins with a presentation of the classic fixed-effects linear model and goes on to illustrate eight common linear models, along with the value of their use in statistics. From this foundation, subsequent chapters introduce concepts pertaining to the linear model, starting with vector space theory and the theory of least-squares estimation. An outline of the Helmert matrix is also presented, along with a thorough explanation of how the ANOVA is created in both typical two-way and higher layout designs, ultimately revealing the distribution theory. Other important topics covered include: Vector space theory The theory of least squares estimation Gauss-Markov theorem Kronecker products Diagnostic and robust methods for linear models Likelihood approaches to estimation A discussion of Bayesian theory is also included for purposes of comparison and contrast, and numerous illustrative exercises assist the reader with uncovering the nature of the models, using both classic and new data sets. Requiring only a working knowledge of basic probability and statistical inference, Linear Models is a valuable book for courses on linear models at the upper-undergraduate and graduate levels. It is also an excellent reference for practitioners who use linear models to conduct research in the fields of econometrics, psychology, sociology, biology, and agriculture.

Periodically Correlated Random Sequences

Spectral Theory and Practice
Author: Harry L. Hurd,Abolghassem Miamee
Publisher: John Wiley & Sons
ISBN: 9780470182826
Category: Mathematics
Page: 384
View: 9473

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Uniquely combining theory, application, and computing, this book explores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary) processes has become increasingly popular in a range of research areas such as meteorology, climate, communications, economics, and machine diagnostics. Periodically Correlated Random Sequences presents the main ideas of these processes through the use of basic definitions along with motivating, insightful, and illustrative examples. Extensive coverage of key concepts is provided, including second-order theory, Hilbert spaces, Fourier theory, and the spectral theory of harmonizable sequences. The authors also provide a paradigm for nonparametric time series analysis including tests for the presence of PC structures. Features of the book include: An emphasis on the link between the spectral theory of unitary operators and the correlation structure of PC sequences A discussion of the issues relating to nonparametric time series analysis for PC sequences, including estimation of the mean, correlation, and spectrum A balanced blend of historical background with modern application-specific references to periodically correlated processes An accompanying Web site that features additional exercises as well as data sets and programs written in MATLAB® for performing time series analysis on data that may have a PC structure Periodically Correlated Random Sequences is an ideal text on time series analysis for graduate-level statistics and engineering students who have previous experience in second-order stochastic processes (Hilbert space), vector spaces, random processes, and probability. This book also serves as a valuable reference for research statisticians and practitioners in areas of probability and statistics such as time series analysis, stochastic processes, and prediction theory.

Statistik mit Excel für Dummies


Author: Joseph Schmuller
Publisher: John Wiley & Sons
ISBN: 3527811702
Category: Computers
Page: 535
View: 1363

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Statistiken und Aussagen zu Wahrscheinlichkeiten begegnen uns heute überall: Die Umsatzentwicklung in Unternehmen, Hochrechnungen für Wahlergebnisse, PISA-Ergebnisse fünfzehnjähriger Schüler sind nur drei von zahlreichen Beispielen. Joseph Schmuller zeigt Ihnen in diesem Buch, wie Sie die Zahlen in den Griff bekommen und Daten, Statistiken und Wahrscheinlichkeiten richtig lesen und interpretieren. Dafür brauchen Sie keinen Statistikkurs zu belegen und kein Mathegenie zu sein. Für alles gibt es in Excel die passende Funktion und das passende Werkzeug. So können Sie Theorie und Praxis sofort miteinander verbinden.

The Construction of Optimal Stated Choice Experiments

Theory and Methods
Author: Deborah J. Street,Leonie Burgess
Publisher: John Wiley & Sons
ISBN: 9780470148556
Category: Mathematics
Page: 320
View: 6487

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The most comprehensive and applied discussion of stated choice experiment constructions available The Construction of Optimal Stated Choice Experiments provides an accessible introduction to the construction methods needed to create the best possible designs for use in modeling decision-making. Many aspects of the design of a generic stated choice experiment are independent of its area of application, and until now there has been no single book describing these constructions. This book begins with a brief description of the various areas where stated choice experiments are applicable, including marketing and health economics, transportation, environmental resource economics, and public welfare analysis. The authors focus on recent research results on the construction of optimal and near-optimal choice experiments and conclude with guidelines and insight on how to properly implement these results. Features of the book include: Construction of generic stated choice experiments for the estimation of main effects only, as well as experiments for the estimation of main effects plus two-factor interactions Constructions for choice sets of any size and for attributes with any number of levels A discussion of designs that contain a none option or a common base option Practical techniques for the implementation of the constructions Class-tested material that presents theoretical discussion of optimal design Complete and extensive references to the mathematical and statistical literature for the constructions Exercise sets in most chapters, which reinforce the understanding of the presented material The Construction of Optimal Stated Choice Experiments serves as an invaluable reference guide for applied statisticians and practitioners in the areas of marketing, health economics, transport, and environmental evaluation. It is also ideal as a supplemental text for courses in the design of experiments, decision support systems, and choice models. A companion web site is available for readers to access web-based software that can be used to implement the constructions described in the book.

Applied Linear Regression


Author: Sanford Weisberg
Publisher: John Wiley & Sons
ISBN: 1118594851
Category: Mathematics
Page: 368
View: 8316

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Praise for the Third Edition "...this is an excellent book which could easily be used as acourse text..." —International Statistical Institute The Fourth Edition of Applied LinearRegression provides a thorough update of the basic theoryand methodology of linear regression modeling. Demonstrating thepractical applications of linear regression analysis techniques,the Fourth Edition uses interesting, real-worldexercises and examples. Stressing central concepts such as model building, understandingparameters, assessing fit and reliability, and drawing conclusions,the new edition illustrates how to develop estimation, confidence,and testing procedures primarily through the use of least squaresregression. While maintaining the accessible appeal of eachprevious edition,Applied Linear Regression, FourthEdition features: Graphical methods stressed in the initial exploratory phase,analysis phase, and summarization phase of an analysis In-depth coverage of parameter estimates in both simple andcomplex models, transformations, and regression diagnostics Newly added material on topics including testing, ANOVA, andvariance assumptions Updated methodology, such as bootstrapping, cross-validationbinomial and Poisson regression, and modern model selectionmethods Applied Linear Regression, Fourth Edition is anexcellent textbook for upper-undergraduate and graduate-levelstudents, as well as an appropriate reference guide forpractitioners and applied statisticians in engineering, businessadministration, economics, and the social sciences.