Articles by "Statistics"

Showing posts with label Statistics. Show all posts

Tuesday, February 27, 2018 , , ,
Paul Kinnear,‎ Colin Gray ... 464 pages - Publisher: Psychology Press; (July, 2004) ... Language: English - ISBN-10: 1841695246 - ISBN-13: 978-1841695242 ...

SPSS 12 Made Simple provides a step-by-step coverage of every aspect of data analysis with SPSS from data entry to interpretation of the output. As well as advice on data entry and checking, there is guidance on the best ways of describing a data set and the choice of an appropriate statistical technique. Finally, the output is fully explained, with reference to fully annotated SPSS output. Extensive illustrations show exactly what is on the screen at every stage of the process, helping the reader to avoid common pitfalls and check their progress along the way. Most chapters end with practical exercises to illustrate the main points raised and allow the reader to test their understanding; but there is a final general revision section with further exercises on a range of topics. SPSS 12 Made Simple: * Covers a wide range of statistical tests including t-tests, ANOVA, correlation, regression, multi-way frequency analysis, discriminant analysis, logistic regression and factor analysis. * Shows you how to get as much out of your data as possible. * Gives advice (with appropriate cautions and caveats) on choosing a statistical test. * Makes extensive use of annotated screen snapshots of SPSS output, windows and dialog boxes. * Includes both chapter-specific and general exercises.
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Monday, February 26, 2018 , ,
Mario F. Triola ... 792 pages - Publisher: Pearson; 13th edition (January, 2017) ... Language: English - ISBN-10: 0134462459 - ISBN-13: 978-0134462455 ...

Real data brings statistics to life From opinion polls and clinical trials to self-driving cars, statistics influences and shapes the world around us. Best-selling author Marty Triola is committed to keeping Elementary Statistics relentlessly current--with an unprecedented amount of up-to-the-minute real data--so that readers of all backgrounds understand the role of statistics in the world around them. In addition to an abundance of new data sets, examples, and exercises, the 13th Edition is designed to be even more flexible, with the addition of learning objectives as an organizational tool, larger data sets, and new topics in line with advancements in statistics.
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Saturday, February 10, 2018 , , , ,
The IBM SPSS Statistics helps data analysts, planners, forecasters, survey researchers, program evaluators and database marketers – among others – to easily accomplish tasks at every phase of the analytical process. It includes a broad array of fully integrated Statistics capabilities and related products for specialized analytical tasks across the enterprise. The software will improve productivity significantly and help achieve superior results for specific projects and business goals.
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Friday, February 09, 2018 ,
Alfredo H-S. Ang,‎ W. H. Tang ... 420 pages - Publisher: Wiley; 2nd edition (March, 2006) ... Language: English - ISBN-10: 047172064X - ISBN-13: 978-0471720645 ...

Apply the principles of probability and statistics to realistic engineering problems: The easiest and most effective way to learn the principles of probabilistic modeling and statistical inference is to apply those principles to a variety of applications. That's why Ang and Tang's Second Edition of Probability Concepts in Engineering (previously titled Probability Concepts in Engineering Planning and Design) explains concepts and methods using a wide range of problems related to engineering and the physical sciences, particularly civil and environmental engineering. Now extensively revised with new illustrative problems and new and expanded topics, this Second Edition will help you develop a thorough understanding of probability and statistics and the ability to formulate and solve real-world problems in engineering. The authors present each basic principle using different examples, and give you the opportunity to enhance your understanding with practice problems. The text is ideally suited for students, as well as those wishing to learn and apply the principles and tools of statistics and probability through self-study. Key Features in this 2nd Edition: * A new chapter (Chapter 5) covers Computer-Based Numerical and Simulation Methods in Probability, to extend and expand the analytical methods to more complex engineering problems. * New and expanded coverage includes distribution of extreme values (Chapter 3), the Anderson-Darling method for goodness-of-fit test (Chapter 6), hypothesis testing (Chapter 6), the determination of confidence intervals in linear regression (Chapter 8), and Bayesian regression and correlation analyses (Chapter 9). * Many new exercise problems in each chapter help you develop a working knowledge of concepts and methods. * Provides a wide variety of examples, including many new to this edition, to help you learn and understand specific concepts. * Illustrates the formulation and solution of engineering-type probabilistic problems through computer-based methods, including developing computer codes using commercial software such as MATLAB and MATHCAD. * Introduces and develops analytical probabilistic models and shows how to formulate engineering problems under uncertainty, and provides the fundamentals for quantitative risk assessment.
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Tuesday, January 09, 2018 , , ,
Richard McElreath ... 487 pages - Publisher: Chapman and Hall/CRC; (December, 2015) ... Language: English - ISBN-10: 1482253445 - ISBN-13: 978-1482253443.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource: The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
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Saturday, December 23, 2017 , , ,
Xiangyu Kong,‎ Changhua Hu,‎ Zhansheng Duan ... 323 pages - Publisher: Springer; 1st edition (January 9, 2017) ... Language: English - ISBN-10: 981102913X - ISBN-13: 978-9811029134 ...

 This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
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Thursday, December 14, 2017 , ,
Sanjeev Kulkarni,‎ Gilbert Harman ... 232 pages - Publisher: Wiley; 1st edition (August, 2011) ... Language: English - ISBN-10: 0470641835 - ISBN-13: 978-0470641835 ...

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning: A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
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Tuesday, November 28, 2017 , , ,
Stat-Ease Design-Expert v10.0.7.0 for x64 [Size: 38 MB] ... Stat-Ease, Inc. welcomes you to version 10 of Design-Expert software (DX10) for design of experiments (DOE). Use this Windows®-based program to optimize your product or process. It provides many powerful statistical tools, such as: Two-level factorial screening designs: Identify the vital factors that affect your process or product so you can make breakthrough improvements. + General factorial studies: Discover the best combination of categorical factors, such as source versus type of raw material supply. + Response surface methods (RSM): Find the optimal process settings to achieve peak performance. + Mixture design techniques: Discover the ideal recipe for your product formulation. + Combinations of process factors, mixture components, and categorical factors: Mix your cake (with different ingredients) and bake it too! Your Design-Expert program offers rotatable 3D plots to easily view response surfaces from all angles. Use your mouse to set flags and explore the contours on interactive 2D graphs. Our numerical optimization function finds maximum desirability for dozens of responses simultaneously! You'll find a wealth of statistical details within the program itself via various Help screens. Take advantage of this information gold mine that is literally at your fingertips. Also, do not overlook the helpful annotations provided on all reports. For a helpful collection of checklists and "cheat sheets," see the Handbook for Experimenters. It's free to all registered users. Furthermore, for quick primers on the principles of design and analysis, we recommend you read the following two soft-cover books from Stat-Ease Principals Mark Anderson and Pat Whitcomb,  published by Productivity Press of New York city: DOE Simplified: Practical Tools for Effective Experimentation,3rd Edition + RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments.
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Wednesday, November 22, 2017 , , , ,
Syouji Nakamura, Toshio Nakagawa ... 316 pages - Publisher: World Scientific Publishing Company (November, 2009) ... Language: English - ISBN-10: 9814277436 - ISBN-13: 978-9814277433 ...

Reliability theory and applications become major concerns of engineers and managers engaged in making high quality products and designing highly reliable systems. This book aims to survey new research topics in reliability theory and useful applied techniques in reliability engineering. Our research group in Nagoya, Japan has continued to study reliability theory and applications for more than twenty years, and has presented and published many good papers at international conferences and in journals. This book focuses mainly on how to apply the results of reliability theory to practical models. Theoretical results of coherent, inspection, and damage systems are summarized methodically, using the techniques of stochastic processes. There exist optimization problems in computer and management sciences and engineering. It is shown that such problems as computer, information and network systems are solved by using the techniques of reliability. Furthermore, some useful techniques applied to the analysis of stochastic models in management science and plants are shown. The reader will learn new topics and techniques, and how to apply reliability models to actual ones. The book will serve as an essential guide to a subject of study for graduate students and researchers and as a useful guide for reliability engineers engaged not only in maintenance work but also in management and computer works.
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Monday, November 20, 2017 , , , ,
Hector Guerrero ... 338 pages - Publisher: Springer; (March, 2010) ... Language: English - ISBN-10: 3642108342 - ISBN-13: 978-3642108341 ...

Why does the World Need―Excel Data Analysis, Modeling, and Simulation? When spreadsheets ?rst became widely available in the early 1980s, it spawned a revolution in teaching. What previously could only be done with arcane software and large scale computing was now available to the common-man, on a desktop. Also, before spreadsheets, most substantial analytical work was done outside the classroom where the tools were; spreadsheets and personal computers moved the work into the classroom. Not only did it change how the analysis curriculum was taught, but it also empowered students to venture out on their own to explore new ways to use the tools. I can’t tell you how many phone calls, of?ce visits, and/or emails I have received in my teaching career from ecstatic students crowing about what they have just done with a spreadsheet model. I have been teaching courses related to spreadsheet based analysis and modeling for about 25 years and I have watched and participated in the spreadsheet revolution.
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Friday, November 17, 2017 , , ,
R. Lyman Ott, Micheal T. Longnecker ... 1297 pages - Publisher: Brooks Cole; 6th edition (December, 2008) ... Language: English - ISBN-10: 0495017582 - ISBN-13: 978-0495017585 ...

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.
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Wednesday, November 15, 2017 , , ,
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ... 426 pages - Publisher: Springer; (September, 2017) ... Language: English - ISBN-10: 1461471370 - ISBN-13: 978-1461471370.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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Tuesday, November 14, 2017 , , ,
Brian Steele, John Chandler, Swarna Reddy ... 430 pages - Publisher: Springer; 1st edition (January, 2017) ... Language: English - ISBN-10: 3319457950 - ISBN-13: 978-3319457956 ...

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts: (a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter. (b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System. (c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Thursday, November 09, 2017 , , , ,
IBM SPSS Statistics v25 [Size: 722 MB] ... IBM SPSS Statistics Subscription delivers the power of SPSS Statistics predictive analytics capabilities with a flexible subscription payment option, including an easier purchasing, management and licensing experience. With the introduction of SPSS Statistics Subscription, organizations, groups and individuals can take advantage of predictive analytics capabilities to deliver the maximum amount of value to the user. Propel research & analysis with a fast and powerful solution: A comprehensive set of statistical tools - Work inside a single, integrated interface to run descriptive statistics, regression, advanced statistics and many more. Create publication ready charts, tables, and decision trees in one tool. - Integration with Open Source - Enhance the SPSS Syntax with R and Python through specialized extensions. Leverage the 130+ extensions available on our Extension Hub, or build your own and share with your peers to create a customized solution. - Easy statistical analysis - Use a simple drag and drop interface to access a wide range of capabilities and work across multiple data sources. Plus, flexible deployment options make purchasing and managing your software easy. Subscription Base: Data Preparation - IBM SPSS Data Preparation is a fully integrated module of IBM SPSS Statistics. - Bootstrapping - Bootstrapping is a useful technique for testing model stability. Custom Tables and Advanced Statistics Add-On: Advanced Statistics - Improve the accuracy of your analysis and the dependability of your conclusions with statistics designed for complex relationships. - Regression - IBM SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. - Custom Tables: IBM SPSS Custom Tables enables you to summarize SPSS Statistics data, and display your analyses as presentation-quality, production-ready tables.
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Sunday, November 05, 2017 , , ,
Bradley P. Carlin, Thomas A. Louis ... 552 pages - Publisher: Chapman and Hall/CRC; 3rd edition (June, 2008) ... Language: English - ISBN-10: 1584886978 - ISBN-13: 978-1584886976 ...

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition: New data examples, corresponding R and WinBUGS code, and homework problems - Explicit descriptions and illustrations of hierarchical modeling―now commonplace in Bayesian data analysis - A new chapter on Bayesian design that emphasizes Bayesian clinical trials - A completely revised and expanded section on ranking and histogram estimation - A new case study on infectious disease modeling and the 1918 flu epidemic - A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem―available both electronically and in print. Ideal for Anyone Performing Statistical Analyses: Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
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Saturday, November 04, 2017 , , ,
Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov ... 488 pages - Publisher: Springer; (September, 2014) ... Language: English - ISBN-10: 3319099027 - ISBN-13: 978-3319099026 ...

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.
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Sunday, October 29, 2017 , ,
Stefan Schäffler ... 148 pages - Publisher: Springer; (June, 2012) ... Language: English - ASIN: B00A9YGPN8 by Amazon Digital Services ...

This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines including mathematics, operations research, engineering, and economics. The volume deals with constrained and unconstrained problems and puts a special emphasis on large scale problems. It also introduces a new unified concept for unconstrained, constrained, vector, and stochastic global optimization problems. All methods presented are illustrated by various examples. Practical numerical algorithms are given and analyzed in detail. The topics presented include the randomized curve of steepest descent, the randomized curve of dominated points, the semi-implicit Euler method, the penalty approach, and active set strategies. The optimal decoding of block codes in digital communications is worked out as a case study and shows the potential and high practical relevance of this new approach. Global Optimization: A Stochastic Approach is an elegant account of a refined theory, suitable for researchers and graduate students interested in global optimization and its applications.
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Saturday, October 28, 2017 , , , , ,
Rao V. Dukkipati ... 680 pages - Publisher: Anshan Publishers; (October, 2010) ... Language: English - ISBN-10: 1904798675 - ISBN-13: 978-1848290433

This book presents an introduction to MATLAB and its applications in engineering problem solving. It is designed as an introductory course in MATLAB for engineers. The classical methods of electrical circuits,control systems, numerical methods, optimization, direct numerical integration methods, engineering mechanics and mechanical vibrations are covered using MATLAB software. The numerous worked examples and unsolved exercise problems are intended to provide the reader with an awareness of the general applicability to electrical circuits, control systems, numerical methods, optimization, direct numerical integration methods, engineering mechanics and mechanical vibrations using MATLAB. Contents: MATLAB Basics - Electrical Circuits - Control Systems - Numerical Methods - Optimization - Direct Numerical Integration Methods - Engineering Mechanics - Mechanical Vibration.
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Sunday, October 15, 2017 , , , ,
Steve Brooks, Andrew Gelman ... 619 pages - Publisher: Chapman and Hall/CRC; (May, 2011) ... Language: English - ISBN-10: 1420079417 - ISBN-13: 978-1420079418.

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory. 

The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology. The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.
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Monday, October 09, 2017 , ,
Ronald Christensen, Wesley Johnson, Adam Branscum, T. E. Hanson ... 516 pages - Publisher: CRC Press; 1st edition (July, 2010) - Language: English - ISBN-10: 1439803544 - ISBN-13: 978-1439803547 ...

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians 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, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. 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. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.
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