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Welcome to the new Wiley Maths & Statistics Rights Highlights Magazine To enquire about rights availability and request reading copies, please email Julie Attrill. jattrill@wiley.com We look forward to receiving your new titles requests!

Statistical Methods for Censored Environmental Data Using Minitab & R 2e Dennis R. Helsel 978-0-470-47988-9 362 pp. Pub: 30/12/11 This second edition introduces methods for censored data that are directly applicable to air quality, water quality, soil, and contaminants in biota, among other media. Most of the methods come from the field of survival analysis, where the primary variable being investigated is length of time. However, this book applies these methods to environmental measures such as concentration.

Most methods cited in the book are widely used in medical and industrial settings, but have been rarely applied to issues of environmental health, e.g. contamination of ground and surface water, soils, air quality, fish, etc. Steps necessary to take these existing procedures, based on ‘survival analyses of right-censored data applied to left-censored environmental data, are fully illustrated. Within each chapter, examples have been provided in sufficient detail so that readers may apply these methods to their own work.

• Solves a current dilemma that occurs across a wide spectrum of environmental science – how to correctly analyze and interpret censored data (data below detection limits).

• The book has been fully revised, and includes a new section on testing censored data for normal distributions.

• Solutions to the exercises are provided at the end of every chapter.

Readership: environmental professionals, including biologists and ecologists in government, consulting firms, and academia; students on undergraduate and graduate courses in statistics for the sciences and environmental statistics.

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"...an excellent addition to an upper level undergraduate course on environmental statistics, and it is a 'must-have' desk reference for environmental practitioners dealing with censored datasets." (Vadose Zone Journal)

Simplicity, Complexity and Modelling Mike Christie, Andrew Cliffe, Philip Dawid, Stephen S Senn 978-0-470-74002-6 232 pp. Pub: 28/10/11 A shared understanding across scientific disciplines regarding approaches used to modelling complex phenomena is becoming increasingly important. Three main issues are of vital concern: the balance between simplicity and complexity required in modelling complex data, how forecasts can be combined using more than one model and how model uncertainty exists, how this can be propagated into predictions. These issues are addressed by the EPSRC- funded project on which this book is based.

The book consists of four sections: an introductory section covering important concepts in modelling and outlining different modelling traditions; subject specific chapters illustrating modelling approaches in various disciplines; a summary chapter explaining what issues have been resolved and which remain unresolved; and a glossary giving terms commonly used in different modelling traditions.

This book includes a number of case studies of complex modelling, including climate change, flood risk, deterministic computer modelling, showing how well each model can predict reality.

• Written by experienced, respected and well established statisticians and engineers to facilitate communication between modellers in different disciplines.

• Provides a number of case studies of complex modelling.

• Uses a variety of methods to illustrate and to demonstrate complex modelling across a wide range of areas including climate modelling, drug development, and industrial engineering.

• Supported by downloadable datasets via website link.

Readership: scientists involved with modelling complex systems; postgraduate students following statistics, mathematics and physical sciences subjects.

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An Introduction to Bootstrap Methods with Applications to R Michael R. Chernick, Robert A. LaBudde 978-0-470-46704-6 208 pp. Pub: 16/09/11 This book provides both an elementary and a modern introduction to the bootstrap for students who do not have an extensive background in advanced mathematics. It offers reliable, hands-on coverage of the bootstrap's considerable advantages – as well as its drawbacks. The book outpaces the competition by skilfully presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, the book exhibits counterexamples to the consistency of bootstrap methods. The authors take great care to draw connections between the more traditional resampling methods and the bootstrap, oftentimes displaying helpful computer routines in R. Emphasis throughout the book is on the use of the bootstrap as an exploratory tool including its value in variable selection and other modelling environments.

• Includes a listing of the most extensive and detailed bootstrap bibliography available on the market, including more than 2,500 references.

• Solutions to bootstrap failures are presented, such as finite populations and extreme values.

• Examples and explanations of when and why the bootstrap is not effective are presented for learning and comparative purposes.

• Discussions of applications to a wide variety of confidence intervals, hypothesis testing, and estimation problems (including pharmaceutical, genomics, and economics) are given, oftentimes at the expense of detailed explanations and/or needless theoretical justification.

Readership: advanced undergraduate and graduate students on courses in either resampling or the bootstrap; practitioners in industry.

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Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB Russell B. Millar 978-0-470-09482-2 376 pp. Pub: 02/09/11

Maximum Likelihood Estimation & Inference provides an accessible and practical introduction to likelihood modelling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. The methods are implemented in SAS – the most widely used statistical software package – and the data sets and SAS code are provided on a website, enabling the reader to use the methods to solve problems in their own work.

• Adopts a practical approach, providing the relevant tools required for analyzing real data.

• Covers more advanced topics, including mixed effects models and the use of maximum likelihood variants such as estimating equations, residual likelihood and partial likelihood.

• Features applications from a range of disciplines, including medicine, biology, and ecology.

• Supported by a website featuring SAS code and data sets.

Readership: applied scientists, with a basic understanding of statistics, who collect and analyze data; graduate students of statistics.

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Bayesian Analysis of Stochastic Process Models David Insua, Fabrizio Ruggeri, Mike Wiper 978-0-470-74453-6 320 pp. Pub: 16/03/12 This book provides analysis of stochastic processes from a Bayesian perspective, with coverage of the main classes of stochastic processing, including modelling, computational, inference, prediction, decision-making and important applied models based on stochastic processes. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable volume.

• A unique book on Bayesian analyses of stochastic process based models.

• Covers the main classes of stochastic processing including modelling and computational aspects.

• Features inference, prediction and decision making.

• Each chapter includes case studies.

Readership: researchers in stochastic processes/Bayesian analysis, practitioners of OR stochastic modelling; advanced postgraduates interested in these fields; undergraduates interested in stochastic processes/Bayesian analysis.

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Extreme Values: Statistical Analysis Using R Lee Fawcett, David Walshaw 978-0-470-74645-5 384 pp. Pub: 02/03/12 The statistical analysis of extremes is becoming more and more prevalent as we observe increasing levels of variability and turbulence, both in the natural world and within social organizations such as commercial and financial institutions.

In this book, full coverage is given to the analysis of extreme value data using R, providing the reader with the best starting point for analyzing data when the aim is inference about extreme values of the underlying process. The main topics in extreme value analysis are featured, together with a clear practical guide on how to implement the relevant statistical analysis using R. The book is aimed at those needing to carry out extreme value analyses, examples used will be taken from applications in engineering, reliability studies and in financial analysis where extremes are of interest (e.g. insurance).

• Examples and case studies are drawn from a broad subject area including environmental sciences, engineering and financial analysis.

• Illustrates the theory behind the techniques but also their application.

• Includes some of the more "cutting edge" techniques in extremes.

• Features Bayesian inference for extremes and spatial extremes.

• A supporting website contains the full data sets for downloading

Readership: environmental scientists, economists, engineers, and statisticians working in extremes or whose work includes the analysis of extremes; graduate students and researchers in environmental science and econometrics.

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The Essentials of Biostatistics for Physicians, Nurses, and Clinicians Michael R. Chernick 978-0-470-64185-9 224 pp. Pub: 05/09/11 Many universities, hospitals, and medical research facilities offer short courses in introductory biostatistics for clinicians, fellows, nurses, and health practitioners to become familiarized with statistical methods. Written in a concise, clear, and straight-to-the-point style, this book begins with a basic introduction to the relationship between biostatistics and medical research. Subsequent chapters explore numerical techniques, including sampling from populations; graphics and summary statistics; normal distribution and related properties; estimating means and proportions; hypothesis testing; correlation; regression; diagnostics; logistic regression; contingency tables; nonparametric methods; and survival analysis.

• Each chapter presents statistical concepts in a concise, straightforward, and applications-driven style that fits the needs of the everyday medical practitioner.

• The author draws from his own experience teaching biostatistics courses for nurses and practitioners, ensuring that the material is presented in a comprehensible and accessible format.

• Exercise sets at the end of each chapter are accompanied by selected solutions, allowing readers to test their comprehension of the presented concepts and techniques.

Readership: upper-undergraduate and graduate levels students studying biostatistics in the health sciences; clinicians, nurses, and physicians as well as researchers in the fields of medicine, public health, and biostatistics.

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Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations Glenn J. Myatt, Wayne P. Johnson 978-0-470-53649-0 386 pp. Pub: 30/09/11 While the first two books in the Making Sense of Data series discussed data visualization, the central role that graphics could play in data exploration and mining was not addressed. If graphics are to take centre stage in the exploration and understanding of a dataset, careful consideration needs to be given to typical tasks performed by professionals and analysts including how they share and discuss what has been learned about their data with others, the design of an interaction model to effectively support these tasks, and the implications of that interaction model for system and component architectures as well as data models. Building an interactive business intelligence system that supports incremental visual exploration and data mining requires knowledge that crosses many disciplines including statistics, data mining, statistical and data mining graphics, human-computer interaction, and computer science. This book integrates relevant information spread across research papers and books from these disciplines, and describes how to build systems that combine mainstream software technologies with advanced approaches in data visualization such as graphical grammars.

• Provides a hands-on guide for users who have little statistical background or for those interested in exploiting graphical grammars for data exploration and mining.

• Focuses on the tasks of data exploration and mining, and what would be required to design a system that supports these tasks through interactive graphics.

Readership: computational statisticians, software engineers, and researchers; graduate students in data analysis and data mining courses.

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Six Sigma Quality Improvement with Minitab 2e G. Robin Henderson 978-0-470-74174-0 528 pp. Pub: 01/08/11 A practical resource, this second edition of the book offers a clear understanding of and the implementation of statistical methods fundamental to the Six Sigma approach to the continuous improvement of products, processes and services. It demonstrates key statistical methods in the application of the Six Sigma methodology and their implementation via the widely used statistical software package Minitab. The book includes: Pareto charts and cause-and effect diagrams; time-weighted control charts – moving, average, CUSUM and EWMA; multivariate control charts – an introduction; acceptance sampling by attributes and variables; tests of association using the chi-square distribution; logistic regression; and Taguchi experimental designs.

• Reflects the latest release of Minitab software (version 16).

• Provides an introduction to statistical methods for Six Sigma and quality improvement using one of the most widely used statistical software packages, Minitab.

• Informative follow-up exercises from a wide variety of scenarios are provided.

• Random data generation facilities in Minitab are used to aid understanding of key statistical results and concepts.

• Supported by a website featuring full data sets and extra computer code.

Readership: primarily aimed at Six Sigma Green and Black Belts, from organisations that use Minitab; undergraduate or postgraduate courses incorporating Six Sigma or other approaches to quality improvement and also for courses on general statistical methods.

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Statistical Pattern Recognition 3e Andrew R. Webb, Keith D. Copsey, Gavin Cawley 978-0-470-68228-9 616 pp. Pub: 07/10/11

Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including engineering, statistics, computer science and the social sciences.

The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set.

• Contains descriptions of up-to-date methodology and pattern processing techniques, including recent advances in nonparametric approaches to discrimination.

• Illustrates techniques with examples of real-world applications including biometrics and security.

• Includes a variety of exercises, now updated to include extensive computer projects.

• Accompanied by a supporting website, featuring new exercises, solutions manual and lecture notes.

Readership: practitioners and researchers in the field of pattern recognition; graduate students studying pattern processing, neural networks, data mining and mathematical statistics; technical professionals working in advanced information development environments.

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A Career in Statistics Gerald J. Hahn, Necip Doganaksoy 978-0-470-40441-6 360 pp. Pub: 01/08/11 A Career in Statistics prepares readers for careers in statistics by emphasizing essential concepts and practices beyond the technical tools provided in standard courses and texts. This insider's guide from internationally recognized applied statisticians helps readers decide whether a career in statistics is right for them, provides hands-on guidance on how to prepare for such a career, and shows how to succeed on the job.

The book provides non-technical guidance for a successful career. The authors' extensive industrial experience is supplemented by insights from contributing authors from government and academia, including: Carol Joyce Blumberg; Leonard M. Gaines; Lynne B. Hare; William Q. Meeker; and Josef Schmee.

• The book is written by a "legend" (Hahn) in the field of industrial statistics. The content is based on a combined 75 years of work and academic experience.

• Chapters conclude with a summary of "major take-aways" and a series of discussion and/or technical questions to stimulate further thought and creativity.

• The authors have asked various eminent statisticians for their views on what makes a successful applied statistician, and what advice they would give aspiring statisticians planning or embarking on a career with a company.

• An author web site is available that provides further detail on some topics, as well as post-publication thoughts and reader commentary.

Readership: students on courses in statistics, career planning, and consulting.

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Introduction to Cultural Mathematics: With Case Studies in the Otomies and the Incas Thomas E. Gilsdorf 978-1-118-11552-7 320 pp. Pub: 16/03/12

The topic of cultural mathematics (also called ethno- mathematics) is a fascinating topic about the human aspects of mathematics, and it is easy to ask readers to participate in the topic by thinking about mathematics from their own perspectives.

This exciting new book has two main parts: Part I covers cultural aspects of mathematics, numeration and number symbols, kinship relations, art and decoration, games and divination, and calendars. Part II includes two chapters consisting of case studies. The cultures studied are the Otomies of central Mexico and the Incas of South America. Each chapter concludes with exercises, followed by further notes about the topics discussed in that chapter. Hints and comments about the exercises are also provided at the end of the book.

• Provides a non-traditional, evidence-based approach that promotes diversity and respect for cultural heritages by capitalizing on the role that civilizations play in the deliberate construction, appropriation, and dissemination of scientific and mathematical knowledge.

• Includes three types of exercises at the end of each chapter: short answer, calculations, and essay/ discussion.

• Features numerous illustrations throughout, many examples that illustrate the presented concepts, and motivational questions that ask readers to contemplate mathematical ideas informally.

Readership: students on courses in cultural mathematics (or ethnomathematics), the history of mathematics, multicultural education; and/or cultural studies.

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Shifting the Earth: The Mathematical Quest to Understand the Motion of the Universe Arthur Mazer 978-1-118-02427-0 326 pp. Pub: 16/09/11

This book presents the story of how humanity shed its geocentric fabrication and moved to a heliocentric universe and details this transition alongside the mathematics to reveal the influences of the one upon the other. Historical and social contexts behind the mathematical advances are provided, and the history includes the larger political and philosophical environment that affected the lives of the contributing scientists. Mathematically, the book provides a modern version of the more acclaimed models. A prime objective of the book is to illustrate how Kepler's heliocentric pathway evolved from Ptolemy's geocentric model with Copernicus as the critical intermediary. Each chapter contains both historical passages and mathematical formulations. The writing flows in a manner that allows readers to engage the mathematics to their personal level of comfort, skipping portions that are not of interest, and still follow the entire historical narrative.

• Discusses how religious, philosophical, and political institutions influenced the development of mathematics and, in turn, how mathematical development changed the institution.

• Features mathematical treatment of significant contributions to both the geocentric and heliocentric theories.

• Communicates historical mathematical and scientific advancements using modern concepts and notation to aid reader comprehension.

Readership: mathematicians, engineers, software developers, actuaries, or for the mathematically inclined general population; students on undergraduate mathematics, science, physics, philosophy, and history courses.

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Stochastic Methods for Pension Funds Pierre De Volder, Jacques Janssen, Raimondo Manca 978-1-84821-204-6 320 pp. Pub: 15/03/12

Over the last few years, quantitative finance has seen an extraordinary level of research and interest, both from an academic point of view and for its practical applications.

At the same time, the pension issue is clearly a major economic and financial topic. Surprisingly few books are devoted to application of modern stochastic calculus to pension analysis.

This book fills this gap and shows how recent methods of stochastic finance can be useful in the risk management of pension funds. Methods of optimal control are explored and applied to fundamental problems such as the optimal asset allocation of the fund or the cost spreading of a pension scheme. In these various problems, financial as well as demographic risks are addressed and modelled.

• Fills a gap in the market, as few books are devoted to application of modern stochastic calculus to pension analysis.

• Demonstrates how recent methods of stochastic finance can be useful for to the risk management of pension funds.

• Methods of optimal control are especially developed and applied to fundamental problems.

Readership: finance students; financial analysts and practitioners; professionals interested in this area.

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The Language of Mathematics: Utilizing Math in Practice Robert L. Baber 978-0-470-87889-7 434 pp. Pub: 02/09/11

This book views mathematical notation as a language and develops the implications of this view for translating English text into mathematical expressions and mathematical models, i.e. for applying mathematics to problems described in English. In order to apply mathematics to a practical problem, one must first transform an English statement of the problem and the requirements for its solution into mathematical expressions. This book examines this process in detail, presents new insight into it, and develops explicit guidelines for this important step. It identifies the basic elements (values, variables, and functions) of the language of mathematics, and presents the grammatical rules for combining them into expressions and other structures. The approach presented in this book makes mathematics accessible to many people who have been turned off from mathematics by their early exposure to it.

• Details how various types of values, variables, functions, and expressions in the language of mathematics correspond to a particular part of speech or grammatical component.

• Facilitates communication between people with different areas of expertise working on specific problems to which mathematics is applied.

• Improves and increases the reader's insight into mathematics as well as how to utilize it in practice.

• Aids readers in applying mathematics to practical problems more easily, efficiently, and effectively.

Readership: the mathematically-inclined general population; those who are curious about the linguistic nature and aspects of mathematical notation; engineers, technicians, and managers.

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Analyzing Quantitative Data: An Introduction for Social Researchers Debra Wetcher-Hendricks 978-0-470-52683-5 414 pp. Pub: 05/08/11

Both an easy-to-use text and a quick reference guide, Analyzing Quantitative Data: An Introduction for Social Researchers offers a unique approach to quantitative research methods. The non-linear organization of the book allows readers to access and comprehend information relevant to their particular projects or assignments without loss of continuity. Intended as a sequel to texts that emphasize the data collection process, this book’s hands-on approach escorts students and practicing statisticians through organization of raw data, descriptive statistics, tests of significance, drawing conclusions, and techniques for writing research reports.

• The non-linear organization of the book allows readers to access information relevant to their particular projects or assignments without loss of continuity; though, great care has been taken by the author to systematically transition from one section to another.

• A related website features data sets, further instructional notes, and additional content.

• Numerous screen shots illustrate the presented the use of techniques in SPSS, and answers are provided to selected exercises.

Readership: advanced undergraduate or graduate students of sociology, psychology, statistics, education, public health, and government; and practicing statisticians involved in public service industries such as medicine, government, and education.

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Statistical Methods for Quality Improvement 3e Thomas P. Ryan 978-0-470-59074-4 704 pp. Pub: 05/09/11

Statistical methods for quality improvement offer numerous benefits for industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. An easy-to-read and easy-to-follow guide based on intuitive reasoning rather than heavy mathematics, this fully expanded and revised third edition of Statistical Methods for Quality Improvement offers upper-level undergraduate and graduate students clear, thorough coverage of all available techniques from basic control charts to regression and design of experiments, as well as the combined use of these tools.

• Based on intuitive reasoning rather than heavy mathematical formulae, making the material accessible to industrial engineers and applied statisticians alike.

• Features many new exercises in each chapter, with the solutions available in an appendix.

• Includes new coverage of multivariate EWMA charts and a method for detecting changing in the covariance matrix.

• Provides expanded discussions on determining the time of a parameter change and tolerance intervals.

• Tackles complicated topics including linear regression, Taguchi methods, and analysis of means, in a logical and engaging style.

Readership: upper level undergraduate and graduate students on courses on quality control and improvement and design of experiments; practicing statisticians, engineers, and physical scientists interested in statistical quality control.

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Introduction to Linear Regression Analysis 5e Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining 978-0-470-54281-1 712 pp. Pub: 09/12/11

Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modelling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with auto-correlated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.

• This fifth edition introduces and features the use of R and JMP software. SAS, S-Plus, and Minitab continue to be employed in this new edition, and the output from all of these packages can be found throughout the book.

• An accompanying website contains data sets, extensive problem solutions, software hints, and Power Point slides.

• New chapters have been added on Regression for Time Series Data and Experimental Designs for Regression Models.

• Written by academics and field practitioners, this book presents a comprehensive and thoroughly up-to-date look at regression analysis, the most widely used technique in statistics today.

Readership: upper-undergraduate level students; statistical practitioners who want solid methodology but may not be well-versed in mathematical or quantitative statistics.

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Handbook of Statistical Systems Biology Michael Stumpf, David J. Balding, Mark Girolami 978-0-470-71086-9 520 pp. Pub: 21/10/11

Systems Biology is moving away from the mathematical modelling and subsequent analysis of systems to a full statistical analysis and probabilistic reasoning over the inferences that can be made from these modelling exercises. There is a need for a comprehensive handbook covering the major themes in the area. This book provides a full and detailed treatment of important and emerging fields of statistical systems biology, with focus on characterising uncertainty and stochastic effects in mathematical models of biological systems. By leading experts in the field, it introduces statistical methods for complex systems and Bayesian methodology for systems biology; covers networks and graphical modelling as well as a wide range of statistical models and dynamical systems; and is packed with colour to help illustrate key concepts and accompanied by a multimedia website with 3D visualizations.

• Introduces statistical methods for complex systems and Bayesian methodology for systems biology.

• Covers networks and graphical modelling as well as a wide range of Statistical models and Dynamical systems.

• Discusses various applications for statistical systems biology, such as gene regulation, and pharmaco- dynamics.

• Packed with colour to help illustrate key concepts and accompanied by a multimedia website.

Readership: academics and researchers in the field of systems biology; pharmaceutical and biotechnology researchers working in industry; advanced level students of systems biology and bioinformatics.

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Statistics in Psychology Using R and SPSS Dieter Rasch, Klaus Kubinger, Takuya Yanagida 978-0-470-97124-6 544 pp. Pub: 02/12/11

This book covers all statistical methods needed in education and research in psychology, and looks at research questions when planning data sampling, that is, to design the intended study and calculate the sample sizes in advance. It also looks at the process of empirical research in seven stages: formulation of the problem; stipulation of the precision requirements; selecting the statistical model for the planning and analysis; (optimal) design of the experiment or survey; performing the experiment or the survey; statistical analysis of the observed results; and interpretation of the results.

• Presents all of the statistical methods needed in for research and practice in psychology, the social and behavioural sciences.

• Addresses both the simple statistics and univariate and multivariate methods.

• Covers experimental design, such as accurate calculation of sample sizes.

• Looks at conventional statistical analyses by actual empirical research examples, using SPSS and R.

• Discusses and uses results on characteristics of statistical test procedures.

Readership: advanced level students of psychology and the social sciences taking basic statistic courses; research workers in social and behavioural sciences.

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Sampling 3e Steven K. Thompson 978-0-470-40231-3 448 pp. Pub: 17/01/12

Following the high praise of the second edition by Technometrics as a necessity to "any good personal statistics library," this new edition covers basic and standard sampling design and estimation methods with special attention paid to methods for populations that are inherently difficult to sample. This book covers basic sampling; ratio and regression estimation; major useful designs; detectability, visibility and catchability methods; spatial sampling; and adaptive sampling designs. It also contains new examples, explanations, and updates for figures from previous editions to increase visualization throughout the book.

• Addresses new sampling developments and provides expanded discussion on the various techniques found throughout the book.

• Features new figures throughout the book to increase the visualization emphasis of the book.

• Covers standard sampling design and estimation methods and gives special attention to methods for populations that are inherently elusive, rare, clustered, hard to detect or difficult to sample.

• Provides new exercises and examples in every chapter, as well as updated references.

• Includes both classical and modern sampling design and estimation methods.

Readership: scientific researchers and others who use sampling in a variety of fields such as statistics, biostatistics, the health sciences, mathematics, ecology, and geology; and students on upper-level undergraduate or graduate-level course in statistical sampling.

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Question Evaluation Methods: Contributing to the Science of Data Quality Jennifer Madans, Kristen Miller, Aaron Maitland, Gordon Willis 978-0-470-76948-5 378 pp. Pub: 12/08/11

Reliability and validity are the most important quality aspects of survey questions. Featuring contributions from prominent researchers in the field of survey methodology, Question Evaluation Methods supplies various points of views on common question evaluation methods and provides insightful observations on best practices for data collection that can be applied across the health and social sciences.

• An interdisciplinary, cross-method analysis is supplied, incorporating popular survey techniques including cognitive interviewing, split sample experiments, field-based assessments, and behaviour coding.

• The book's broad scope of content and varying perspectives enforce important themes that all researchers must consider when working with question evaluation, such as implicit assumptions and limitations; benefits and advantages; and characterization of error.

• The book gives special focus to the specific role of response error in total survey error, a topic that is not given much coverage in the current survey literature.

• Each chapter supplies the background of a pretesting method from one perspective which is then followed by discussion of related issues and alternative points of view.

Readership: government statisticians, survey methodologists, and researchers and practitioners who carry out survey research across various disciplines; students on graduate level courses in survey research and question evaluation.

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Time Series Analysis and Forecasting by Example Søren Bisgaard, Murat Kulahci 978-0-470-54064-0 392 pp. Pub: 01/08/11

Times Series Analysis and Forecasting by Example presents seemingly difficult techniques and methodologies in an insightful and application-based way, through a hands-on and user-friendly approach. The book includes exercises, graphical techniques, examples, Excel spread- sheets, and software applications on time series analysis. The reference offers step-by-step procedures and instructions. This textbook is essential for students, emphasizing intuitive learning rather than theory through modelling the data in careful interpretation and use of modern statistical graphics.

• Emphasizes intuitive application of methods rather than mathematical rigor.

• Showcases many practical examples to illustrate the wide variety of application areas.

• Step-by-step procedures and instructions are emphasized when necessary.

• An author web site is available that features data sets, solutions, author hints, PowerPoint® slides, and software screenshots.

Readership: upper undergraduate and/or beginning graduate level statistics, business (MBA), finance, economics, engineering (industrial/operations), and chemistry students; applied statisticians; and practitioners in industry.

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T Applied Probability & Statistics Latent Variable Models and Factor Analysis: A Unified Approach/Bartholomew

Applied Probability & Statistics - Models ANOVA and ANCOVA: A GLM Approach/Rutherford

Biostatistics Bayesian Methods in Biostatistics/Lesaffre

Business & Finance Mathematical Finance/Alhabeeb

Clinical Trials A Practical Guide to Cluster Randomised Trials in Health Services Research/Eldridge Clinical Trial Design: Bayesian and Frequentist Approaches /Yin

Computational & Graphical Statistics An Elementary Introduction to Statistical Learning Theory/Kulkarni

Computational / Numerical Methods Mechanics of Discontinua/Munjiza

Data Analysis Compositional Data Analysis: Theory and Applications/Pawlowsky-Glahn

Database & Data Warehousing Technologies Classification Analysis of DNA Microarray Data/Peterson

Engineering Statistics A Primer on Experiments with Mixtures/Cornell Statistical and Managerial Techniques for Six Sigma Methodology: Theory and Application/Barone

Graph Theory Graph Edge Coloring: Vizing's Theorem and Goldberg's Conjecture/Stiebitz Graph Partitioning/Bichot

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Mathematical Modeling An Introduction to Mathematical Modeling: A Course in Mechanics/Oden

Modern/Abstract Algebra Introduction to Abstract Algebra/Nicholson

Multivariate Analysis Statistics for Imaging, Optics, and Photonics/Bajorski Multivariate Analysis for the Biobehavioral & Social Sciences: A Graphical Approach/Brown

Pattern Analysis Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging/Maji

Probability & Mathematical Statistics General Theory of Coherent Lower Previsions/Troffaes

Probability Theory Branching Random Walks in Nonhomogenous Environments/Yarovaya

Queuing Theory Fundamentals of Stochastic Networks/Ibe

Real Analysis Real Analysis: A Historical Approach/Stahl

Regression Analysis Adaptive Tests of Significance Using Permutations of Residuals:With R & SAS® /O'Gorman

Statistics for Social Sciences Agent-Based Computational Sociology/Squazzoni

Surveying Research Methods & Sampling Modern Analysis of Customer Satisfaction Surveys/Kenett

Time Series Causality: Statistical Perspectives and Applications/Berzuini