Contents Statistics for the Biological Sciences ..................3 Probability Theory and Applications..................5 Computational Statistics ..................................6 Statistics for Business and Finance ....................8 Page 5
Statistics for Engineering ................................11
Page 15
Statistics for the Social Sciences ......................13 Biostatistics ....................................................14 Environmental Statistics ..................................14 Statistical Genetics ..........................................15 Statistical Theory and Methods ......................16 Page 10
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MBTST10 MC6_7/2810gtr
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Statistics for the Biological Sciences
New!
Introduction to Statistical Data Analysis for the Life Sciences Claus Thorn Ekstrom and Helle Sorensen University of Copenhagen, Denmark
Developed from the authors’ courses at the University of Copenhagen, this textbook covers all the usual material but goes further than other texts. The authors imbue students with the ability to model and analyze data early in the text and then gradually fill in the blanks with needed probability and statistics theory. While the main text can be used with any statistical software, the authors encourage a reliance on R. They provide a short tutorial for students new to the software and include R commands and output at the end of each chapter. Ultimately, students come away with a computational toolbox that enables them to perform actual analysis for real data sets as well as the confidence and skills to undertake more sophisticated analyses as their careers progress.
Features
Selected Contents Description of Samples and Populations Linear Regression Comparison of Groups The Normal Distribution Statistical Models, Estimation, and Confidence Intervals Hypothesis Tests Model Validation and Prediction Linear Normal Models Probabilities The Binomial Distribution The independent trials model The binomial distribution
• Includes numerous exercises, half of which can be done by hand
Estimation, confidence intervals, and hypothesis tests
• Contains ten case exercises that encourage students to apply their knowledge to larger data sets and learn more about approaches specific to the life sciences
Analysis of Count Data The chi-square test for goodness-of-fit
• Offers a tutorial for students new to R
2 × 2 contingency table
• Provides data sets used in the text on a supporting website
Two-sided contingency tables
• Emphasizes both data analysis and the mathematics underlying classical statistical analysis
Odds and odds ratios
• Covers modeling aspects of statistical analysis with added focus on biological interpretations
Estimation and confidence intervals
• Explores applications of statistical software in analyzing real-world problems and data sets
Model validation and prediction
Differences between proportions
Logistic Regression Logistic regression models Hypothesis tests Case Exercises
Solutions manual available for qualifying instructors
R commands and output and exercises appear at the end of each chapter. Databases & Code also available online.
Catalog no. K11221, August 2010, 427 pp. Soft Cover, ISBN: 978-1-4398-2555-6, $69.95
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3
Statistics for the Biological Sciences Introductory Statistics An Introduction to Statistical Inference and Its Applications with R
Applied Stochastic Modelling Second Edition
Michael W. Trosset
Byron J.T. Morgan
Indiana University, Bloomington, USA
University of Kent, UK
Emphasizing concepts rather than recipes, this text provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses.
Features • Explains how statistical methods are used for data analysis • Uses the elementary functions of R to perform the individual steps of statistical procedures • Includes amusing anecdotes and trivia, such as Ambrose Bierce’s definition of insurance • Introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, ANOVA, and regression • Presents many applications along with supporting data sets • Contains exercises at the end of each chapter • Offers the R code and data sets available for download online Solutions manual available for qualifying instructors
Contents Experiments. Mathematical Preliminaries. Probability. Discrete Random Variables. Continuous Random Variables. Quantifying Population Attributes. Data. Lots of Data. Inference. 1-Sample Location Problems. 2Sample Location Problems. The Analysis of Variance. Goodness-of-Fit. Association. Simple Linear Regression. Simulation-Based Inference. R: A Statistical Programming Language. Index.
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 recommend it. …” —Tim Auton, Journal of the Royal Statistical Society
Continuing in the tradition of its bestselling predecessor, this textbook remains an excellent resource for teaching students how to fit stochastic models to data. Although the book can be used without reference to computational programs, the author provides the option of using powerful computational tools for stochastic modeling. All of the data sets and MATLAB® and R programs found in the text as well as lecture slides and other ancillary material are available 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. General Families of Models. Index of Data Sets. Index of MATLAB Programs. Appendices. Solutions and Comments for Selected Exercises. Bibliography. Index. Catalog no. C6668, 2009, 368 pp., Soft Cover ISBN: 978-1-58488-666-2, $60.95
Catalog no. C9470, 2009, 496 pp. ISBN: 978-1-58488-947-2, $79.95
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Probability Theory and Applications
Introduction to Probability with Mathematica® Second Edition Kevin J. Hastings Knox College, Galesburg, Illinois, USA
“Introduction to Probability with Mathematica adds computational exercises to the traditional undergraduate probability curriculum without cutting out theory. … a good textbook for a class with a strong emphasis on hands-on experience with probability. …” —MAA Reviews, Dec. 2009
Updated to conform to Mathematica® 7.0, this second edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems.
New to the Second Edition • Expanded section on Markov chains that includes a study of absorbing chains • New sections on order statistics, transformations of multivariate normal random variables, and Brownian motion • More example data of the normal distribution • More attention on conditional expectation, which has become significant in financial mathematics • 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 from Mathematica 7.0 • Updated Mathematica notebooks on the CD-ROM This text takes an interactive approach that complements today’s highly technological teaching environment. The accompanying CD-ROM offers instructors the option of creating class notes, demonstrations, and projects. Solutions manual available for qualifying instructors
Catalog no. C7938, January 2010, 465 pp. ISBN: 978-1-4200-7938-8, $89.95
Contents Discrete Probability The Cast of Characters Properties of Probability Simulation Random Sampling Conditional Probability Independence Discrete Distributions Discrete Random Variables, Distributions, and Expectations Bernoulli and Binomial Random Variables Geometric and Negative Binomial Random Variables Poisson Distribution Joint, Marginal, and Conditional Distributions More on Expectation Continuous Probability From the Finite to the (Very) Infinite Continuous Random Variables and Distributions Continuous Expectation Continuous Distributions The Normal Distribution Bivariate Normal Distribution New Random Variables from Old Order Statistics Gamma Distributions Chi-Square, Student’s t, and F-Distributions Transformations of Normal Random Variables Asymptotic Theory Strong and Weak Laws of Large Numbers Central Limit Theorem Stochastic Processes and Applications Markov Chains Poisson Processes Queues Brownian Motion Financial Mathematics Appendix References Index
For more information and complete contents, visit www.crctextbooks.com
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Probability Theory and Applications
Computational Statistics New!
Probability and Statistics with R Maria Dolores Ugarte and Ana F. Militino Public University of Navarre, Pamplona, Spain
Alan T. Arnholt Appalachian State University, Boone, North Carolina, USA
“… Detailed executable codes and codes to generate the figures [in R and S-PLUS] are available at http://www1.appstate.edu/~arnholta/PASWR/front .htm … Students or self-learners can learn some basic techniques for using R in statistical analysis on their way to learning about various topics in probability and statistics. … wonderful stand-alone textbook … .” —Technometrics, May 2009, Vol. 51, No. 2 Solutions manual available for qualifying instructors
Catalog no. C8911, 2008, 728 pp. ISBN: 978-1-58488-891-8, $89.95
Graphics for Statistics and Data Analysis with R Kevin J. Keen University Northern British Columbia, Prince George, Canada
Showing students how to use graphics to display or summarize data, this text presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation. Downloadable R code and data for the figures in the text are available at www.graphicsforstatistics.com Catalog no. C0756, April 2010, 489 pp. ISBN: 978-1-58488-087-5, $69.95
Interactive Graphics for Data Analysis Introduction to Probability with R Kenneth P. Baclawski Northeastern University, Boston, Massachusetts, USA
“… The book is clearly written and very well-organized and it stems in part from a popular course at MIT taught by the late Gian-Carlo Rota … . The book goes well beyond the MIT course in making extensive use of computation and R. … It would serve as an exemplary test for the first semester of a two-semester course on probability and statistics.” —Journal of Statistical Software, April 2009 Solutions manual available for qualifying instructors
Catalog no. C6521, 2008, 384 pp. ISBN: 978-1-4200-6521-3, $89.95
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Principles and Examples Martin Theus Munich, Germany
Simon Urbanek Madison, New Jersey, USA
This full-color text discusses EDA and how interactive graphical methods can help students gain insights as well as generate new questions and hypotheses from data sets. It uses Mondrian software and R. The authors provide exercises at the end of each chapter and offer course suggestions, slides, and extra code on the book’s website. Times Higher Education (Dec. 2009) called the text “a brief, powerful book with excellent and clear graphics.” Catalog no. C5947, 2009, 290 pp. ISBN: 978-1-58488-594-8, $79.95
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Computational Statistics
Computational Statistics An Introduction to R Günther Sawitzki StatLab, Heidelberg, Germany
“… a fresh perspective on teaching statistics. … The book introduces its topics and the corresponding methodologies well. … the book is well put together and quite enjoyable for its purpose of serving a small course on computational statistics.” —Journal of Statistical Software, Dec. 2009
“… it is the integration of interesting examples and associated R code that make the text a pleasure to read and work through. The examples are neither overly trivial … nor excessively complicated, and the R code is similarly accessible without being either too simple or complex. … It could also be useful as a supplementary text for upper-level undergraduate or graduate 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 to tackle statistical problems • Focuses on the underlying concepts of statistics • Covers distribution diagnostics, Monte Carlo tests, ANOVA, general linear models, distribution-free tests, and dimension reduction • Includes numerous exercises and real-world examples 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 material at http://sintro.r-forge.r-project.org/
Catalog no. C6782, 2009, 264 pp. SBN: 978-1-4200-8678-2, $79.95
Contents Introduction Basic Data Analysis R Programming Conventions Generation of Random Numbers and Patterns Case Study: Distribution Diagnostics Moments and Quantiles Regression General Regression Model Linear Model Variance Decomposition and Analysis of Variance Simultaneous Inference Beyond Linear Regression Comparisons Shift/Scale Families and Stochastic Order QQ Plot, PP Plot, and Comparison of Distributions Tests for Shift Alternatives A Road Map Power and Confidence Qualitative Features of Distributions Dimensions 1, 2, 3, …, ∞ Dimensions Selections Projections Sections, Conditional Distributions, and Coplots Transformations and Dimension Reduction Higher Dimensions High Dimensions Appendix: R as a Programming Language and Environment 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
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7
Statistics for Business and Finance
Stochastic Financial Models Douglas Kennedy Trinity College, Cambridge, UK
“This book is a superb beginning-level text for senior undergraduate/graduate mathematicians, which is based on lectures delivered by its author to many generations of appreciative Cambridge mathematicians. Many of my own Ph.D. and masters students have taken Dr. Kennedy’s course to uniformly good reviews; this readable book will make its material available to a worldwide audience. … the book contains 40 pages of fully worked out solutions … .” —M.A.H. Dempster, Centre for Financial Research, Statistical Laboratory, University of Cambridge, UK
Developed from the esteemed author’s advanced undergraduate and graduate courses at the University of Cambridge, this text provides a hands-on, sound introduction to mathematical finance. The author first presents the classical topics of utility and the mean-variance approach to portfolio choice. Focusing on derivative pricing, he then covers the binomial model, the general discrete-time model, Brownian motion, the Black–Scholes model and various interest-rate models.
Features • Presents a self-contained treatment of mathematical models in finance by including the 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 with relatively light mathematical content and books with a rigorous, formal approach to stochastic integration and probabilistic ideas
Contents Portfolio Choice Introduction Utility Mean-variance analysis The Binomial Model One-period model Multi-period model A General Discrete-Time Model One-period model Multi-period model Brownian Motion Introduction Hitting-time distributions Girsanov’s theorem Brownian motion as a limit Stochastic calculus The Black–Scholes Model Introduction The Black–Scholes formula Hedging and the Black–Scholes equation Path-dependent claims Dividend-paying assets Interest-Rate Models Introduction Survey of interest-rate models Gaussian random-field model Appendix A: Mathematical Preliminaries Appendix B: Solutions to the Exercises Further Reading References Index Exercises appear at the end of each chapter
Catalog no. C3452, January 2010, 264 pp. ISBN: 978-1-4200-9345-2, $69.95
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Statistics for Business and Finance
Interest Rate Modeling Theory and Practice Lixin Wu University of Science & Technology, Kowloon, Hong Kong
“The book presents in a balanced way both theory and applications of interest rate modeling. …The book can serve as a textbook. It is self-contained in mathematics and presents rigorous justifications for almost all results. …” —Pavel Stoynov, Zentralblatt MATH 1173
Containing many results that are new or exist only in recent research articles, this text portrays the theory of interest rate modeling as a threedimensional object of finance, mathematics, and computation. It introduces all models with financial-economical justifications, develops options along the martingale approach, and handles option evaluations with precise numerical methods. Taking a top-down approach, the author provides students with a clear picture of this important subject by not overwhelming them with too many specific models. The text includes exercises and real-world examples, along with code, tables, and figures accessible on the author’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 intriguing industrial issues, such as volatility and correlation adjustments • Contains exercise sets and a number of examples, with many based on real market data • Includes comments on cutting-edge research, such as volatility-smile, positive interest-rate models, and convexity adjustment • Offers code, tables, and figures on the author’s website
Selected Contents The Basics of Stochastic Calculus The Martingale Representation Theorem Interest Rates and Bonds The Heath–Jarrow–Morton Model Short-Rate Models and Lattice Implementation The LIBOR Market Model LIBOR Market Instruments The LIBOR Market Model Pricing of Caps and Floors Pricing of Swaptions Specifications of the LIBOR Market Model Monte Carlo Simulation Method Calibration of LIBOR Market Model Implied Cap and Caplet Volatilities Calibrating the LIBOR Market Model to Caps Calibration to Caps, Swaptions, and Input Correlations Calibration Methodologies Sensitivity with Respect to the Input Prices Volatility and Correlation Adjustments Adjustment due to Correlations Adjustment due to Convexity Timing Adjustment Quanto Derivatives Affine Term Structure Models An Exposition with One-Factor Models Analytical Solution of Riccarti Equations Pricing Options on Coupon Bonds Distributional Properties of Square-Root Processes Multi-Factor Models Swaption Pricing under ATSMs
Solutions manual available for qualifying instructors
Catalog no. C0569, 2009, 353 pp. ISBN: 978-1-4200-9056-7, $79.95
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9
Statistics for Business and Finance
Applied Statistics for Business and Economics Robert M. Leekley Illinois Wesleyan University, Bloomington, USA
Designed for a one-semester course, this text offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world. Numerous interesting and important examples reflect real-life situations and calculations can be performed using any standard spreadsheet package. To help with the examples, the author offers both actual and hypothetical databases on his website. After reading the book, students will be able to summarize data in insightful ways using charts, graphs, and summary statistics as well as make inferences from samples.
Features • Highlights the connections among various statistical topics • Encourages the use of spreadsheets to handle raw data sets large enough to be meaningful, helping students gain a greater understanding of real-life applications • Includes answers to odd-numbered problems at the back of the book • Offers databases available for download on http://iwu.edu/~bleekley
Introduction to Financial Models for Management and Planning James R. Morris and John P. Daley University of Colorado, Denver, USA
This authoritative text provides graduate-level instruction on the development of models for financial management and planning. By working through the problems and models in the text, students learn how computer-based models should be structured to analyze a firm’s investment and financing. Emphasizing Monte Carlo simulation, the authors cover modeling problems related to financial management, firm valuation, forecasting, and security pricing. While the primary focus is on models related to corporate financial management, the book also introduces students to a variety of models related to security markets, stock and bond investments, 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 build successful 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 Introduction to Statistics. Describing Data: Tables and Graphs. Describing Data: Summary Statistics. Basic Probability. Probability Distributions. Sampling and Sampling Distributions. Estimation and Confidence Intervals. Tests of Hypotheses: One-Sample Tests. Tests of Hypotheses: Two-Sample Tests. Tests of Hypotheses: Contingency and Goodness-of-Fit. Tests of Hypotheses: ANOVA and Tests of Variances. Simple Regression and Correlation. Multiple Regression. Time-Series Analysis. Appendices. Index.
Contents Tools for Financial Planning and Modeling: Financial Analysis. Tools for Financial Planning and Modeling: Simulation. Introduction to Forecasting Methods. A Closer Look at the Details of a Financial Model. Modeling Security Prices and Investment Portfolios. Optimization Models. References. Index. Catalog no. C0542, 2009, 754 pp. ISBN: 978-1-4200-9054-3, $89.95
Catalog no. K10296, March 2010, 496 pp. ISBN: 978-1-4398-0568-8, $79.95
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Statistics for Engineering Modeling and Analysis of Stochastic Systems Second Edition Vidyadhar G. Kulkarni University of North Carolina, Chapel Hill, USA
This text covers the most important classes of stochastic processes used in the modeling of diverse systems. For each class of stochastic process, the author includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. He provides many exercises and offers downloadable MATLAB®-based programs on his website.
New to the Second Edition • A new chapter on diffusion processes that gives an accessible and non-measure-theoretic treatment with applications to finance • A more streamlined, application-oriented approach to renewal, regenerative, and Markov regenerative processes • Two appendices that collect relevant results from analysis and differential and difference equations Rather than offer special tricks that work in specific problems, this book provides thorough coverage of general tools that enable the solution and analysis of stochastic models. After mastering the material in the text, students will be wellequipped to build and analyze useful stochastic models for various situations. Solutions manual available for qualifying instructors
Contents Introduction. Discrete-Time Markov Chains: Transient Behavior. DTMCs: First Passage Times. DTMCs: Limiting Behavior. Poisson Processes. Continuous-Time Markov Chains. Queueing Models. Renewal Processes. Markov Regenerative Processes. Diffusion Processes. Epilogue. Appendices. Answers to Selected Problems. References. Index.
Reliability Engineering and Risk Analysis A Practical Guide, Second Edition Mohammad Modarres and Mark Kaminskiy University of Maryland, College Park, USA
Vasiliy Krivtsov Ford Motor Company, Dearborn, Michigan, USA
With a focus on reliability analysis, this text is a proven educational tool that provides a practical and comprehensive overview of reliability and risk analysis techniques. This second edition features additional topics, including generalized renewal with applications, more detailed Bayesian estimation methods, and estimation of bounds of repairable unit reliability and availability. It also presents elementary risk analysis techniques.
Features • Provides access to an invaluable Excel-based tool used in failure prediction • Details the generalized renewal process in repairable system analysis • Reviews estimation of probability bounds of availability of repairable systems • Discusses recent developments in Bayesian reliability estimation • Presents advanced models for a physicsof-failure approach to lifetime estimation Solutions manual available for qualifying instructors
Contents Reliability Engineering in Perspective. Basic Reliability Mathematics: Review of Probability and Statistics. Elements of Component Reliability. System Reliability Analysis. Reliability and Availability of Repairable Components and Systems. Selected Topics in Reliability Modeling. Selected Topics in Reliability Data Analysis. Risk Analysis. Appendices. Index. Catalog no. 9247, January 2010, 471 pp. ISBN: 978-0-8493-9247-4, $99.95
Catalog no. K10430, January 2010, 563 pp. ISBN: 978-1-4398-0875-7, $99.95
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Statistics for Engineering Coming soon!
Coming soon!
Statistical and Econometric Methods for Transportation Data Analysis
Transportation Statistics and Microsimulation
Second Edition
Texas A&M University, College Station, USA
Laurence R. Rilett
Simon P. Washington Matthew G. Karlaftis Fred L. Mannering
University of Nebraska, Lincoln, USA
Praise for the First Edition “It is well done and well organized, and provides good coverage of all the essential elements of statistical and econometric methods and models applied to transportation … I suspect it will be the definitive text on statistics in transportation for some years to come…” —Technometrics, Nov. 2004
Now in its second edition, this popular book describes tools that are commonly used in transportation data analysis. This edition features new chapters on mixed logit models, logistic regression, and ordered probability models. It also provides additional coverage of Bayesian statistical modeling, including Bayesian inference and MCMC methods. Data sets are available at www.crctextbooks.com to use with the modeling techniques discussed.
Contents FUNDAMENTALS: Statistical Inference I: Descriptive Statistics. Statistical Inference II: Interval Estimation, Hypothesis Testing and Population Comparisons. CONTINUOUS DEPENDENT VARIABLE MODELS: Linear Regression. Violations of Regression Assumptions. Simultaneous-Equation Models. Panel Data Analysis. Background and Exploration in Time Series. Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions. Latent Variable Models. Duration Models. COUNT AND DISCRETE DEPENDENT VARIABLE MODELS: Count Data Models. Logistic Regression. Discrete Outcome Models. Ordered Probability Models. Discrete/Continuous Models. 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
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Cliff Spiegelman and Eun Sug Park
While typical statistics texts are useful, they are not typically developed with civil engineering students in mind. Based on the authors’ collaborative educational and research activities over the past ten years, this textbook focuses on statistics used in the transportation industry. Through examples, the authors explore the issues behind many of the most popular techniques.
Features • Introduces important statistical techniques that are frequently used in transportation engineering • Includes practical examples that highlight the issues behind the different techniques commonly used in the profession today • Offers homework problems at the end of most chapters • Contains computer code so students can learn how to solve problems using software, such as JMP and MATLAB®
Contents Overview: The Role of Statistics in Transportation Engineering. Graphical Methods for Displaying Data. Numerical Summary Measures. Probability and Random Variables. Common Probability Distributions. Sampling Distributions. Inferences: Hypothesis Testing and Interval Estimation. Other Inferential Procedures: ANOVA and Distribution-Free Tests. Inferences Concerning Categorical Data. Linear Regression. Regression Models for Count Data. Experimental Design. Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors. Bayesian Approaches to Transportation Data Analysis. Microsimulation. Appendix. Catalog no. K10032, October 2010, c. 356 pp. ISBN: 978-1-4398-0023-2, $59.95
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Statistics for the Social Sciences Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences
Foundations of Factor Analysis Second Edition
Brian S. Everitt
Stanley A Mulaik
King’s College, University of London, UK
Georgia Institute of Technology, Atlanta, USA
“… The first two chapters give a magnificent introduction before approaching the modeling issues. … among the best I have ever seen in books on multivariate methods. … He also goes well beyond the typical graphs showing how to explore real insights of the data. … Putting the R code in an appendix and on the website is an excellent choice. … I’ll be happy to recommend this book to students and researchers.”
Presenting the mathematics only as needed to understand the derivation of an equation or procedure, this textbook prepares students for later courses on structural equation modeling. It enables them to choose the proper factor analytic procedure, make modifications to the procedure, and produce new results.
—International Statistical Review, 2010
Features • Presents an accessible introduction to intermediate statistics for behavioral science students • Contains a large number of real data sets arising from actual problems, including cognitive behavioral therapy, crime rates, and drug usage • Separates mathematical details from the main body of the text • Removes the burden of performing necessary calculations by encouraging the use of R and providing the code online • Includes many real-world examples, graphs, and exercises • Provides solutions to the problems as well as all R code and data sets for the examples on www.crctextbooks.com
Contents Data, Measurement, and Models. Looking at Data. Simple Linear and Locally Weighted Regression. Multiple Linear Regression. The Equivalence of Analysis of Variance and Multiple Linear Regression, and An Introduction to the Generalized Linear Model. Logistic Regression. Survival Analysis. Linear Mixed Models for Longitudinal Data. Multivariate Data and Multivariate Analysis. Principal Components Analysis. Factor Analysis. Cluster Analysis. Grouped Multivariate Data. References. Appendix. Index.
New to the Second Edition • A new chapter on the multivariate normal distribution, its general properties, and the concept of maximum-likelihood estimation • More complete coverage of descriptive factor analysis 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 factor score indeterminacy • A revised chapter on confirmatory factor analysis that addresses philosophy of science issues, model specification and identification, parameter estimation, and algorithm derivation
Contents Introduction. Mathematical Foundations for Factor Analysis. Composite Variables and Linear Transformations. Multiple and Partial Correlations. Multivariate Normal Distribution. Fundamental Equations of Factor Analysis. Methods of Factor Extraction. Common-Factor Analysis. Other Models of Factor Analysis. Factor Rotation. Orthogonal Analytic Rotation. Oblique Analytic Rotation. Factor Scores and Factor Indeterminacy. Factorial Invariance. Confirmatory-Factor Analysis. References. Indices. Catalog no. K10005, January 2010, 548 pp. ISBN: 978-1-4200-9961-4, $79.95
Catalog no. K10396, January 2010, 320 pp. Soft Cover, ISBN: 978-1-4398-0769-9, $69.95
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Biostatistics
Environmental Statistics
Coming soon!
Exercises and Solutions in Biostatistical Theory Lawrence L. Kupper University of North Carolina, Chapel Hill, USA
Sean M. O’Brien Duke Clinical Research Institute, Durham, North Carolina, USA
Brian H. Neelon University of North Carolina, Chapel Hill, USA
This self-contained resource offers an unusual collection of problems and solutions that illustrate theoretical concepts essential to understanding the underlying principles of the field of biostatistics. Each chapter begins with a review of basic tools and concepts that aid in the solution of the problems encountered in that chapter. Exercises and solutions are provided at end of each chapter. The material illustrated extends from the basic elements of probability to advanced multiparameter maximum likelihoodbased methods for estimation and hypothesis testing. The authors include highly practical problems based on real-life applications taken predominantly from the health sciences.
Environmental and Ecological Statistics with R Song S. Qian Duke University, Durham, North Carolina, USA
Based on courses taught by the author at Duke University, this text connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The text explains how to conduct data analysis, discusses simulation for model checking, and presents multilevel regression models. The author uses many examples to illustrate the statistical models and presents R implementations of the models. By guiding students through the processes of scientific problem solving and statistical model development, this book eases the transition from scientific hypothesis to statistical model.
Features • Describes each type of statistical model through examples
Features
• Explains how to conduct data analysis
• Serves as a supplemental source of a wide variety of exercises for advanced undergraduate and graduate students in statistical theory courses
• Discusses simulation for model checking, an important aspect of model development and assessment
• Gives students an understanding of the underlying principles of biostatistics
• Presents multilevel regression models, such as multilevel ANOVA, multilevel linear regression, and generalized multilevel
• Includes detailed solutions to every exercise, explaining the key principles in depth
• Shows students how the methods can be implemented using R
• Contains problems drawn from real-life applications in the health sciences
• Offers the data sets and R scripts used in the book along with exercises and solutions on http://www.duke.edu/~song/eeswithr.htm
Contents Basic Probability Theory. Univariate Distribution Theory. 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
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Contents Basic Concepts. Statistical Modeling. Advanced Statistical Modeling. References. Index. Catalog no. C6206, January 2010, 440 pp. Soft Cover, ISBN: 978-1-4200-6206-9, $79.95
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Statistical Genetics
Statistics in Human Genetics and Molecular Biology Cavan Reilly University of Minnesota, Minneapolis, USA
Focusing on the roles of different segments of DNA, this textbook provides students with a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments. Ideal for graduate students in statistics, biostatistics, computer science, and related fields in applied mathematics, the text introduces a diverse set of problems and a number of approaches that have been used to address these problems. It discusses basic molecular biology and likelihood-based statistics, along with physical mapping, markers, linkage analysis, parametric and nonparametric linkage, sequence alignment, and feature recognition. The text illustrates the use of methods that are widespread among researchers who analyze genomic data, such as hidden Markov models and the extreme value distribution. It also covers differential gene expression detection as well as classification and cluster analysis using gene expression data sets.
Features • Provides classroom-proven material for teaching a variety of statistical methods used to solve problems in genetics and genomics • Focuses on genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments • Presents popular methods, such as hidden Markov models, for analyzing genomic data • Contains a substantial breadth of material on microarrays • Describes some Bayesian approaches for solving problems • Includes many worked examples and end-of-chapter exercises
Catalog no. C7263, 2009, 280 pp. ISBN: 978-1-4200-7263-1, $59.95
Selected Contents Basic Molecular Biology for Statistical Genetics and Genomics Basics of Likelihood-Based Statistics Markers and Physical Mapping Basic Linkage Analysis Extensions of the Basic Model for Parametric Linkage Nonparametric Linkage and Association Analysis Sequence Alignment Significance of Alignments and Alignment in Practice Hidden Markov Models Feature Recognition in Biopolymers Multiple Alignment and Sequence Feature Discovery Statistical Genomics Functional genomics The technology Spotted cDNA arrays Oligonucleotide arrays Normalization Detecting Differential Expression Multiple testing and the false discovery rate Significance analysis for microarrays Model based empirical Bayes approach A case study: normalization and differential detection Cluster Analysis in Genomics Some approaches to cluster analysis Determining the number of clusters Biclustering Classification in Genomics For more complete contents, visit www.crctextbooks.com
For more information and complete contents, visit www.crctextbooks.com
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Statistical Theory and Methods
New!
Design of Experiments An Introduction Based on Linear Models Max Morris Iowa State University, Ames, USA
Offering deep insight into the connections between design choice and the resulting statistical analysis, this graduate-level text explores how experiments are designed using the language of linear statistical models. It presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems. The core material covers a review of linear statistical models, completely randomized designs, randomized complete blocks designs, Latin squares, analysis of data from orthogonally blocked designs, balanced incomplete block designs, random block effects, split-plot designs, and two-level factorial experiments. The remainder of the text discusses factorial group screening experiments, regression model design, and an introduction to optimal design. This textbook enables students to fully appreciate the fundamental concepts and techniques of experimental design as well as the real-world value of design. It gives them a profound understanding of how design selection affects the information obtained in an experiment.
Features • Discusses the explicit relationship between experimental design and the quality of data analysis • 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 commands provided in an appendix • Incorporates actual experiments drawn from the scientific and technical literature • Includes many end-of-chapter exercises Solutions manual available for qualifying instructors
Selected Contents Linear Statistical Models Completely Randomized Designs Randomized Complete Blocks and Related Designs Latin Squares and Related Designs Some Data Analysis for CRDs and Orthogonally 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-Order Polynomial Models Regression Experiments: Second-Order Polynomial Models Introduction to Optimal Design Optimal design fundamentals Optimality criteria Algorithms Exercises appear at the end of each chapter. For more complete contents, visit www.crctextbooks.com
Catalog no. C9233, July 2010, 370 pp. ISBN: 978-1-58488-923-6, $89.95
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Statistical Theory and Methods
New!
Design and Analysis of Experiments with SAS John Lawson Brigham Young University, Provo, Utah, USA
A culmination of the author’s many years of consulting and teaching, this textbook covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis. Drawing on a variety of application areas, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments. Harnessing the capabilities of SAS 9.2, it includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The author discusses how the sample size, the assignment of experimental units to combinations of treatment factor levels (error control), and the selection of treatment factor combinations (treatment design) affect the resulting variance and bias of estimates as well as the validity of conclusions.
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
Features
Constrained Mixture Experiments
• Emphasizes the connection between the experimental units, the way treatments are randomized to experimental units, and the proper error term for an analysis of data • Uses SAS 9.2 throughout to illustrate the construction of experimental designs and analysis of data • Shows how to display experimental results graphically using SAS 9.2 ods graphics • Provides uniform coverage on experimental designs and design concepts that are most commonly 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 at http://lawson.mooo.com
Blocking Mixture Experiments
Catalog no. C6060, May 2010, 596 pp. ISBN: 978-1-4200-6060-7, $99.95
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 Increasing Knowledge Sequential Experimentation One-Step Screening and Optimization Evolutionary Operation Exercises appear at the end of each chapter. For more complete contents, visit www.crctextbooks.com
For more information and complete contents, visit www.crctextbooks.com
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Statistical Theory and Methods
New! Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen, Wesley O. Johnson, Adam J. Branscum, and Timothy E. Hanson Emphasizing the use of WinBUGS and R to analyze real data, this text presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference. The text then covers Monte Carlo methods. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions.
Features
Selected Contents Fundamental Ideas I Integration versus Simulation Fundamental Ideas II Comparing Populations Simulations Generating Random Samples Traditional Monte Carlo Methods Basics of Markov Chain Theory Markov Chain Monte Carlo Basic Concepts of Regression Binomial Regression Linear Regression Correlated Data Count Data Time to Event Data Time to Event Regression
• Offers flexible options for teaching a variety of courses • Covers a large number of statistical models • Emphasizes the elicitation of reasonable prior information • 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-world examples throughout • Provides data, programming code, and other materials at www.stat.unm.edu/~fletcher
Binary Diagnostic Tests Basic Ideas One Test, One Population Two Tests, Two Populations Prevalence Distributions Illustrations: Coronary Artery Disease Nonparametric Models Flexible Density Shapes Flexible Regression Functions Proportional Hazards Modeling Illustrations: Galaxy Data For more complete contents, visit www.crctextbooks.com
Catalog no. K10199, July 2010, 516 pp. ISBN: 978-1-4398-0354-7, $69.95
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Statistical Theory and Methods New!
Time Series Modeling, Computation, and Inference Raquel Prado University of California, Santa Cruz, USA
Mike West Duke University, Durham, North Carolina, USA
Focusing on Bayesian approaches and computations using up-to-date simulation-based methods for inference, Time Series integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. Along with core models and methods, this text offers students sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application 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 to illustrate the flexibility and practical impact of the models and methods • Emphasizes model-based, computationally intensive analysis of structured time series • Discusses recent techniques for modeling time series data, such as dynamic graphical models, SMC methods, and nonlinear/non-Gaussian dynamic 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
Selected Contents Traditional Time Domain Models The Frequency Domain Dynamic Linear Models General linear model structures Forecast functions and model forms Inference in dynamic linear models (DLMs): basic normal theory Extensions: non-Gaussian and nonlinear models Posterior simulation: MCMC algorithms State-Space Time-Varying Autoregressive Models Time-varying autoregressions (TVAR) and decompositions TVAR model specification and posterior inference Extensions Sequential Monte Carlo Methods for State-Space Models Mixture Models in Time Series Markov switching models Multiprocess models Mixtures of general state-space models Case study: detecting fatigue from EEGs Univariate stochastic volatility models Topics and Examples in Multiple Time Series Multichannel modeling of EEG data Some spectral theory Dynamic lag/lead models Other approaches Vector AR and ARMA Models Multivariate DLMs and Covariance Models Theory of multivariate and matrix normal DLMs Multivariate DLMs and exchangeable time series Learning cross-series covariances Time-varying covariance matrices Multivariate dynamic graphical models Problems appear at the end of each chapter. For more complete contents, visit www.crctextbooks.com
For more information and complete contents, visit www.crctextbooks.com
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Statistical Theory and Methods New!
Introduction to General and Generalized Linear Models Henrik Madsen and Poul Thyregod Technical University, Lyngby, Denmark
Since the mathematics behind generalized linear models is often difficult to follow while the mathematics behind general linear models is well understood, Introduction to General and Generalized Linear Models describes the methodology behind both models in a parallel setup. After introducing a likelihood framework sufficient to cover both approaches, the authors present general linear models, including analysis of covariance, before moving on to more complicated generalized linear models using the same likelihood-based example. Numerous simulated and real-world examples, implemented using R and SAS, illustrate the methods discussed. The text also provides exercises to develop further understanding.
Features • Provides a unified likelihood-based framework for general and generalized linear models • Covers mixed-effects and hierarchical models • Contains a number of simulated and real-world examples implemented using R and SAS • Includes exercises to help develop an understanding of the methods
Applied Statistical Inference with MINITAB® Sally A. Lesik Central Connecticut State University, New Britain, USA
Through clear, step-by-step mathematical calculations, this text enables students to gain a solid understanding of how to apply statistical techniques in practice using MINITAB. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis. Taking an introductory, practical approach to statistics, the text establishes the foundation for students to build on work in more advanced inferential statistics. Ideal for students in the social sciences, the book is designed for a course in applied statistics and research methods.
Features • Presents the techniques and methods of applied inference in a step-by-step manner • Provides a complete integration of MINITAB throughout the text • Includes fully worked out examples so students can easily follow the calculations • Offers data sets and a trial version of MINITAB on accompanying CD-ROMs • Contains a set of homework problems at the end of each chapter Solutions manual available for qualifying instructors
Contents
Contents
The Likelihood Principle. General Linear Models. Generalized Linear Models. Mixed Effects Models. Hierarchical Models. Some Probability Distributions.
Introduction. Graphing Variables. Descriptive Representations of Data and Random Variables. Basic Statistical Inference. Simple Linear Regression. More on Simple Linear Regression. Multiple Regression Analysis. More on Multiple Regression. ANOVA. Other Topics. Index.
Catalog no. C9155, September 2010, c. 318 pp. ISBN: 978-1-4200-9155-7, $79.95
Catalog no. C6583, January 2010, 464 pp. ISBN: 978-1-4200-6583-1, $89.95
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Statistical Theory and Methods Design and Analysis of Experiments
Logistic Regression Models
Classical and Regression Approaches with SAS
Joseph M. Hilbe Jet Propulsion Laboratory, California Institute of Technology, Pasadena, and Arizona State University, Tempe, USA
Leonard C. Onyiah St. Cloud State University, Minnesota, USA
Based on a successful course taught by the author, this text presents an overview of the full range of logistic models. It illustrates how to apply the models to various types of data. Stata is used to develop, evaluate, and display most models while R code is given at the end of most chapters. Example data sets are accessible online in Stata, R, Excel, SAS, SPSS, and Limdep formats. Solutions manual available for qualifying instructors
Capitalizing on the availability of cutting-edge software, the author uses both manual methods and SAS programs to carry out analyses. He provides examples to illustrate numerous designs. The text includes the full SAS code and outputs as well as end-of-chapter exercises to encourage hands-on SAS programming experience. Solutions manual available for qualifying instructors
Catalog no. C6054, 2009, 856 pp. ISBN: 978-1-4200-6054-6, $99.95
Catalog no. C7575, 2009, 656 pp. ISBN: 978-1-4200-7575-5, $79.95
An Introduction to Generalized Linear Models Third Edition
A Primer on Linear Models
Annette J. Dobson University of Queensland, Herston, Australia
John F. Monahan
Adrian G. Barnett
North Carolina State University, Raleigh, USA
Queensland University of Technology, Kelvin Grove, Australia
“… explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text … .” —Biometrics
Updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis, this new edition provides a cohesive framework for statistical modeling. Data sets and solutions to the exercises are offered online. Catalog no. C9500, 2008, 320 pp., Soft Cover ISBN: 978-1-58488-950-2, $60.95
“… well written … would serve well as the textbook for an introductory course in linear models … .” —Justine Shults, Journal of Biopharmaceutical Statistics, 2009, Issue 3
Employing non-full-rank design matrices throughout, this text provides a concise yet solid foundation for understanding basic linear models. It presents proofs and discussions from both algebraic and geometric viewpoints and includes exercises of varying levels of difficulty at the end of each chapter. Catalog no. C6201, 2008, 304 pp., Soft Cover ISBN: 978-1-4200-6201-4, $49.95
For more information and complete contents, visit www.crctextbooks.com
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Bayesian Methods for Data Analysis Third Edition
Nonparametric Statistical Inference
Bradley P. Carlin
Fifth Edition
University of Minnesota, Minneapolis, USA
Thomas A. Louis Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
An Introduction, Second Edition
Jean Dickinson Gibbons and Subhabrata Chakraborti
Peter W. Jones and Peter Smith
University of Alabama, Tuscaloosa, USA
• Uses Mathematica® and R to illustrate the modeling and analysis of random experiments using the theory of probability • Over 50 worked examples and more than 200 end-ofchapter problems • Describes applications of probability to modeling problems in engineering, medicine, and biology • Book website includes the Mathematica and R programs as well as a solutions manual for qualifying instructors
• The source for learning about “… the third edition has … new nonparametric additions of BUGS and R code statistics—covers the most throughout the book and reorgancommonly used nonization or expansion of several parametric procedures chapters. … I am glad to see that the software code and examples • At least 50 percent of the material revised, with more have also been made available on problems and examples the website http://www.biostat. umn.edu/~brad/dataCL3.html so • Carefully states assumptions that users can truly enjoy easy and develops the theory access and convenience in reprobehind procedures ducing the computations in the book. … a very worthy edition and • Realistic research examples from the social, behavioral, I highly recommend it as a textand life sciences book …” —Journal of Applied Statistics, • Many tables needed for Vol. 37, No. 4, April 2010 finding P values and obtaining confidence interval Solutions manual available estimates of parameters for qualifying instructors Catalog no. C6978, 2008 552 pp. ISBN: 978-1-58488-697-6 $69.95
Stochastic Processes
Catalog no. C7619, July 2010 650 pp. ISBN: 978-1-4200-7761-2 $99.95
Keele University, Staffordshire, UK
Catalog no. K10004, January 2010 232 pp., Soft Cover ISBN: 978-1-4200-9960-7 $79.95
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