Statistics Textbooks - August 2010

Contents

Statistics for the Biological Sciences ..................3

Probability Theory and Applications..................5

Computational Statistics ..................................6

Statistics for Business and Finance ....................8

Statistics for Engineering ................................11

Statistics for the Social Sciences ......................13

Biostatistics ....................................................14

Environmental Statistics ..................................14

Statistical Genetics ..........................................15

Statistical Theory and Methods ......................16

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3

Statistics for the Biological Sciences

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New!

Introduction to Statistical DataAnalysis for the Life SciencesClaus Thorn Ekstrom and Helle SorensenUniversity of Copenhagen, Denmark

Selected Contents

Description of Samples and Populations

Linear Regression

Comparison of Groups

The Normal Distribution

Statistical Models, Estimation, andConfidence Intervals

Hypothesis Tests

Model Validation and Prediction

Linear Normal Models

Probabilities

The Binomial Distribution

The independent trials model

The binomial distribution

Estimation, confidence intervals, and hypothesis tests

Differences between proportions

Analysis of Count Data

The chi-square test for goodness-of-fit

2 × 2 contingency table

Two-sided contingency tables

Logistic Regression

Odds and odds ratios

Logistic regression models

Estimation and confidence intervals

Hypothesis tests

Model validation and prediction

Case Exercises

R commands and output and exercises appear at the end of each chapter.

Databases & Code also available online.

For more complete contents, visit www.crctextbooks.com

Developed from the authors’ courses at theUniversity of Copenhagen, this textbook coversall the usual material but goes further than othertexts. The authors imbue students with the abili-ty to model and analyze data early in the text andthen gradually fill in the blanks with needed prob-ability and statistics theory. While the main textcan be used with any statistical software, theauthors encourage a reliance on R. They providea short tutorial for students new to the softwareand include R commands and output at the endof each chapter. Ultimately, students come awaywith a computational toolbox that enables themto perform actual analysis for real data sets as wellas the confidence and skills to undertake moresophisticated analyses as their careers progress.

Features

• Includes numerous exercises, half of which can be done by hand

• Contains ten case exercises that encouragestudents to apply their knowledge to largerdata sets and learn more about approachesspecific to the life sciences

• Offers a tutorial for students new to R

• Provides data sets used in the text on a supporting website

• Emphasizes both data analysis and the mathematics underlying classical statisticalanalysis

• Covers modeling aspects of statistical analysiswith added focus on biological interpretations

• Explores applications of statistical software inanalyzing real-world problems and data sets

Solutions manual available for qualifying instructors

Catalog no. K11221, August 2010, 427 pp.Soft Cover, ISBN: 978-1-4398-2555-6, $69.95


4 Order your review copy at www.crctextbooks.com

Statistics for the Biological Sciences

IntroductoryStatistics An Introduction toStatistical Inferenceand Its Applicationswith RMichael W. TrossetIndiana University, Bloomington, USA

Emphasizing concepts rather than recipes, thistext provides a clear exposition of the methodsof statistical inference for students who are com-fortable with mathematical notation. Numerousexamples, case studies, and exercises are includ-ed. R is used to simplify computation, create fig-ures, and draw pseudorandom samples—not toperform entire analyses.

Features

• Explains how statistical methods are usedfor data analysis

• Uses the elementary functions of R to per-form the individual steps of statistical proce-dures

• Includes amusing anecdotes and trivia, suchas Ambrose Bierce’s definition of insurance

• Introduces basic concepts of inferencethrough a careful study of several importantprocedures, including parametric and non-parametric methods, ANOVA, and regression

• Presents many applications along with sup-porting data sets

• Contains exercises at the end of each chapter• Offers the R code and data sets available for

download online


Contents

Experiments. Mathematical Preliminaries.Probability. Discrete Random Variables.Continuous Random Variables. QuantifyingPopulation Attributes. Data. Lots of Data.Inference. 1-Sample Location Problems. 2-Sample Location Problems. The Analysis ofVariance. Goodness-of-Fit. Association. SimpleLinear Regression. Simulation-Based Inference.R: A Statistical Programming Language. Index.

Catalog no. C9470, 2009, 496 pp.ISBN: 978-1-58488-947-2, $79.95

AppliedStochasticModellingSecond EditionByron J.T. MorganUniversity of Kent, UK

Praise for the First Edition

“There are plenty of interesting example data sets …The book covers much ground in quite a short space… In conclusion, I like this book and strongly rec-ommend it. …”

—Tim Auton, Journal of the Royal Statistical Society

Continuing in the tradition of its bestsellingpredecessor, this textbook remains an excellentresource for teaching students how to fit sto-chastic models to data. Although the book canbe used without reference to computationalprograms, the author provides the option ofusing powerful computational tools for stochas-tic modeling. All of the data sets and MATLAB®

and R programs found in the text as well as lec-ture slides and other ancillary material are avail-able for download online.

New to the Second Edition

• An extended discussion on Bayesian methods• A large number of new exercises • A new appendix on computational methods• Updated bibliography and improved figures

Contents

Introduction and Examples. Basic Model Fitting.Function Optimisation. Basic Likelihood Tools.General Principles. Simulation Techniques.Bayesian Methods and MCMC. GeneralFamilies of Models. Index of Data Sets. Index ofMATLAB Programs. Appendices. Solutions andComments for Selected Exercises. Bibliography.Index.

Catalog no. C6668, 2009, 368 pp., Soft CoverISBN: 978-1-58488-666-2, $60.95



5

Probability Theory and Applications


Introduction to Probability withMathematica®

Second EditionKevin J. HastingsKnox College, Galesburg, Illinois, USA

Contents

Discrete ProbabilityThe Cast of Characters Properties of Probability Simulation Random SamplingConditional ProbabilityIndependenceDiscrete DistributionsDiscrete Random Variables, Distributions, andExpectations

Bernoulli and Binomial Random VariablesGeometric and Negative Binomial RandomVariables

Poisson DistributionJoint, Marginal, and Conditional Distributions More on ExpectationContinuous ProbabilityFrom the Finite to the (Very) Infinite Continuous Random Variables and DistributionsContinuous ExpectationContinuous DistributionsThe Normal Distribution Bivariate Normal DistributionNew Random Variables from OldOrder Statistics Gamma DistributionsChi-Square, Student’s t, and F-DistributionsTransformations of Normal Random VariablesAsymptotic TheoryStrong and Weak Laws of Large Numbers Central Limit TheoremStochastic Processes and ApplicationsMarkov ChainsPoisson Processes QueuesBrownian MotionFinancial MathematicsAppendixReferencesIndex

“Introduction to Probability with Mathematicaadds computational exercises to the traditionalundergraduate probability curriculum without cut-ting out theory. … a good textbook for a class with astrong emphasis on hands-on experience with proba-bility. …”

—MAA Reviews, Dec. 2009

Updated to conform to Mathematica® 7.0, thissecond edition continues to show students howto easily create simulations from templates andsolve problems using Mathematica. It provides areal understanding of probabilistic modeling andthe analysis of data and encourages the applica-tion of these ideas to practical problems.


• Expanded section on Markov chains thatincludes a study of absorbing chains

• New sections on order statistics, transformationsof multivariate normal random variables, andBrownian motion

• More example data of the normal distribution • More attention on conditional expectation,

which has become significant in financialmathematics

• Additional problems from Actuarial Exam P• New appendix that gives a basic introduction

to Mathematica• New examples, exercises, and data sets,

particularly on the bivariate normal distribution

• New visualization and animation features fromMathematica 7.0

• Updated Mathematica notebooks on the CD-ROM

This text takes an interactive approach that com-plements today’s highly technological teachingenvironment. The accompanying CD-ROM offersinstructors the option of creating class notes,demonstrations, and projects.


Catalog no. C7938, January 2010, 465 pp.ISBN: 978-1-4200-7938-8, $89.95



Probability Theory and Applications Computational Statistics

Probability andStatistics with RMaria Dolores Ugarteand Ana F. MilitinoPublic University of Navarre,Pamplona, Spain

Alan T. ArnholtAppalachian State University,Boone, North Carolina, USA

“… Detailed executable codes and codes to gener-ate the figures [in R and S-PLUS] are available athttp://www1.appstate.edu/~arnholta/PASWR/front.htm … Students or self-learners can learn somebasic techniques for using R in statistical analysis ontheir way to learning about various topics in proba-bility and statistics. … wonderful stand-alone text-book … .”

—Technometrics, May 2009, Vol. 51, No. 2



New!

Graphics forStatistics andData Analysiswith RKevin J. KeenUniversity Northern BritishColumbia, Prince George,Canada

Showing students how to use graphics to displayor summarize data, this text presents the basicprinciples of sound graphical design and appliesthese principles to engaging examples using thegraphical functions available in R. It offers a widearray of graphical displays for the presentation ofdata, including modern tools for data visualiza-tion and representation. Downloadable R codeand data for the figures in the text are availableat www.graphicsforstatistics.com

Catalog no. C0756, April 2010, 489 pp.ISBN: 978-1-58488-087-5, $69.95

Introduction toProbabilitywith RKenneth P. BaclawskiNortheastern University, Boston,Massachusetts, USA

“… The book is clearly written and very well-organ-ized and it stems in part from a popular course atMIT taught by the late Gian-Carlo Rota … . Thebook goes well beyond the MIT course in makingextensive use of computation and R. … It wouldserve as an exemplary test for the first semester of atwo-semester course on probability and statistics.”

—Journal of Statistical Software, April 2009



InteractiveGraphics forData AnalysisPrinciples andExamples

Martin TheusMunich, Germany

Simon UrbanekMadison, New Jersey, USA

This full-color text discusses EDA and how inter-active graphical methods can help students gaininsights as well as generate new questions andhypotheses from data sets. It uses Mondriansoftware and R. The authors provide exercises atthe end of each chapter and offer course sug-gestions, slides, and extra code on the book’swebsite. Times Higher Education (Dec. 2009)called the text “a brief, powerful book withexcellent and clear graphics.”






7

Computational Statistics


Computational StatisticsAn Introduction to RGünther SawitzkiStatLab, Heidelberg, Germany

Contents

IntroductionBasic Data Analysis R Programming Conventions Generation of Random Numbers and PatternsCase Study: Distribution DiagnosticsMoments and Quantiles Regression General Regression Model Linear ModelVariance Decomposition and Analysis of Variance Simultaneous InferenceBeyond Linear RegressionComparisonsShift/Scale Families and Stochastic Order QQ Plot, PP Plot, and Comparison ofDistributions

Tests for Shift Alternatives A Road Map Power and Confidence Qualitative Features of DistributionsDimensions 1, 2, 3, …, ∞ Dimensions Selections Projections Sections, Conditional Distributions, and Coplots Transformations and Dimension Reduction Higher Dimensions High DimensionsAppendix: R as a Programming Language andEnvironment

References Functions and Variables by Topic Function and Variable Index Subject Index

R complements, a statistical summary, and literature and additional References are included

with most chapters

“… a fresh perspective on teaching statistics. … Thebook introduces its topics and the correspondingmethodologies well. … the book is well put togetherand quite enjoyable for its purpose of serving a smallcourse on computational statistics.”

—Journal of Statistical Software, Dec. 2009

“… it is the integration of interesting examples andassociated R code that make the text a pleasure toread and work through. The examples are neitheroverly trivial … nor excessively complicated, and theR code is similarly accessible without being either toosimple or complex. … It could also be useful as a sup-plementary text for upper-level undergraduate orgraduate courses with labs that use R… .”

—Ronald D. Fricker, Jr., The American Statistician

Suitable for a compact course or self-study, this text illustrates how to use R for data analysis, statistical programming, and graphics.Integrating R code, examples, and a color insert,it only requires basic knowledge of statistics and computing.

Features

• Shows students how R can be employed totackle statistical problems

• Focuses on the underlying concepts of statistics

• Covers distribution diagnostics, Monte Carlotests, ANOVA, general linear models, distribution-free tests, and dimension reduction

• Includes numerous exercises and real-worldexamples from biology, medicine, and more

• Provides a handy appendix that describes elements and functions of R by topic

• Offers the full R source code for all examples,selected solutions, and other ancillary materialat http://sintro.r-forge.r-project.org/

Catalog no. C6782, 2009, 264 pp.SBN: 978-1-4200-8678-2, $79.95



Statistics for Business and Finance

Stochastic Financial ModelsDouglas KennedyTrinity College, Cambridge, UK

Contents

Portfolio ChoiceIntroductionUtilityMean-variance analysisThe Binomial ModelOne-period modelMulti-period modelA General Discrete-Time ModelOne-period modelMulti-period modelBrownian MotionIntroductionHitting-time distributionsGirsanov’s theoremBrownian motion as a limitStochastic calculusThe Black–Scholes ModelIntroductionThe Black–Scholes formulaHedging and the Black–Scholes equationPath-dependent claimsDividend-paying assetsInterest-Rate ModelsIntroductionSurvey of interest-rate modelsGaussian random-field modelAppendix A: Mathematical PreliminariesAppendix B: Solutions to the ExercisesFurther ReadingReferencesIndex

Exercises appear at the end of each chapter

“This book is a superb beginning-level text for seniorundergraduate/graduate mathematicians, which isbased on lectures delivered by its author to many gen-erations of appreciative Cambridge mathematicians.Many of my own Ph.D. and masters students havetaken Dr. Kennedy’s course to uniformly good reviews;this readable book will make its material available toa worldwide audience. … the book contains 40 pagesof fully worked out solutions … .”

—M.A.H. Dempster, Centre for Financial Research,Statistical Laboratory, University of Cambridge, UK

Developed from the esteemed author’s advancedundergraduate and graduate courses at theUniversity of Cambridge, this text provides ahands-on, sound introduction to mathematicalfinance. The author first presents the classical top-ics of utility and the mean-variance approach toportfolio choice. Focusing on derivative pricing,he then covers the binomial model, the generaldiscrete-time model, Brownian motion, theBlack–Scholes model and various interest-ratemodels.

Features

• Presents a self-contained treatment of mathematical models in finance by includingthe relevant mathematical background

• Takes a hands-on approach to calculations,enabling students to derive the prices of many common financial products

• Assumes no prior knowledge of stochastic calculus or measure-theoretic probability

• Includes exercises in each chapter and solutions in an appendix

• Fills the void between surveys of the field withrelatively light mathematical content andbooks with a rigorous, formal approach to sto-chastic integration and probabilistic ideas



9



Interest Rate ModelingTheory and PracticeLixin WuUniversity of Science & Technology, Kowloon, Hong Kong

Selected Contents

The Basics of Stochastic Calculus The Martingale Representation Theorem Interest Rates and Bonds The Heath–Jarrow–Morton ModelShort-Rate Models and LatticeImplementationThe LIBOR Market ModelLIBOR Market InstrumentsThe LIBOR Market ModelPricing of Caps and Floors Pricing of SwaptionsSpecifications of the LIBOR Market ModelMonte Carlo Simulation MethodCalibration of LIBOR Market ModelImplied Cap and Caplet Volatilities Calibrating the LIBOR Market Model to CapsCalibration to Caps, Swaptions, and InputCorrelations Calibration MethodologiesSensitivity with Respect to the Input Prices Volatility and Correlation AdjustmentsAdjustment due to CorrelationsAdjustment due to ConvexityTiming Adjustment Quanto Derivatives Affine Term Structure ModelsAn Exposition with One-Factor ModelsAnalytical Solution of Riccarti Equations Pricing Options on Coupon Bonds Distributional Properties of Square-RootProcesses Multi-Factor Models Swaption Pricing under ATSMs


“The book presents in a balanced way both theoryand applications of interest rate modeling. …Thebook can serve as a textbook. It is self-contained inmathematics and presents rigorous justifications foralmost all results. …”

—Pavel Stoynov, Zentralblatt MATH 1173

Containing many results that are new or existonly in recent research articles, this text portraysthe theory of interest rate modeling as a three-dimensional object of finance, mathematics, andcomputation. It introduces all models with finan-cial-economical justifications, develops optionsalong the martingale approach, and handlesoption evaluations with precise numerical meth-ods. Taking a top-down approach, the authorprovides students with a clear picture of thisimportant subject by not overwhelming themwith too many specific models. The text includesexercises and real-world examples, along withcode, tables, and figures accessible on theauthor’s website.

Features

• Presents a complete cycle of model construction and applications, showing students how to build and use models

• Incorporates high-power numerical methodologies

• Provides a systematic treatment of intriguingindustrial issues, such as volatility and correlation adjustments

• Contains exercise sets and a number of examples, with many based on real marketdata

• Includes comments on cutting-edge research,such as volatility-smile, positive interest-ratemodels, and convexity adjustment

• Offers code, tables, and figures on theauthor’s website






AppliedStatistics forBusiness andEconomicsRobert M. LeekleyIllinois Wesleyan University,Bloomington, USA

Designed for a one-semester course, this textoffers students in business and the social sci-ences an effective introduction to some of themost basic and powerful techniques available forunderstanding their world. Numerous interest-ing and important examples reflect real-life situ-ations and calculations can be performed usingany standard spreadsheet package. To help withthe examples, the author offers both actual andhypothetical databases on his website. Afterreading the book, students will be able to sum-marize data in insightful ways using charts,graphs, and summary statistics as well as makeinferences from samples.

Features

• Highlights the connections among variousstatistical topics

• Encourages the use of spreadsheets to handle raw data sets large enough to bemeaningful, helping students gain a greaterunderstanding of real-life applications

• Includes answers to odd-numbered problemsat the back of the book

• Offers databases available for download onhttp://iwu.edu/~bleekley

Contents

Introduction to Statistics. Describing Data:Tables and Graphs. Describing Data: SummaryStatistics. Basic Probability. ProbabilityDistributions. Sampling and SamplingDistributions. Estimation and ConfidenceIntervals. Tests of Hypotheses: One-SampleTests. Tests of Hypotheses: Two-Sample Tests.Tests of Hypotheses: Contingency andGoodness-of-Fit. Tests of Hypotheses: ANOVAand Tests of Variances. Simple Regression andCorrelation. Multiple Regression. Time-SeriesAnalysis. Appendices. Index.

Catalog no. K10296, March 2010, 496 pp.ISBN: 978-1-4398-0568-8, $79.95

Introduction toFinancialModels forManagementand PlanningJames R. Morris andJohn P. DaleyUniversity of Colorado, Denver, USA

This authoritative text provides graduate-levelinstruction on the development of models forfinancial management and planning. By work-ing through the problems and models in thetext, students learn how computer-based mod-els should be structured to analyze a firm’sinvestment and financing. Emphasizing MonteCarlo simulation, the authors cover modelingproblems related to financial management, firmvaluation, forecasting, and security pricing.While the primary focus is on models related tocorporate financial management, the book alsointroduces students to a variety of models relat-ed to security markets, stock and bond invest-ments, portfolio management, and options.

Features

• Covers all key aspects of financial modeling• Introduces powerful tools for the financial

toolbox and shows how to use them to buildsuccessful models

• Contains extensive exercises throughout the text

• Provides complementary access to the Monte Carlo simulation software @Risk

Solutions manual and PowerPoint slides available for qualifying instructors

Contents

Tools for Financial Planning and Modeling:Financial Analysis. Tools for Financial Planningand Modeling: Simulation. Introduction toForecasting Methods. A Closer Look at theDetails of a Financial Model. Modeling SecurityPrices and Investment Portfolios. OptimizationModels. References. Index.




11

Statistics for Engineering


Modeling andAnalysis ofStochasticSystemsSecond EditionVidyadhar G. KulkarniUniversity of North Carolina,Chapel Hill, USA

This text covers the most important classes ofstochastic processes used in the modeling ofdiverse systems. For each class of stochasticprocess, the author includes its definition, char-acterization, applications, transient and limitingbehavior, first passage times, and cost/rewardmodels. He provides many exercises and offersdownloadable MATLAB®-based programs on hiswebsite.


• A new chapter on diffusion processes thatgives an accessible and non-measure-theoret-ic treatment with applications to finance

• A more streamlined, application-orientedapproach to renewal, regenerative, andMarkov regenerative processes

• Two appendices that collect relevant resultsfrom analysis and differential and differenceequations

Rather than offer special tricks that work in spe-cific problems, this book provides thorough cov-erage of general tools that enable the solutionand analysis of stochastic models. After master-ing the material in the text, students will be well-equipped to build and analyze useful stochasticmodels for various situations.


Contents

Introduction. Discrete-Time Markov Chains:Transient Behavior. DTMCs: First Passage Times.DTMCs: Limiting Behavior. Poisson Processes.Continuous-Time Markov Chains. QueueingModels. Renewal Processes. MarkovRegenerative Processes. Diffusion Processes.Epilogue. Appendices. Answers to SelectedProblems. References. Index.

Catalog no. K10430, January 2010, 563 pp.ISBN: 978-1-4398-0875-7, $99.95

ReliabilityEngineeringand RiskAnalysisA Practical Guide,Second EditionMohammad Modarresand Mark KaminskiyUniversity of Maryland, College Park, USA

Vasiliy KrivtsovFord Motor Company, Dearborn, Michigan, USA

With a focus on reliability analysis, this text is aproven educational tool that provides a practicaland comprehensive overview of reliability andrisk analysis techniques. This second edition fea-tures additional topics, including generalizedrenewal with applications, more detailedBayesian estimation methods, and estimation ofbounds of repairable unit reliability and avail-ability. It also presents elementary risk analysistechniques.

Features

• Provides access to an invaluable Excel-basedtool used in failure prediction

• Details the generalized renewal process inrepairable system analysis

• Reviews estimation of probability bounds ofavailability of repairable systems

• Discusses recent developments in Bayesianreliability estimation

• Presents advanced models for a physics-of-failure approach to lifetime estimation


Contents

Reliability Engineering in Perspective. BasicReliability Mathematics: Review of Probabilityand Statistics. Elements of ComponentReliability. System Reliability Analysis. Reliabilityand Availability of Repairable Components andSystems. Selected Topics in ReliabilityModeling. Selected Topics in Reliability DataAnalysis. Risk Analysis. Appendices. Index.

Catalog no. 9247, January 2010, 471 pp.ISBN: 978-0-8493-9247-4, $99.95




Statistics for Engineering

Coming soon!

Statistical andEconometricMethods forTransportationData AnalysisSecond Edition

Simon P. WashingtonMatthew G. KarlaftisFred L. Mannering

Praise for the First Edition

“It is well done and well organized, and provides goodcoverage of all the essential elements of statistical andeconometric methods and models applied to trans-portation … I suspect it will be the definitive text onstatistics in transportation for some years to come…”

—Technometrics, Nov. 2004

Now in its second edition, this popular bookdescribes tools that are commonly used in trans-portation data analysis. This edition features newchapters on mixed logit models, logistic regres-sion, and ordered probability models. It alsoprovides additional coverage of Bayesian statisti-cal modeling, including Bayesian inference andMCMC methods. Data sets are available atwww.crctextbooks.com to use with the model-ing techniques discussed.

Contents

FUNDAMENTALS: Statistical Inference I:Descriptive Statistics. Statistical Inference II:Interval Estimation, Hypothesis Testing andPopulation Comparisons. CONTINUOUSDEPENDENT VARIABLE MODELS: LinearRegression. Violations of RegressionAssumptions. Simultaneous-Equation Models.Panel Data Analysis. Background andExploration in Time Series. Forecasting in TimeSeries: Autoregressive Integrated MovingAverage (ARIMA) Models and Extensions.Latent Variable Models. Duration Models.COUNT AND DISCRETE DEPENDENT VARIABLEMODELS: Count Data Models. LogisticRegression. Discrete Outcome Models. OrderedProbability Models. Discrete/ContinuousModels. OTHER STATISTICAL METHODS:Random-Parameter Models. Bayesian Models.Appendices. References.

Catalog no. C285X, October 2010, c. 600 pp.ISBN: 978-1-4200-8285-2, $99.95

Coming soon!

TransportationStatistics andMicrosimulationCliff Spiegelman andEun Sug ParkTexas A&M University, CollegeStation, USA

Laurence R. RilettUniversity of Nebraska, Lincoln, USA

While typical statistics texts are useful, they arenot typically developed with civil engineeringstudents in mind. Based on the authors’ collab-orative educational and research activities overthe past ten years, this textbook focuses on sta-tistics used in the transportation industry.Through examples, the authors explore theissues behind many of the most popular tech-niques.

Features

• Introduces important statistical techniquesthat are frequently used in transportationengineering

• Includes practical examples that highlight the issues behind the different techniquescommonly used in the profession today

• Offers homework problems at the end ofmost chapters

• Contains computer code so students canlearn how to solve problems using software,such as JMP and MATLAB®

Contents

Overview: The Role of Statistics inTransportation Engineering. Graphical Methodsfor Displaying Data. Numerical SummaryMeasures. Probability and Random Variables.Common Probability Distributions. SamplingDistributions. Inferences: Hypothesis Testingand Interval Estimation. Other InferentialProcedures: ANOVA and Distribution-Free Tests.Inferences Concerning Categorical Data. LinearRegression. Regression Models for Count Data.Experimental Design. Cross-Validation,Jackknife, and Bootstrap Methods for ObtainingStandard Errors. Bayesian Approaches toTransportation Data Analysis. Microsimulation.Appendix.

Catalog no. K10032, October 2010, c. 356 pp.ISBN: 978-1-4398-0023-2, $59.95



13

Statistics for the Social Sciences


MultivariableModeling andMultivariateAnalysis for theBehavioralSciencesBrian S. EverittKing’s College, University ofLondon, UK

“… The first two chapters give a magnificent intro-duction before approaching the modeling issues. …among the best I have ever seen in books on multi-variate methods. … He also goes well beyond thetypical graphs showing how to explore real insightsof the data. … Putting the R code in an appendixand on the website is an excellent choice. … I’ll behappy to recommend this book to students andresearchers.”

—International Statistical Review, 2010

Features

• Presents an accessible introduction to intermediate statistics for behavioral sciencestudents

• Contains a large number of real data setsarising from actual problems, including cognitive behavioral therapy, crime rates, and drug usage

• Separates mathematical details from themain body of the text

• Removes the burden of performing necessarycalculations by encouraging the use of R andproviding the code online

• Includes many real-world examples, graphs,and exercises

• Provides solutions to the problems as well asall R code and data sets for the examples onwww.crctextbooks.com

Contents

Data, Measurement, and Models. Looking atData. Simple Linear and Locally WeightedRegression. Multiple Linear Regression. TheEquivalence of Analysis of Variance and MultipleLinear Regression, and An Introduction to theGeneralized Linear Model. Logistic Regression.Survival Analysis. Linear Mixed Models forLongitudinal Data. Multivariate Data andMultivariate Analysis. Principal ComponentsAnalysis. Factor Analysis. Cluster Analysis.Grouped Multivariate Data. References.Appendix. Index.

Catalog no. K10396, January 2010, 320 pp.Soft Cover, ISBN: 978-1-4398-0769-9, $69.95

Foundations ofFactor AnalysisSecond EditionStanley A MulaikGeorgia Institute of Technology,Atlanta, USA

Presenting the mathematics only as needed tounderstand the derivation of an equation or pro-cedure, this textbook prepares students for latercourses on structural equation modeling. Itenables them to choose the proper factor ana-lytic procedure, make modifications to the pro-cedure, and produce new results.


• A new chapter on the multivariate normaldistribution, its general properties, and theconcept of maximum-likelihood estimation

• More complete coverage of descriptive factoranalysis and doublet factor analysis

• A rewritten chapter on analytic oblique rotation that focuses on the gradient projection algorithm and its applications

• Discussions on the developments of factorscore indeterminacy

• A revised chapter on confirmatory factoranalysis that addresses philosophy of scienceissues, model specification and identification,parameter estimation, and algorithm derivation

Contents

Introduction. Mathematical Foundations forFactor Analysis. Composite Variables and LinearTransformations. Multiple and PartialCorrelations. Multivariate Normal Distribution.Fundamental Equations of Factor Analysis.Methods of Factor Extraction. Common-FactorAnalysis. Other Models of Factor Analysis.Factor Rotation. Orthogonal Analytic Rotation.Oblique Analytic Rotation. Factor Scores andFactor Indeterminacy. Factorial Invariance.Confirmatory-Factor Analysis. References.Indices.

Catalog no. K10005, January 2010, 548 pp.ISBN: 978-1-4200-9961-4, $79.95



14

Biostatistics Environmental Statistics


Coming soon!

Exercises andSolutions in BiostatisticalTheoryLawrence L. KupperUniversity of North Carolina,Chapel Hill, USA

Sean M. O’BrienDuke Clinical Research Institute,Durham, North Carolina, USA

Brian H. NeelonUniversity of North Carolina, Chapel Hill, USA

This self-contained resource offers an unusualcollection of problems and solutions that illus-trate theoretical concepts essential to under-standing the underlying principles of the field ofbiostatistics. Each chapter begins with a reviewof basic tools and concepts that aid in the solu-tion of the problems encountered in that chap-ter. Exercises and solutions are provided at endof each chapter. The material illustrated extendsfrom the basic elements of probability toadvanced multiparameter maximum likelihood-based methods for estimation and hypothesistesting. The authors include highly practicalproblems based on real-life applications takenpredominantly from the health sciences.

Features

• Serves as a supplemental source of a widevariety of exercises for advanced under-graduate and graduate students in statisticaltheory courses

• Gives students an understanding of theunderlying principles of biostatistics

• Includes detailed solutions to every exercise,explaining the key principles in depth

• Contains problems drawn from real-life applications in the health sciences

Contents

Basic Probability Theory. Univariate DistributionTheory. Multivariate Distribution Theory.Estimation Theory. Hypothesis Testing Theory.Appendices. Index.

Catalog no. C7222, October 2010, c. 428 pp.Soft Cover, ISBN: 978-1-58488-722-5, $49.95

Environmentaland EcologicalStatistics with RSong S. QianDuke University, Durham, North Carolina, USA

Based on courses taught by the author at DukeUniversity, this text connects applied statistics tothe environmental and ecological fields. It fol-lows the general approach to solving a statisticalmodeling problem, covering model specifica-tion, parameter estimation, and model evalua-tion. The text explains how to conduct dataanalysis, discusses simulation for model check-ing, and presents multilevel regression models.The author uses many examples to illustrate thestatistical models and presents R implementa-tions of the models. By guiding studentsthrough the processes of scientific problem solv-ing and statistical model development, this bookeases the transition from scientific hypothesis tostatistical model.

Features

• Describes each type of statistical modelthrough examples

• Explains how to conduct data analysis

• Discusses simulation for model checking, an important aspect of model developmentand assessment

• Presents multilevel regression models, such as multilevel ANOVA, multilevel linear regression, and generalized multilevel

• Shows students how the methods can beimplemented using R

• Offers the data sets and R scripts used in thebook along with exercises and solutions onhttp://www.duke.edu/~song/eeswithr.htm

Contents

Basic Concepts. Statistical Modeling. AdvancedStatistical Modeling. References. Index.

Catalog no. C6206, January 2010, 440 pp.Soft Cover, ISBN: 978-1-4200-6206-9, $79.95



15

Statistical Genetics


Statistics in Human Genetics andMolecular BiologyCavan ReillyUniversity of Minnesota, Minneapolis, USA

Selected Contents

Basic Molecular Biology for StatisticalGenetics and Genomics

Basics of Likelihood-Based StatisticsMarkers and Physical Mapping Basic Linkage Analysis Extensions of the Basic Model for ParametricLinkage

Nonparametric Linkage and AssociationAnalysis

Sequence Alignment Significance of Alignments and Alignment in Practice

Hidden Markov Models Feature Recognition in Biopolymers Multiple Alignment and Sequence Feature Discovery

Statistical GenomicsFunctional genomics The technology Spotted cDNA arrays Oligonucleotide arrays NormalizationDetecting Differential Expression Multiple testing and the false discovery rate Significance analysis for microarrays Model based empirical Bayes approach A case study: normalization and differential

detectionCluster Analysis in Genomics Some approaches to cluster analysis Determining the number of clusters BiclusteringClassification in Genomics


Focusing on the roles of different segments ofDNA, this textbook provides students with a basicunderstanding of problems arising in the analysisof genetics and genomics. It presents statisticalapplications in genetic mapping, DNA/proteinsequence alignment, and analyses of geneexpression data from microarray experiments.Ideal for graduate students in statistics, biostatis-tics, computer science, and related fields inapplied mathematics, the text introduces adiverse set of problems and a number ofapproaches that have been used to address theseproblems. It discusses basic molecular biologyand likelihood-based statistics, along with physi-cal mapping, markers, linkage analysis, paramet-ric and nonparametric linkage, sequence align-ment, and feature recognition. The text illustratesthe use of methods that are widespread amongresearchers who analyze genomic data, such ashidden Markov models and the extreme valuedistribution. It also covers differential geneexpression detection as well as classification andcluster analysis using gene expression data sets.

Features

• Provides classroom-proven material for teaching a variety of statistical methods usedto solve problems in genetics and genomics

• Focuses on genetic mapping, DNA/proteinsequence alignment, and analyses of geneexpression data from microarray experiments

• Presents popular methods, such as hiddenMarkov models, for analyzing genomic data

• Contains a substantial breadth of material onmicroarrays

• Describes some Bayesian approaches for solving problems

• Includes many worked examples and end-of-chapter exercises




Statistical Theory and Methods

New!

Design of ExperimentsAn Introduction Based on Linear ModelsMax MorrisIowa State University, Ames, USA

Selected Contents

Linear Statistical Models

Completely Randomized Designs

Randomized Complete Blocks and Related Designs

Latin Squares and Related Designs

Some Data Analysis for CRDs andOrthogonally Blocked Designs

Balanced Incomplete Block Designs

Random Block Effects

Factorial Treatment Structure

Split-Plot Designs

Two-Level Factorial Experiments: Basics

Two-Level Factorial Experiments: Blocking

Two-Level Factorial Experiments: Fractional Factorials

Factorial Group Screening Experiments

Example: semiconductors and simulation

Factorial structure of group screening designs

Group screening design considerations

Regression Experiments: First-OrderPolynomial Models

Regression Experiments: Second-OrderPolynomial Models

Introduction to Optimal Design

Optimal design fundamentals

Optimality criteria

Algorithms

Exercises appear at the end of each chapter.


Offering deep insight into the connectionsbetween design choice and the resulting statisti-cal analysis, this graduate-level text explores howexperiments are designed using the language oflinear statistical models. It presents an organizedframework for understanding the statisticalaspects of experimental design as a whole withinthe structure provided by general linear models,rather than as a collection of seemingly unrelatedsolutions to unique problems.The core material covers a review of linear statis-tical models, completely randomized designs,randomized complete blocks designs, Latinsquares, analysis of data from orthogonallyblocked designs, balanced incomplete blockdesigns, random block effects, split-plot designs,and two-level factorial experiments. The remain-der of the text discusses factorial group screeningexperiments, regression model design, and anintroduction to optimal design.This textbook enables students to fully appreciatethe fundamental concepts and techniques ofexperimental design as well as the real-worldvalue of design. It gives them a profound under-standing of how design selection affects the infor-mation obtained in an experiment.

Features

• Discusses the explicit relationship betweenexperimental design and the quality of dataanalysis

• Presents the fundamental concepts and techniques of experimental design

• Describes specific forms or classes of experimental designs

• Contains an introduction to design for regression models

• Performs calculations using R, with commandsprovided in an appendix

• Incorporates actual experiments drawn fromthe scientific and technical literature

• Includes many end-of-chapter exercises


Catalog no. C9233, July 2010, 370 pp.ISBN: 978-1-58488-923-6, $89.95


17



New!

Design and Analysis of Experimentswith SASJohn LawsonBrigham Young University, Provo, Utah, USA

Selected Contents

Completely Randomized Designs with One Factor

Factorial Designs

Randomized Block Designs

Designs to Study Variances

Fractional Factorial Designs

Incomplete and Confounded Block Designs

Split-Plot Designs

Crossover and Repeated Measures Designs

Response Surface Designs

Mixture Experiments

Introduction

Models and Designs for Mixture Experiments

Creating Mixture Designs in SAS

Analysis of Mixture Experiment

Constrained Mixture Experiments

Blocking Mixture Experiments

Mixture Experiments with Process Variables

Mixture Experiments in Split Plot Arrangements

Robust Parameter Design Experiments

Noise Sources of Functional Variation

Product Array Parameter Design Experiments

Analysis of Product Array Experiments

Single Array Parameter Design Experiments

Joint Modeling of Mean and Dispersion Effects

Experimental Strategies for IncreasingKnowledge

Sequential Experimentation

One-Step Screening and Optimization

Evolutionary Operation

Exercises appear at the end of each chapter.


A culmination of the author’s many years of con-sulting and teaching, this textbook covers bothclassical ideas in experimental design and the latestresearch topics. It clearly discusses the objectives ofa research project that lead to an appropriatedesign choice, the practical aspects of creating adesign and performing experiments, and the inter-pretation of the results of computer data analysis. Drawing on a variety of application areas, the bookpresents numerous examples of experiments andexercises that enable students to perform their ownexperiments. Harnessing the capabilities of SAS 9.2,it includes examples of SAS data step programmingand IML, along with procedures from SAS Stat, SASQC, and SAS OR. The author discusses how thesample size, the assignment of experimental unitsto combinations of treatment factor levels (errorcontrol), and the selection of treatment factor com-binations (treatment design) affect the resultingvariance and bias of estimates as well as the validityof conclusions.

Features

• Emphasizes the connection between theexperimental units, the way treatments arerandomized to experimental units, and theproper error term for an analysis of data

• Uses SAS 9.2 throughout to illustrate the construction of experimental designs andanalysis of data

• Shows how to display experimental resultsgraphically using SAS 9.2 ods graphics

• Provides uniform coverage on experimentaldesigns and design concepts that are mostcommonly used in practice

• Presents many applications from the pharmaceutical, agricultural, industrial chemicals, and machinery industries

• Includes exercises at the end of every chapter• Offers all the SAS code for examples, data for

exercises, PowerPoint slides, and more athttp://lawson.mooo.com

Catalog no. C6060, May 2010, 596 pp.ISBN: 978-1-4200-6060-7, $99.95




New!

Bayesian Ideas and Data AnalysisAn Introduction for Scientists and StatisticiansRonald Christensen, Wesley O. Johnson, Adam J. Branscum, andTimothy E. Hanson

Selected Contents

Fundamental Ideas I

Integration versus Simulation

Fundamental Ideas II

Comparing Populations

Simulations Generating Random Samples Traditional Monte Carlo MethodsBasics of Markov Chain TheoryMarkov Chain Monte Carlo

Basic Concepts of Regression

Binomial Regression

Linear Regression

Correlated Data

Count Data

Time to Event Data

Time to Event Regression

Binary Diagnostic Tests Basic Ideas One Test, One Population Two Tests, Two Populations Prevalence DistributionsIllustrations: Coronary Artery Disease

Nonparametric ModelsFlexible Density ShapesFlexible Regression Functions Proportional Hazards ModelingIllustrations: Galaxy Data


Emphasizing the use of WinBUGS and R to ana-lyze real data, this text presents statistical tools toaddress scientific questions. It highlights founda-tional issues in statistics, the importance of mak-ing accurate predictions, and the need for scien-tists and statisticians to collaborate in analyzingdata. The WinBUGS code provided offers a con-venient platform to model and analyze a widerange of data. The first five chapters of the book contain corematerial that spans basic Bayesian ideas, calcula-tions, and inference. The text then covers MonteCarlo methods. After discussing linear structuresin regression, it presents binomial regression, nor-mal regression, analysis of variance, and Poissonregression, before extending these methods tohandle correlated data. The authors also examinesurvival analysis and binary diagnostic testing. Acomplementary chapter on diagnostic testing forcontinuous outcomes is available on the book’swebsite. The last chapter on nonparametric infer-ence explores density estimation and flexibleregression modeling of mean functions.

Features

• Offers flexible options for teaching a variety of courses

• Covers a large number of statistical models

• Emphasizes the elicitation of reasonable priorinformation

• Explores numerical approximations via simulation

• Uses WinBUGS and R for computational problems

• Reviews basic concepts of matrix algebra and probability

• Includes numerous exercises and real-worldexamples throughout

• Provides data, programming code, and othermaterials at www.stat.unm.edu/~fletcher

Catalog no. K10199, July 2010, 516 pp.ISBN: 978-1-4398-0354-7, $69.95


19



New!

Time SeriesModeling, Computation, and InferenceRaquel PradoUniversity of California, Santa Cruz, USA

Mike WestDuke University, Durham, North Carolina, USA

Selected ContentsTraditional Time Domain ModelsThe Frequency DomainDynamic Linear ModelsGeneral linear model structures Forecast functions and model formsInference in dynamic linear models (DLMs):basic normal theory

Extensions: non-Gaussian and nonlinear models Posterior simulation: MCMC algorithmsState-Space Time-Varying AutoregressiveModels

Time-varying autoregressions (TVAR) anddecompositions

TVAR model specification and posterior inferenceExtensionsSequential Monte Carlo Methods for State-Space Models

Mixture Models in Time SeriesMarkov switching models Multiprocess models Mixtures of general state-space models Case study: detecting fatigue from EEGs Univariate stochastic volatility modelsTopics and Examples in Multiple Time SeriesMultichannel modeling of EEG data Some spectral theory Dynamic lag/lead models Other approachesVector AR and ARMA ModelsMultivariate DLMs and Covariance ModelsTheory of multivariate and matrix normal DLMs Multivariate DLMs and exchangeable time series Learning cross-series covariances Time-varying covariance matricesMultivariate dynamic graphical modelsProblems appear at the end of each chapter.


Focusing on Bayesian approaches and computa-tions using up-to-date simulation-based methodsfor inference, Time Series integrates mainstreamapproaches for time series modeling with signifi-cant recent developments in methodology andapplications of time series analysis. It encompass-es a graduate-level account of Bayesian timeseries modeling and analysis, a broad range ofreferences to state-of-the-art approaches to uni-variate and multivariate time series analysis, andemerging topics at research frontiers.Along with core models and methods, this textoffers students sophisticated tools for analyzingchallenging time series problems. It also demon-strates the growth of time series analysis into newapplication areas.

Features

• Covers the major areas of modern time series models and theory, including time and spectral domain and univariate and multivariate time series methods

• Presents analyses of actual time series data in numerous examples and case studies toillustrate the flexibility and practical impact of the models and methods

• Emphasizes model-based, computationallyintensive analysis of structured time series

• Discusses recent techniques for modeling timeseries data, such as dynamic graphical models,SMC methods, and nonlinear/non-Gaussiandynamic models

• Includes a collection of end-of-chapter exercises

• Offers many of the data sets, R and MATLAB®

code, and other material on the authors’ websites

Catalog no. C9336, May 2010, 368 pp.ISBN: 978-1-4200-9336-0, $89.95




New!

Introduction toGeneral andGeneralizedLinear ModelsHenrik Madsen andPoul ThyregodTechnical University, Lyngby, Denmark

Since the mathematics behind generalized linearmodels is often difficult to follow while themathematics behind general linear models iswell understood, Introduction to General andGeneralized Linear Models describes themethodology behind both models in a parallelsetup. After introducing a likelihood frameworksufficient to cover both approaches, the authorspresent general linear models, including analysisof covariance, before moving on to more com-plicated generalized linear models using thesame likelihood-based example. Numerous simulated and real-world examples,implemented using R and SAS, illustrate themethods discussed. The text also provides exer-cises to develop further understanding.

Features

• Provides a unified likelihood-based framework for general and generalizedlinear models

• Covers mixed-effects and hierarchical models

• Contains a number of simulated andreal-world examples implemented usingR and SAS

• Includes exercises to help develop anunderstanding of the methods

Contents

The Likelihood Principle. General LinearModels. Generalized Linear Models. MixedEffects Models. Hierarchical Models. SomeProbability Distributions.

Catalog no. C9155, September 2010, c. 318 pp.ISBN: 978-1-4200-9155-7, $79.95

AppliedStatisticalInference withMINITAB®

Sally A. LesikCentral Connecticut StateUniversity, New Britain, USA

Through clear, step-by-step mathematical calcu-lations, this text enables students to gain a solidunderstanding of how to apply statistical tech-niques in practice using MINITAB. It focuses onthe concepts of confidence intervals, hypothesistesting, validating model assumptions, andpower analysis. Taking an introductory, practicalapproach to statistics, the text establishes thefoundation for students to build on work inmore advanced inferential statistics. Ideal for stu-dents in the social sciences, the book is designedfor a course in applied statistics and researchmethods.

Features

• Presents the techniques and methods ofapplied inference in a step-by-step manner

• Provides a complete integration of MINITABthroughout the text

• Includes fully worked out examples so students can easily follow the calculations

• Offers data sets and a trial version ofMINITAB on accompanying CD-ROMs

• Contains a set of homework problems at theend of each chapter


Contents

Introduction. Graphing Variables. DescriptiveRepresentations of Data and Random Variables.Basic Statistical Inference. Simple LinearRegression. More on Simple Linear Regression.Multiple Regression Analysis. More on MultipleRegression. ANOVA. Other Topics. Index.




21



LogisticRegressionModelsJoseph M. HilbeJet Propulsion Laboratory,California Institute of Technology,Pasadena, and Arizona StateUniversity, Tempe, USA

Based on a successful course taught by theauthor, this text presents an overview of the fullrange of logistic models. It illustrates how toapply the models to various types of data. Statais used to develop, evaluate, and display mostmodels while R code is given at the end of mostchapters. Example data sets are accessible onlinein Stata, R, Excel, SAS, SPSS, and Limdep for-mats.



Design andAnalysis ofExperimentsClassical andRegressionApproaches with SASLeonard C. OnyiahSt. Cloud State University,Minnesota, USA

Capitalizing on the availability of cutting-edgesoftware, the author uses both manual methodsand SAS programs to carry out analyses. He pro-vides examples to illustrate numerous designs.The text includes the full SAS code and outputsas well as end-of-chapter exercises to encouragehands-on SAS programming experience.



An Introductionto GeneralizedLinear ModelsThird EditionAnnette J. DobsonUniversity of Queensland,Herston, Australia

Adrian G. BarnettQueensland University ofTechnology, Kelvin Grove,Australia

“… explanations are fundamentally sound andaimed well at an upper-level undergrad or earlygraduate student in a statistics-related field. This isa very worthwhile book: a good class text … .”

—Biometrics

Updated with Stata, R, and WinBUGS code aswell as three new chapters on Bayesian analysis,this new edition provides a cohesive frameworkfor statistical modeling. Data sets and solutionsto the exercises are offered online.


A Primer onLinear ModelsJohn F. MonahanNorth Carolina State University,Raleigh, USA

“… well written … would serve well as the textbookfor an introductory course in linear models … .”—Justine Shults, Journal of Biopharmaceutical Statistics,

2009, Issue 3

Employing non-full-rank design matricesthroughout, this text provides a concise yet solidfoundation for understanding basic linear mod-els. It presents proofs and discussions from bothalgebraic and geometric viewpoints andincludes exercises of varying levels of difficulty atthe end of each chapter.






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Bayesian Methodsfor Data Analysis

Third EditionBradley P. Carlin

University of Minnesota, Minneapolis, USA

Thomas A. LouisJohns Hopkins Bloomberg School of

Public Health, Baltimore, Maryland, USA

“… the third edition has … newadditions of BUGS and R codethroughout the book and reorgan-ization or expansion of severalchapters. … I am glad to see thatthe software code and exampleshave also been made available onthe website http://www.biostat.umn.edu/~brad/dataCL3.html sothat users can truly enjoy easyaccess and convenience in repro-ducing the computations in thebook. … a very worthy edition andI highly recommend it as a text-book …”

—Journal of Applied Statistics, Vol. 37, No. 4, April 2010


Catalog no. C6 978, 2008552 pp.

ISBN: 978-1-58488-697-6 $69.95

NonparametricStatisticalInferenceFifth Edition

Jean Dickinson Gibbons andSubhabrata Chakraborti

University of Alabama, Tuscaloosa, USA

• The source for learning aboutnonparametric statistics—covers the mostcommonly used non-parametric procedures

• At least 50 percent of thematerial revised, with moreproblems and examples

• Carefully states assumptionsand develops the theorybehind procedures

• Realistic research examplesfrom the social, behavioral,and life sciences

• Many tables needed for finding P values and obtaining confidence intervalestimates of parameters

Catalog no. C7619, July 2010650 pp.

ISBN: 978-1-4200-7761-2$99.95

StochasticProcesses

An Introduction, Second Edition

Peter W. Jones and Peter SmithKeele University, Staffordshire, UK

• Uses Mathematica® and R to illustrate the modeling and analysis of randomexperiments using the theory of probability

• Over 50 worked examplesand more than 200 end-of-chapter problems

• Describes applications ofprobability to modelingproblems in engineering,medicine, and biology

• Book website includes theMathematica and R programsas well as a solutions manualfor qualify ing instructors

Catalog no. K10004, January 2010 232 pp., Soft Cover

ISBN: 978-1-4200-9960-7$79.95




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