Probability and statistics Books

Book SynopsisRenowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques.Table of ContentsPART I. DESIGNS OF EXPERIMENTS Chapter 1. Controlled Experiments Chapter 2. Observational Studies PART II. DESCRIPTIVE STATISTICS Chapter 3. The Histogram Chapter 4. The Average Standard Deviation Chapter 5. The Normal Approximation for Data Chapter 6. Measurement Error Chapter 7. Plotting Points and Lines PART III. CORRELATION AND REGRESSION Chapter 8. Correlation Chapter 9. More about Correlation Chapter 10. Regression Chapter 11. The R.M.S. Error for Regression Chapter 12. The Regression Line PART IV. PROBABILITY Chapter 13. What Are the Chances Chapter 14. More about Chance Chapter 15. The Binomial Formula PART V. CHANCE VARIABILITY Chapter 16. The Law of Averages Chapter 17. The Expected Value and Standard Error Chapter 18. The Normal Approximation for Probablity PART VI. SAMPLING Chapter 19. Sample Surveys Chapter 20. Chance Errors in Sampling Chapter 21. The Accuracy of Percentages Chapter 22. Measuring Employment and Significance Unemployment Chapter 23. The Accuracy of Averages PART VII. CHANCE MODELS Chapter 24. Model for Measurement Error Chapter 25. Chance Models in Genetics PART VIII. TESTS OF SIGNIFICANCE Chapter 26. Tests of Significance Chapter 27 More Tests for Averages Chapter 28. The Chi-Square Test Chapter 29. A Closer Look at Tests of Significance

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Book SynopsisThis practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.Trade Review'This book is an exciting addition to the literature on machine learning and graphical models. What makes it unique and interesting is that it provides a unified treatment of machine learning and related fields through graphical models, a framework of growing importance and popularity. Another feature of this book lies in its smooth transition from traditional artificial intelligence to modern machine learning. The book is well-written and truly pleasant to read. I believe that it will appeal to students and researchers with or without a solid mathematical background.' Zheng-Hua Tan, Aalborg University, Denmark'With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying website, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included.' Jaakko Hollmén, Aalto University'The chapters on graphical models form one of the clearest and most concise presentations I have seen … The exposition throughout uses numerous diagrams and examples, and the book comes with an extensive software toolbox - these will be immensely helpful for students and educators. It's also a great resource for self-study.' Arindam Banerjee, University of Minnesota'I repeatedly get unsolicited comments from my students that the contents of this book have been very valuable in developing their understanding of machine learning … My students praise this book because it is both coherent and practical, and because it makes fewer assumptions regarding the reader's statistical knowledge and confidence than many books in the field.' Amos Storkey, University of EdinburghTable of ContentsPreface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.

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Book SynopsisReveals the simple techniques needed to estimate virtually anything and illustrates them using an eclectic array of problems. This title shows how to estimate everything from how closely you can orbit a neutron star without being pulled apart by gravity, to the fuel used to transport your food from the farm to the store.Trade Review"This follow-up to the popular Guesstimation offers more on the joy of mathematical estimation, and inspiration for the budding analyst."--Nature "The books do a wonderful job at helping the reader to master the craft."--Cut the Knot Insights "A delightful volume... I hope to be able to use many of the tricks I learned in the future. I also hope to teach some of them to students. This would make a great secondary textbook in many classes, ranging from quantitative literacy to a science methods class for future educators. A careful study of this book would certainly improve a student's ability to take a complicated question, break it down into solvable parts, and assemble the parts to find an answer. Because this is quite close to what I want my students to do when faced with a difficult problem in pure mathematics as well, I consider this to be a very valuable book indeed."--Dominic Klyve, MAA Reviews "Guesstimation 2.0: Solving Today's Problems on the Back of a Napkin succeeds where most popular science literature so often fails. This is because it provides its readers with a scientific tool they can use immediately in their everyday lives... [Makes] an excellent addition for the casual scientist, job interviewee, or anyone hoping to impress their friends at a party."--Gabriel Thoumi, Mongabay.com "Readers who enjoyed Weinstein's first volume will be pleased with this instalment."--Choice "Guesstimation 2.0 is a book that was made to mediate between fun and useful... Whether or not a fan of numbers, it's always cool to appear smart, therefore Guesstimation 2.0 is an excellent element to add to one's arsenal."--Sarthak Shankar, Organiser "Certainly a good read for any teacher who enjoys numbers and the world around us."--Mark Hughes, Mathematics Teaching in the Middle School "Guesstimation's problems are fun and engaging in character, and the solutions are intuitive and well explained. Each problem and solution stands independently, and is about four pages long, making the book ideal for passing a quick ten minutes, and easy to pick up and put down. If, like me, you like ill-posed questions to have concrete answers then Guesstimation is definitely a good place to hone your estimation skills!"--Fionntan Roukema, Mathematical SpectrumTable of ContentsAcknowledgments xi Preface xiii 1 How to Solve Problems 1 2 General Questions 11 *2.1 Who unrolled the toilet paper? 13 *2.2 Money height 17 *2.3 Blotting out the Sun 19 *2.4 Really extra-large popcorn 21 *2.5 Building volume 25 *2.6 Mass of money 29 *2.7 A baseball in a glass of beer 33 *2.8 Life on the phone 37 *2.9 Money under the bridge 41 *2.10 Monkeys and Shakespeare 45 *2.11 The titans of siren 49 *2.12 Airheads at the movies 53 *2.13 Heavy cars and heavier people 55 *2.14 Peeing in the pool 59 3 Recycling: What Really Matters? 63 *3.1 Water bottles 67 *3.2 99 bottles of beer on the wall ... 71 *3.3 Can the aluminum 75 *3.4 Paper or plastic? 79 *3.5 Paper doesn't grow on trees! 83 *3.6 The rain in Spain ... 87 *3.7 Bottom feeders 91 *3.8 You light up my life! 95 4 The Five Senses 101 *4.1 Don't stare at the Sun 103 *4.2 Men of vision 105 *4.3 Light a single candle 109 *4.4 Oh say can you see? 113 *4.5 Bigger eyes 117 *4.6 They're watching us! 121 *4.7 Beam the energy down, Scotty! 125 *4.8 Oh say can you hear? 131 *4.9 Heavy loads 135 5 Energy and Work 139 *5.1 Power up the stairs 143 *5.2 Power workout 145 *5.3 Water over the dam 149 *5.4 A hard nut to crack 153 *5.5 Mousetrap cars 155 *5.6 Push hard 159 *5.7 Pumping car tires 161 *5.8 Pumping bike tires 165 *5.9 Atomic bombs and confetti 169 6 Energy and Transportation 173 *6.1 Gas-powered humans 177 *6.2 Driving across country 181 *6.3 Keep on trucking 185 *6.4 Keep on biking 189 *6.5 Keep on training 193 *6.6 Keep on flying 197 *6.7 To pee or not to pee 201 *6.8 Solar-powered cars 205 *6.9 Put a doughnut in your tank 209 *6.10 Perk up your car 213 *6.11 Don't slow down 217 *6.12 Throwing tomatoes 219 7 Heavenly Bodies 223 *7.1 Orbiting the Sun 227 *7.2 Flying off the Earth 229 *7.3 The rings of Earth 233 *7.4 It is not in the stars to hold our destiny 237 *7.5 Orbiting a neutron star 241 *7.6 How high can we jump? 245 *7.7 Collapsing Sun 249 *7.8 Splitting the Moon 253 *7.9 Splitting a smaller moon 257 *7.10 Spinning faster and slower 263 *7.11 Shrinking Sun 267 *7.12 Spinning Earth 271 *7.13 The dinosaur killer and the day 273 *7.14 The Yellowstone volcano and the day 277 *7.15 The orbiting Moon 281 *7.16 The shortest day 283 8 Materials 289 *8.1 Stronger than spider silk 291 *8.2 Beanstalk to orbit 295 *8.3 Bolt failure 299 *8.4 Making mountains out of molecules 303 *8.5 Chopping down a tree 307 9 Radiation 311 *9.1 Nuclear neutrinos 315 *9.2 Neutrinos and you 319 *9.3 Solar neutrinos 323 *9.4 Supernovas can be dangerous 327 *9.5 Reviving ancient bacteria 331 *9.6 Decaying protons 335 *9.7 Journey to the center of the galaxy 337 Appendix A * Dealing with Large Numbers 341 * A.1 Large Numbers 341 * A.2 Precision, Lots of Digits, and Lying 343 * A.3 Numbers and Units 345 Appendix B * Pegs to Hang Things On 347 Bibliography 351 Index 355

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Book SynopsisStigler shows how statistics arose from the interplay of mathematical concepts and the needs of several applied sciences. His emphasis is upon how methods of probability theory were developed for measuring uncertainty, for reducing uncertainty, and as a conceptual framework for quantitative studies in the social sciences.Trade ReviewOne is tempted to say that the history of statistics in the nineteenth century will be associated with the name Stigler. -- Morris Kline * New York Times Book Review *An exceptionally searching, almost loving, study of the relevant inspirations and aberrations of its principal characters James Bernoulli, de Moivre, Bayes, Laplace, Gauss, Quetelet, Lexis, Galton, Edgeworth, and Pearson, not neglecting a grand supporting cast… The definitive record of an intellectual Golden Age, an overoptimistic climb to a height not to be maintained. -- M. Stone * Science *In this tour de force of careful scholarship, Stephen Stigler has laid bare the people, ideas, and events underlying the development of statistics… He has written an important and wonderful book… Sometimes Stigler’s prose is so evocative it is almost poetic. -- Howard Wainer * Contemporary Psychology *The book is a pleasure to read: the prose sparkles; the protagonists are vividly drawn; the illustrations are handsome and illuminating; the insights plentiful and sharp. This will remain the definitive work on the early development of mathematical statistics for some time to come. -- Lorraine J. Daston * Journal of Modern History *Stigler’s book exhibits a rare combination of mastery of technical materials, sensitivity to conceptual milieu, and near exhaustive familiarity with primary sources. An exemplary study. -- Lorraine DastonTable of ContentsIntroduction PART 1: The Development of Mathematical Statistics in Astronomy and Geodesy before 1827 1. Least Squares and the Combination of Observations Legendre in 1805 Cotes's Rule Tobias Mayer and the Libration of the Moon Saturn, Jupiter, and Enter Laplace's Rescue of the Solar System Roger Boscovich and the Figure of the Earth Laplace and the Method of Situation Legendre and the Invention of Least Squares 2. Probabilists and the Measurement of Uncertainty Jacob Bernoulli De Moivre and the Expanded Binomial Bernoulli's Failure De Moivre's Approximation De Moivre's Deficiency Simpson and Bayes Simpson's Crucial Step toward Error A Bayesian Critique 3. Inverse Probability Laplace and Inverse Probability The Choice of Means The Deduction of a Curve of Errors in 1772-1774

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Book SynopsisTaken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con ducted in statistics departmeTrade ReviewWinner of the 2005 DeGroot Prize.From the reviews:"Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004"This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004"Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005"As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005)"This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004)"This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)"The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004)"The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005)Table of ContentsProbability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.

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Book SynopsisDoes the subject of data analysis make you dizzy? This book features introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages. It helps you to identify valid, useful, and understandable patterns in data.Table of ContentsIntroduction 1 Part I: Introducing Big Data Statistics 7 Chapter 1: What Is Big Data and What Do You Do With It? 9 Chapter 2: Characteristics of Big Data: The Three Vs 19 Chapter 3: Using Big Data: The Hot Applications 27 Chapter 4: Understanding Probabilities 41 Chapter 5: Basic Statistical Ideas 57 Part II: Preparing and Cleaning Data 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis 83 Chapter 7: Figuring the Format: Important Computer File Formats 99 Chapter 8: Checking Assumptions: Testing for Normality 107 Chapter 9: Dealing with Missing or Incomplete Data 119 Chapter 10: Sending Out a Posse: Searching for Outliers 129 Part III: Exploratory Data Analysis (EDA) 141 Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143 Chapter 12: A Plot to Get Graphical: Graphical Techniques 155 Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173 Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191 Chapter 15: Regression Analysis 215 Chapter 16: When You’ve Got the Time: Time Series Analysis 243 Part IV: Big Data Applications 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271 Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297 Chapter 19: Seeking Free Sources of Financial Data 319 Part V: The Part of Tens 331 Chapter 20: Ten (or So) Best Practices in Data Preparation 333 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339 Index 349

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Book SynopsisScientific research is a proven and powerful tool for discovering the answers to biological questions. As such, today''s students need to be well-versed in experimental design, analysis, and the communication of research. Furthermore, they must appreciate how all of these aspects are interlinked--how, for example, statistics can be used to inform the design of a particular experiment. As a resource which skillfully integrates all of the key aspects relating to biological research, Research Methods for the Biosciences is the perfect guide for undergraduates.The exceptionally clear layout takes students through choosing a project and planning their research; collecting, evaluating, and analyzing their data; and finally reporting their results. Research methods, which can often seem abstract, are brought to life through the use of examples taken from real undergraduate research. Friendly guidance and advice is provided throughout the text, and little prior knowledge or mathematical experience is required. Since statistics is a subject best learned through doing, frequent worked examples appear throughout Part Two ''Handling your data'', showing step-by-step how to carry out the various statistical tests. In addition, online software walkthroughs and video screencasts clearly demonstrate how to use software such as SPSS, Minitab, Excel and R to carry out statistical analyses.Online Resource CentreThe Online Resource Centre to accompany Research Methods for the Biosciences features:For students: New video screencasts showing how to carry out statistical tests using software Statistical software walkthroughs for SPSS, Excel, and Minitab Additional statistical tests not included in the main text Full details of calculations given in the in-text boxes Interactive and printable decision tree, to help you design your experiment Interactive and printable risk assessment form Integrative exercises to help students test their understanding of the topics in the bookFor lecturers: A test bank of questions Figures from the book available to downloadTable of ContentsPLANNING YOUR EXPERIMENT; HANDLING YOUR DATA; REPORTING YOUR RESULTS

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Book SynopsisR is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible.This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model.Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.Trade ReviewReview from previous edition The book would make the ideal text for a short course on data management and presentation - it truly packs an amazing amount of wisdom and wit between slim covers. * Trends in Ecology and Evolution *I was engaged by the refreshing style of the authors, that while informal, gives the user clear step-by-step instructions for using the software. Apart from the clear biological leaning of the example data, this book is applicable to anyone learning R (even a statistician!). * Significance *Table of ContentsPreface 1: Getting and getting acquainted with R 2: Getting your data into R 3: Data management, manipulation, and exploration with dplyr 4: Visualising your data 5: Introducing statistics in R 6: Advancing your statistics in R 7: Getting started with generalised linear models 8: Pimping your plots: scales and themes in ggplot2 9: Closing remarks Appendices

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Book SynopsisDr Andrew Mayers is Senior Lecturer in Psychology at Bournemouth UniversityTrade Review"This is a comprehensive resource of all the statistics you could ever want for degree level Psychology" - Neil Cruickshank, Budmouth College, Weymouth "Definitely my go-to statistics textbook, the perfect guide for all levels of undergraduate" - Psychology student, University of Glasgow "This book provides clear and comprehensive coverage of a wide range of statistical tests used in psychology. It will be of great value to novice and advanced researchers alike." - Dr Richard rowe, University of Sheffield "This engaging and student-centred book demystifies the challenges of statistics and SPSS for the numerically anxious student." - Dr Kate Bullen, Aberystwyth University Table of Contents1. Introduction 2. SPSS: The Basics 3. Normal Distribution 4. Significance, effect size, and power 5. Experimental methods - how to choose the correct statistical test 6. Correlation 7. Independent t-test 8. Related t-test 9. Independent one-way ANOVA 10. Repeated-measures one-way ANOVA 11. Independent multi-factorial ANOVA 12. Repeated-measures multi-factorial ANOVA 13. Mixed multi-factorial ANOVA 14. Multivariate analyses 15. Analyses of covariance 16. Linear and multiple linear regression 17. Logistic regression 18. Non-parametric tests 19. Tests for categorical variables 20. Factor analysis 21. Reliability analysis

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Book SynopsisStatistical ConceptsâA First Course presents the first 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra.Covering the most basic statistical concepts, this book is designed to help readers really understand statistical concepts, in what situations they can be applied, and how to apply them to data. Specifically, the text covers basic descriptive statistics, including ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling. The remainder of the text covers various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations.Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.Trade Review"This edition delivers on many fronts and sets this book apart from the rest. The clear and conversational style emphasizes the applied and practical without compromising the theoretical and conceptual underpinnings. The parallel use of SPSS and R that walks the reader step-by-step through the procedures coupled with fully annotated interpretation of printouts are very appealing to both novice and more seasoned applied researchers. Rather than treating subjects like power and effect size or verification of assumptions in isolation, the authors do a fantastic job of blending them with the analyses to make the story behind the numbers more compelling and complete. The abundance of visuals and APA style write-ups all contribute to simplify and enhance the learning experience." - Devdass Sunnassee, Assistant Clinical Professor, University of North Carolina, USA. "I have relied on previous versions of this textbook to bring to life statistical concepts in my beginning and intermediate level graduate classes. I also share this valuable resource with students who ask questions when working on quantitative projects. This fourth edition brings enhanced materials, explanations, and examples to aid students in gaining basic proficiency in foundational statistical concepts. The detailed and numerous practical examples demonstrate the inner workings of basic statistical methods in the social and behavioural sciences. I look forward to sharing this enhanced edition with our graduate program" -Brian F. French, Washington State University, USA. "Combining theory and mathematical accessibility with examples in various fields of behavioral sciences, SPSS and R applications, APA style write-ups, after-chapter conceptual and practice problems for students, online pedagogical aids, this is a valuable book for introductory statistical courses in behavioral sciences. It has a broad coverage of topics, and the addition of the new chapter on mediation and moderation adds to its value as a classroom text or as a reference for applied researchers." -Feifei Ye, RAND Corporation, USA."Anyone familiar with previous editions of Statistical Concepts from Lomax and Hahs-Vaughn recognize and appreciate the pedigagogically sound treatment of statistical methods comprising introductory and intermediate topics found in many quantitative methods graduate programs. In addition to enhancements found in the past versions such as APA-style write-ups of statistical results and the numerous screen shots depicting both annotated SPSS input commands and output; the fourth edition begins each chapter with a concrete research scenario to motivate the particular statistical method. Another new feature that will resonate with instructors and graduate students are the insightful Stop and Think boxes that offer moments to reflect and to make connections between statistical ideas, data, and the software. Clearly, Lomax and Hahs-Vaughn are committed to preparing the next generation of researchers and practitioners, and the latest edition of Statistical Concepts is a must-have reference for those seeking this type of comprehensive quantitative methods training." - Jeffrey R. Harring, University of Maryland, College Park, USA. "I have required this textbook for my introductory and intermediate-level students throughout multiple editions, and it has continued to get better and better. This new edition continues to emphasize the development of statistical understanding while also providing readers with valuable information on how to perform a variety of procedures using SPSS and R. The authors have added a terrific new chapter on mediation and moderation that reviews concepts and procedures that are often not a point of emphasis in traditional (textbook) coverage of multiple regression (but that are crucial for more modern data analysis). This is a book that not only is a wonderful learning resource for students, but also one they will want to keep in their personal libraries to reference when carrying out their own future research." - H. Michael Crowson, The University of Oklahoma, USA.Table of Contents PrefaceAcknowledgements1. INTRODUCTION2. DATA REPRESENTATION3. UNIVARIATE POPULATION PARAMETERS AND SAMPLE STATISTICS4. THE NORMAL DISTRIBUTION AND STANDARD SCORES5. INTRODUCTION TO PROBABILITY AND SAMPLE STATISTICS6. INTRODUCTION TO HYPOTHESIS TESTING: INFERENCES ABOUT A SINGLE MEAN7. INFERENCES ABOUT THE DIFFERENCE BETWEEN TWO MEANS8. INFERENCES ABOUT PROPORTIONS9. INFERENCES ABOUT VARIANCES10. BIVARIATE MEASURES OF ASSOCIATION AppendixReferencesName Index Subject Index

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Book SynopsisSPSS Explained provides the student with all that they need to undertake statistical analysis using SPSS. It combines a step-by-step approach to each procedure with easy-to-follow screenshots at each stage of the process. A number of other helpful features are provided, including: regular advice boxes with tips specific to each test explanations divided into essential' and advanced' sections to suit readers at different levels frequently asked questions at the end of each chapter The third edition of this popular book has been fully updated for IBM SPSS version 27 and also includes: a new chapter on how to undertake mediation and moderation with SPSS updates on changes to SPSS, including updated functionality within ANOVAs and calculations of a priori power analysis Presented in full colour and with a fresh, reader-friendly layout, this fully updated new edition also comes with online support materTrade Review'Statistical methods are an integral part of teaching and research in almost all disciplines in the age of data revolution. The authors have presented introductory, descriptive, and numerical methods, as well as univariate and multivariate model fitting and inferential statistical procedures in a very clear, simplified, and step-by-step illustrative way. This book will be helpful to college and university students, teachers, and researchers who wish to analyse and interpret observational and experimental data from different disciplines across the world.'Professor Dr Shahjahan Khan, Vice Chancellor, Asian University of Bangladesh, Dhaka, BangladeshTable of Contents1. Introduction2. Data entry3. Descriptive statistics4. Illustrative statistics5. Introduction to statistical testing6. t tests7. Introduction to analysis of variance (general linear model)8. One-factor analysis of variance9. Two-factor analysis of variance10. Introduction to multivariate analysis of variance11. Nonparametric two sample tests12. Nonparametric k sample tests13. Chi-square test of independence and goodness of fit test14. Linear correlation and regression15. Multiple regression and multiple correlation16. Moderation and mediation17. Introduction to factor analysis18. Using SPSS to analyse questionnaires: reliability18. Using SPSS to analyse questionnaires: reliability

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Book SynopsisPraise for the First EditionLuck, Logic, and White Lies teaches readers of all backgrounds about the insight mathematical knowledge can bring and is highly recommended reading among avid game players, both to better understand the game itself and to improve one's skills. Midwest Book ReviewThe best book I''ve found for someone new to game math is Luck, Logic and White Lies by Jörg Bewersdorff. It introduces the reader to a vast mathematical literature, and does so in an enormously clear manner. . . Alfred Wallace, Musings, Ramblings, and Things Left UnsaidThe aim is to introduce the mathematics that will allow analysis of the problem or game. This is done in gentle stages, from chapter to chapter, so as to reach as broad an audience as possible . . . Anyone who likes games and has a taste for analytical thinking will enjoy this book. Peter Fillmore, CMS NotesLuck, Logic, and Trade Review"The book presents mathematical explanation of problems related to playing games of chance, combinatorial and strategic games, with descriptions of their historical perspectives and recreational aspects. [. . .] The author notes that people play games investigating the unknown outcomes, in amusement and hope of winning in conditions of uncertainty caused by three possible mechanisms: chance, a large number of combinations of various moves, and different states of information among the individual players. Respectively, the games can be divided to three classes: games of chance (e.g., dice, cards, roulette) where the random processes dominate the players decisions; combinatorial games (chess, go) where the uncertainty rests on the multiplicity of possible moves; and strategic games (rock-paper-scissors) where the players’ uncertainty arises from imperfect information. Many games have mixed features (backgammon, poker, skat), and the degree of influence of the three main causes of uncertainty defines specifics of each game. The book introduces mathematical methods developed for description and solutions of games: the games of chance can be analyzed with the help of probability theory, the combinatorial games are considered by variety of methods used in particular problems, and the strategic games are studied by the game theory models for decision-making in the interactive optimizing economic processes. The book is organized in four parts containing 51 chapters on various topics.[. . .] All topics are illustrated by multiple figures and numerical tables. [. . .] It can be useful to instructors, students, and readers wishing to extend understanding of the games’ intrinsic features needed to improve ability to win in actual playing."- Stan Lipovetsky, Technometrics"As the title indicates, Bewersdorff’s book is intended to span the mathematics of games in general – not only games of chance but also including strategic and skill games. The author covers all the big categories of games – casino, tournament, and house or social games. In fact, the skill-strategic dimension of the games balanced with the chance-uncertainty dimension is the central element around which the author presents games as an important field of application of mathematics; he takes them as a good opportunity to advocate for the beauty and power of mathematics. To that point, the book is written so as to be both popular and scholarly, and these attributes are not at all inconsistent with each other for such a general topic, content, and style. [. . .] The book leaves the impression of its author’s being a skilled advocate of the unlimited power of mathematics, shown through the examples of games. Not only is mathematics able to describe the games and the way we play them, but it is entitled to address fundamental questions beyond the problem-solving aspects of games and gaming. It is mainly game theory and probability theory that grant mathematics such a virtue. [. . .] Although the chapters can mostly be read independent of each other, and the mathematical content is not systematized throughout the book, the mathematically-inclined reader can put things together to have an objective overview of one of the most interesting fields in application of mathematics – games – which themselves shaped the development of mathematics."– International Gambling Studies"The author provides a great deal of insight into a wide variety of games, all inspected from a mathematical point of view. He develops the prerequisites mathematically, so that someone with a good high-school background in mathematics and a willingness to learn will be able to build up the necessary tools for successful play. Moreover, the author’s arguments are often very detailed, so that even a novice can easily follow them. The numerous diagrams also help.I find Bewersdorff's writing to be clear and detailed. He has taken care in the presentation of the ideas. The book, the size of which has now grown to 568 pages, provides a great deal of information, and the reader can easily pick and choose topics of interest without having to absorb the entire treatise. The level of Mathematical skill needed, however, does vary greatly from chapter to chapter. When necessary, the reader can make use of previous chapters to develop the required background to proceed. To the prospective reader, good luck, and may your play be a winning one!"– The Mathematical IntelligencerThis book, successor to the first edition (2005) and translated from the 7th German edition, treats games of chance (“luck”), combinatorial games (“logic”), and games of strategy (bluff, or “white lies”). The first part develops succinctly the needed theory of probability and investigates the nature of randomness. The second part explores minimax optimization, Grundy values, Conway’s theory of games, and complexity theory. The third part is based on the fact that in a symmetric two-person zero-sum game, the players are guaranteed optimal mixed strategies; for some games, finding such strategies can be done by linear programming. This edition adds a fourth part that investigates measuring the proportion of skill in a game, with particular application to poker. The reader needs to be comfortable with algebra and summation signs, and infinite series make appearances; end-of-chapter notes and footnotes contribute further mathematical depth.– Mathematics Magazine, MAA"Exceptionally well written, organized and presented, Luck, Logic, and White Lies: The Mathematics of Games is a unique and unreservedly recommended addition to professional, community, college, and university library Game Theory & Mathematics collections."– Midwest Books Review"A great variety of games are analyzed in an accessible way. The treatment of blackjack, in particular, is superb."– Stewart Ethier, Professor Emeritus, University of Utah and author of The Doctrine of Chances: Probabilistic Aspects of Gambling "People play games for fun and for profit. To become better at a game, you need to study it. In Luck, Logic and White Lies, Jörg Bewersdorff takes you, almost imperceptibly, from the history of numerous concrete games to their mathematical analysis. This touches upon a wide range of techniques, not only in mathematics, but also in computing and psychology. If you get the hang of it, you can apply these techniques to other areas of life, such as business, economics, biology, and sociology."– Tom Verhoeff, Dept. Math & CS, Eindhoven University of TechnologyPraise for the First Edition"Luck, Logic, and White Lies teaches readers of all backgrounds about the insight mathematical knowledge can bring and is highly recommended reading among avid game players, both to better understand the game itself and to improve one's skills."– Midwest Book Review"The best book I've found for someone new to game math is Luck, Logic and White Lies by Jörg Bewersdorff. It introduces the reader to a vast mathematical literature, and does so in an enormously clear manner. . ."– Alfred Wallace, Musings, Ramblings, and Things Left Unsaid"The aim is to introduce the mathematics that will allow analysis of the problem or game. This is done in gentle stages, from chapter to chapter, so as to reach as broad an audience as possible [. . .] Anyone who likes games and has a taste for analytical thinking will enjoy this book."– Peter Fillmore, CMS NotesTable of ContentsI. Games of Chance. 1. Dice and Probability. 2. Waiting for a Double. 3. Tips on Playing the Lottery: More Equal Than Equal? 4. A Fair Division: But How? 5. The Red and the Black: The Law of Large Numbers. 6. Asymmetric Dice: Are They Worth Anything? 7. Probability and Geometry. 8. Chance and Mathematical Certainty: Are They Reconcilable? 9. In Quest of the Equiprobable. 10. Winning the Game: Probability and Value. 11. Which Die Is Best? 12. A Die Is Tested. 13. The Normal Distribution: A Race to the Finish! 14. And Not Only at Roulette: The Poisson Distribution. 15. When Formulas Become Too Complex: The Monte Carlo Method. 16. Markov Chains and the Game Monopoly. 17 Blackjack: A Las Vegas Fairy Tale. II. Combinatorial Games. 18. Which Move Is Best? 19. Chances of Winning and Symmetry. 20. A Game for Three. 21. Nim: The Easy Winner! 22. Lasker Nim: Winning Along a Secret Path. 23. Black-and-White Nim: To Each His (or Her) Own. 24. A Game with Dominoes: Have We Run Out of Space Yet? 25. Go: A Classical Game with a Modern Theory. 26. Misere Games: Loser Wins! 27. The Computer as Game Partner. 28. Can Winning Prospects Always Be Determined? 29. Games and Complexity: When Calculations Take Too Long. 30. A Good Memory and Luck: And Nothing Else? 31. Backgammon: To Double or Not to Double? 32. Mastermind: Playing It Safe. III. Strategic Games. 33. Rock–Paper–Scissors: The Enemy's Unknown Plan. 34. Minimax Versus Psychology: Even in Poker? 35. Bluffing in Poker: Can It Be Done Without Psychology? 36. Symmetric Games: Disadvantages Are Avoidable, but How? 37. Minimax and Linear Optimization: As Simple as Can Be. 38. Play It Again, Sam: Does Experience Make Us Wiser? 39. Le Her: Should I Exchange? 40. Deciding at Random: But How? 41. Optimal Play: Planning Efficiently. 42. Baccarat: Draw from a Five? 43. Three-Person Poker: Is It a Matter of Trust? 44 QUAAK! Child's Play? 45 Mastermind: Color Codes and Minimax. 46. A Car, Two Goats–and a Quizmaster. IV. Epilogue: Chance, Skill, and Symmetry. 47. A Player's Inuence and Its Limits. 48. Games of Chance and Games of Skill. 49. In Quest of a Measure. 50. Measuring the Proportion of Skill. 51. Poker: The Hotly Debated Issue.

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Book SynopsisAnalysis of Categorical Data with R, Second Editionpresents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them.The second edition is a substantial update of the first based on the authors' experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chaptergroup testing and splines. The computing has been completely updated, with the emmeans package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The s

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Book SynopsisThis book is designed to provide practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate text into their modeling pipelines. We assume that the reader is somewhat familiar with R, predictive modeling concepts for non-text data, and the tidyverse family of packages.Trade Review"I find this book very useful, as predictive modelling with text is an important field in data science and statistics, and yet the one that has been consistently under-represented in technical literature. Given the growing volume, complexity and accessibility of unstructured data sources, as well as the rapid development of NLP algorithms, knowledge and skills in this domain is in increasing demand. In particular, there’s a demand for pragmatic guidelines that offer not just the theoretical background to the NLP issues but also explain the end-to-end modelling process and good practices supported with code examples, just like "Supervised Machine Learning for Text Analysis in R" does. Data scientists and computational linguists would be a prime audience for this kind of publication and would most likely use it as both, (coding) reference and a textbook."~Kasia Kulma, data science consultant"This book fills a critical gap between the plethora of text mining books (even in R) that are too basic for practical use and the more complex text mining books that are not accessible to most data scientists. In addition, this book uses statistical techniques to do text mining and text prediction and classification. Not all text mining books take this approach, and given the level of this book, it is one of its strongest features."~Carol Haney, Quatrics"This book would be valuable for advanced undergraduates and early PhD students in a wide range of areas that have started using text as data…The main strength of the book is its connection to the tidyverse environment in R. It's relatively easy to pick up and do powerful things."~David Mimno, Cornell University"The authors do a great job of presenting R programmers a variety of deep learning applications to text-based problems. Perhaps one of the best parts of this book is the section on interpretability, where the authors showcase methods to diagnose features on which these complex models rely to make their prediction. Considering how important the area of interpretability is to natural language processing research and is often skipped in applied textbooks, the authors should be commended for incorporating it in this book."~Kanishka Misra, Purdue University"In conclusion, the presented book is extremely useful for graduate students, advanced researchers, and practitioners of statistics and data science who are interested in learning cutting-edge supervised ML techniques for text data. By utilizing the tidyverse environment and providing easy-to-understand R code examples with detailed study cases of real-world text mining problems, this book stands out and is a worthwhile read."-Han-Ming Wu, National Chengchi University, Biometrics, September 2022"The volume is a valuable methodological resource, primarily for students interested in data science, concerned with: understanding the fundamentals of preprocessing steps required to transform a corpus, not always large, into a structure that is a good fit for modeling; implementation of machine learning and deep learning algorithms for building text predictive models under given research contexts in which they have to be integrated."-Anca Vitcu in ISCB Book Reviews, September 2022Table of Contents1. Language and modeling. 2. Tokenization. 3. Stop words. 4. Stemming. 5. Word Embeddings. 6. Regression. 7. Classification. 8. Dense neural networks. 9. Long short-term memory (LSTM) networks. 10. Convolutional neural networks.

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Book SynopsisA systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book areTrade Review"The PsychoSystems team at the University of Amsterdam has sparked a conceptual and methodological revolution in psychology. Their network approach to mental disorders is galvanizing our field, producing an urgent need for an accessible, user-friendly text for novices as well as for experienced researchers. Network Psychometrics with R is a splendid book that fulfills this need admirably. Importantly, the authors are seasoned teachers of network analysis, accustomed to introducing the approach to beginners in the field." -- Professor Richard McNally, Harvard University, USA"This thorough introduction into all important details of network psychometrics, by a group of authors including many of the leading scientists in the field, fills an important lacuna in the literature. It is highly recommended for widespread use in teaching and applied research." -- Professor Peter Molenaar, Pennsylvania State University, USA"The PsychoSystems team at the University of Amsterdam has sparked a conceptual and methodological revolution in psychology. Their network approach to mental disorders is galvanizing our field, producing an urgent need for an accessible, user-friendly text for novices as well as for experienced researchers. Network Psychometrics with R is a splendid book that fulfills this need admirably. Importantly, the authors are seasoned teachers of network analysis, accustomed to introducing the approach to beginners in the field." Professor Richard McNally, Harvard University, USA"This thorough introduction into all important details of network psychometrics, by a group of authors including many of the leading scientists in the field, fills an important lacuna in the literature. It is highly recommended for widespread use in teaching and applied research." Professor Peter Molenaar, Pennsylvania State University, USATable of ContentsI: Network Science in R 1 Network Perspectives 2 Short Introduction to R 3 Descriptive Analysis of Network Structures 4 Constructing and Drawing Networks in qgraph 5 Association and Conditional Independence; II: Estimating Undirected Network Models 6 Pairwise Markov Random Fields 7 Estimating Network Structures using Model Selection 8 Network Stability, Comparison, and Replicability; III: Network Models for Longitudinal Data 9 Longitudinal Design Choices: Relating Data to Analysis 10 Network Estimation from Time Series and Panel Data 11 Modeling Change in Networks; IV: Theory and Causality 12 Causal Inference 13 Idealized Modeling of Psychological Dynamics

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Book SynopsisThe book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.In summary, this book provides a comprehensive technological path from fundamentalTable of Contents1. Introduction 2. Linear Algebra 3. Machine Learning 4. Some Practical Notes 5. Deep Learning 6. Generative Adversarial Networks 7. Implementation

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Book SynopsisData graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book presents a practical approach to data visualisation with real applications front and centre.The first part of the book is a series of case studies, each describing a graphical analysis of a real dataset. The second part pulls together ideas from the case studies and provides an overview of the main factors affecting understanding graphics.Key Features: Explains how to get insights from graphics. Emphasises the value of drawing many graphics. Underlines the importance for analysis of background knowledge and context. Readers may be data scientists, statisticians or people who want to become more visually literate. A knowledge of Statistics is not required, just an interest in data graphics and some experience of working with data. It will help if the reader knows something of basic graphic forms such as barcharts, histograms, and scatterplots.

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Book SynopsisBased on the syllabus of the actuarial profession courses on general insurance pricing â with additional material inspired by the authorâs own experience as a practitioner and lecturer â Pricing in General Insurance, Second Edition presents pricing as a formalised process that starts with collecting information about a particular policyholder or risk and ends with a commercially informed rate. The first edition of the book proved very popular among students and practitioners with its pragmatic approach, informal style, and wide-ranging selection of topics, including: Background and context for pricing Process of experience rating, ranging from traditional approaches (burning cost analysis) to more modern approaches (stochastic modelling) Exposure rating for both property and casualty products Specialised techniques for personal lines (e.g., GLMs), reinsurance, and specific products such as credit risk and weatheTable of Contents1.The Pricing Process: A Gentle Start. 2. Insurance and Reinsurance Products. 3.The Cover Structure.4. The Insurance Markets. 5. Pricing in Context. 6. The Scientific Basis for Pricing: Risk Theory. 7. Familiarise Yourself with the Risk. 8. Data Requirements for Pricing. 9. Setting the Loss Inflation Assumptions. 10. Data Preparation. 11. The Burning Cost Approach. 12. What Is This Thing Called Modelling? A Gentle Introduction to Machine Learning. 13. Frequency Modelling: Adjusting for Claim Count IBNR. 14. Frequency Modelling: Selecting and Calibrating a Frequency Model. 15. Severity Modelling: Adjusting for IBNER and Other Factors. 16. Severity Modelling: Selecting and Calibrating a Severity Model. 17. Aggregate Loss Modelling. 18. Identifying, Measuring, and Communicating Uncertanity. 19. Setting the Premium. 20. The Pricing Cycle and Rate Change Calculations. 21. Experience Rating for Non-Proportional Reinsurance. 22. Exposure Rating for Property Insurance. 23. Liability Rating Using Increased Limit Factor Curves. 24. Pricing Considerations for Specific Lines of Business. 25. Catastrophe Modelling in Pricing. 26. Credibilty Theory. 27. Rating Factor Selection and Calibration: GLMs, GAMs, and Regularisation. 28. Multilevel Factors and Smoothing. 29. Pricing Multiple Lines of Business and Risks. 30. Insurance Structure Optimisation. 31. An Introduction to Pricing Models.

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Book SynopsisRasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques, demonstrations of analyses using several R packages that contain Rasch measurement functions, and sample interpretations of results. Features: Accessible to users with relatively little experience with R programming Reproducible data analysis examples that can be modified to accommodate usersâ own data Accompanying e-book website with links to additional resources and R code updates as needed Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines This book is designed for graduate students, researchers, and practitioners across the social, health, and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory, including evaluating data for adherence to measurement requirements, applying the dichotomous, Rating Scale, Partial Credit, and Many-Facet Rasch models, examining data for evidence of differential item functioning, and considering potential interpretations of results from such analyses.Trade ReviewOver 60 years ago, Georg Rasch introduced a fundamentally new way of viewing measurement theory into the social sciences. His approach to invariant measurement provides the opportunity to achieve sample-free calibration of items and item-free measurement of persons. His research remains the gold standard for developing psychometrically sound assessments. Stefanie A. Wind and Cheng Hua introduce Rasch's fundamental ideas to students, researchers, and practitioners using readily available software in R that facilitates the quest for invariant measurement. ­-George Engelhard, University of GeorgiaTable of Contents1 Introduction 2 Dichotomous Rasch Model 3 Evaluating the Quality of Measures 4 Rating Scale Model 5 Partial Credit Model 6 Many Facet Rasch Model 7 Basics of Differential Item Functioning

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Book SynopsisA leading data visualisation expert explores the negative—and positive-influences that charts have on our perception of truthTrade Review"[Alberto Cairo's] book reminds readers not to infer too much from a chart, especially when it shows them what they already wanted to see. Mr Cairo has sent a copy to the White House." -- The Economist

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Book SynopsisData, data, data: It''s all one ever hears about these days. Science is all about big data. Our bosses call out for analytics, whatever those might be. And everyone wants to predict what will happen next. Can we accurately predict if a company''s stock will rise, whether or not a disease will spread, or who will become the next President of the United States? As anyone who has ever opened up a spreadsheet groaning with weeks, months, or years of data knows, numbers aren''t enough: we have to know how to make them talk.Enter Scott Page and The Model Thinker. A leading professor of quantitative social science at the University of Michigan, he has taken his expertise as both a teacher and researcher and distilled it into the one book anyone will need to master data and turn it to professional use. This is no armchair exercise in imagined understanding, like The Signal and the Noise or The Black Swan or a legion of books on networks, the purposes of which ar

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Book SynopsisAnalysis of Financial Time Series, Third Edition provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.Trade Review"Analysis of financial time series, third edition, is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level." (Mathematical Reviews, 2011) "Nevertheless, all in all the book can be a very useful reference for students as well as for professionals." (Zentralblatt MATH, 2011) "Factor models, an important technique used in quantitative finance, are given a full treatment with macroeconomic factor models and fundamental factor models. The coverage of the book is comprehensive. It starts from basic time series techniques and finishes with advanced concepts such as state space models and MCMC methods. There is a balance between the theoretical background necessary to appreciate the nuances and the practical aspect of implementation. More importantly it gives insights about what time series models can't address. The book has an excellent supporting website which has all the programs and data sets which helps to internalize the concepts. Finally, teaching professionals should find the solutions manual as a valuable tool to explain concepts and to ensure understanding." (BookPleasures.com, January 2011) "This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described." (Insurance News Net, 8 December 2010)Table of ContentsPreface xvii Preface to the Second Edition xix Preface to the First Edition xxi 1 Financial Time Series and Their Characteristics 1 1.1 Asset Returns, 2 1.2 Distributional Properties of Returns, 7 1.3 Processes Considered, 22 2 Linear Time Series Analysis and Its Applications 29 2.1 Stationarity, 30 2.2 Correlation and Autocorrelation Function, 30 2.3 White Noise and Linear Time Series, 36 2.4 Simple AR Models, 37 2.5 Simple MA Models, 57 2.6 Simple ARMA Models, 64 2.7 Unit-Root Nonstationarity, 71 2.8 Seasonal Models, 81 2.9 Regression Models with Time Series Errors, 90 2.10 Consistent Covariance Matrix Estimation, 97 2.11 Long-Memory Models, 101 3 Conditional Heteroscedastic Models 109 3.1 Characteristics of Volatility, 110 3.2 Structure of a Model, 111 3.3 Model Building, 113 3.4 The ARCH Model, 115 3.5 The GARCH Model, 131 3.6 The Integrated GARCH Model, 140 3.7 The GARCH-M Model, 142 3.8 The Exponential GARCH Model, 143 3.9 The Threshold GARCH Model, 149 3.10 The CHARMA Model, 150 3.11 Random Coefficient Autoregressive Models, 152 3.12 Stochastic Volatility Model, 153 3.13 Long-Memory Stochastic Volatility Model, 154 3.14 Application, 155 3.15 Alternative Approaches, 159 3.16 Kurtosis of GARCH Models, 165 4 Nonlinear Models and Their Applications 175 4.1 Nonlinear Models, 177 4.2 Nonlinearity Tests, 205 4.3 Modeling, 214 4.4 Forecasting, 215 4.5 Application, 218 5 High-Frequency Data Analysis and Market Microstructure 231 5.1 Nonsynchronous Trading, 232 5.2 Bid–Ask Spread, 235 5.3 Empirical Characteristics of Transactions Data, 237 5.4 Models for Price Changes, 244 5.5 Duration Models, 253 5.6 Nonlinear Duration Models, 264 5.7 Bivariate Models for Price Change and Duration, 265 5.8 Application, 270 6 Continuous-Time Models and Their Applications 287 6.1 Options, 288 6.2 Some Continuous-Time Stochastic Processes, 288 6.3 Ito's Lemma, 292 6.4 Distributions of Stock Prices and Log Returns, 297 6.5 Derivation of Black–Scholes Differential Equation, 298 6.6 Black–Scholes Pricing Formulas, 300 6.7 Extension of Ito's Lemma, 309 6.8 Stochastic Integral, 310 6.9 Jump Diffusion Models, 311 6.10 Estimation of Continuous-Time Models, 318 7 Extreme Values, Quantiles, and Value at Risk 325 7.1 Value at Risk, 326 7.2 RiskMetrics, 328 7.3 Econometric Approach to VaR Calculation, 333 7.4 Quantile Estimation, 338 7.5 Extreme Value Theory, 342 7.6 Extreme Value Approach to VaR, 353 7.7 New Approach Based on the Extreme Value Theory, 359 7.8 The Extremal Index, 377 8 Multivariate Time Series Analysis and Its Applications 389 8.1 Weak Stationarity and Cross-Correlation Matrices, 390 8.2 Vector Autoregressive Models, 399 8.3 Vector Moving-Average Models, 417 8.4 Vector ARMA Models, 422 8.5 Unit-Root Nonstationarity and Cointegration, 428 8.6 Cointegrated VAR Models, 432 8.7 Threshold Cointegration and Arbitrage, 442 8.8 Pairs Trading, 446 9 Principal Component Analysis and Factor Models 467 9.1 A Factor Model, 468 9.2 Macroeconometric Factor Models, 470 9.3 Fundamental Factor Models, 476 9.4 Principal Component Analysis, 483 9.5 Statistical Factor Analysis, 489 9.6 Asymptotic Principal Component Analysis, 498 10 Multivariate Volatility Models and Their Applications 505 10.1 Exponentially Weighted Estimate, 506 10.2 Some Multivariate GARCH Models, 510 10.3 Reparameterization, 516 10.4 GARCH Models for Bivariate Returns, 521 10.5 Higher Dimensional Volatility Models, 537 10.6 Factor–Volatility Models, 543 10.7 Application, 546 10.8 Multivariate t Distribution, 548 11 State-Space Models and Kalman Filter 557 11.1 Local Trend Model, 558 11.2 Linear State-Space Models, 576 11.3 Model Transformation, 577 11.4 Kalman Filter and Smoothing, 591 11.5 Missing Values, 600 11.6 Forecasting, 601 11.7 Application, 602 12 Markov Chain Monte Carlo Methods with Applications 613 12.1 Markov Chain Simulation, 614 12.2 Gibbs Sampling, 615 12.3 Bayesian Inference, 617 12.4 Alternative Algorithms, 622 12.5 Linear Regression with Time Series Errors, 624 12.6 Missing Values and Outliers, 628 12.7 Stochastic Volatility Models, 636 12.8 New Approach to SV Estimation, 649 12.9 Markov Switching Models, 660 12.10 Forecasting, 666 12.11 Other Applications, 669 Exercises, 670 References, 671 Index 673

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Book Synopsis* Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.Table of ContentsChapter 1 The Role of Statistics in Engineering 1 1-1 The Engineering Method and Statistical Thinking 2 1-2 Collecting Engineering Data 6 1-2.1 Retrospective Study 7 1-2.2 Observational Study 8 1-2.3 Designed Experiments 9 1-2.4 Random Samples 12 1-3 Mechanistic and Empirical Models 15 1-4 Observing Processes Over Time 17 Chapter 2 Data Summary and Presentation 23 2-1 Data Summary and Display 24 2-2 Stem-and-Leaf Diagram 29 2-3 Histograms 34 2-4 Box Plot 39 2-5 Time Series Plots 41 2-6 Multivariate Data 46 Chapter 3 Random Variables and Probability Distributions 57 3-1 Introduction 58 3-2 Random Variables 60 3-3 Probability 62 3-4 Continuous Random Variables 66 3-4.1 Probability Density Function 66 3-4.2 Cumulative Distribution Function 68 3-4.3 Mean and Variance 70 3-5 Important Continuous Distributions 74 3-5.1 Normal Distribution 74 3-5.2 Lognormal Distribution 84 3-5.3 Gamma Distribution 86 3-5.4 Weibull Distribution 86 3-5.5 Beta Distribution 88 3-6 Probability Plots 92 3-6.1 Normal Probability Plots 92 3-6.2 Other Probability Plots 94 3-7 Discrete Random Variables 97 3-7.1 Probability Mass Function 97 3-7.2 Cumulative Distribution Function 98 3-7.3 Mean and Variance 99 3-8 Binomial Distribution 102 3-9 Poisson Process 109 3-9.1 Poisson Distribution 109 3-9.2 Exponential Distribution 113 3-10 Normal Approximation to the Binomial and Poisson Distributions 119 3-11 More than One Random Variable and Independence 123 3-11.1 Joint Distributions 123 3-11.2 Independence 124 3-12 Functions of Random Variables 129 3-12.1 Linear Functions of Independent Random Variables 130 3-12.2 Linear Functions of Random Variables That are Not Independent 131 3-12.3 Nonlinear Functions of Independent Random Variables 133 3-13 Random Samples, Statistics, and the Central Limit Theorem 136 Chapter 4 Decision Making for a Single Sample 148 4-1 Statistical Inference 149 4-2 Point Estimation 150 4-3 Hypothesis Testing 156 4-3.1 Statistical Hypotheses 156 4-3.2 Testing Statistical Hypotheses 158 4-3.3 P-Values in Hypothesis Testing 164 4-3.4 One-Sided and Two-Sided Hypotheses 166 4-3.5 General Procedure for Hypothesis Testing 167 4-4 Inference on the Mean of a Population, Variance Known 169 4-4.1 Hypothesis Testing on the Mean 169 4-4.2 Type II Error and Choice of Sample Size 173 4-4.3 Large-Sample Test 177 4-4.4 Some Practical Comments on Hypothesis Testing 177 4-4.5 Confidence Interval on the Mean 178 4-4.6 General Method for Deriving a Confidence Interval 184 4-5 Inference on the Mean of a Population, Variance Unknown 186 4-5.1 Hypothesis Testing on the Mean 187 4-5.2 Type II Error and Choice of Sample Size 193 4-5.3 Confidence Interval on the Mean 195 4-6 Inference on the Variance of a Normal Population 199 4-6.1 Hypothesis Testing on the Variance of a Normal Population 199 4-6.2 Confidence Interval on the Variance of a Normal Population 203 4-7 Inference on a Population Proportion 205 4-7.1 Hypothesis Testing on a Binomial Proportion 205 4-7.2 Type II Error and Choice of Sample Size 208 4-7.3 Confidence Interval on a Binomial Proportion 210 4-8 Other Interval Estimates for a Single Sample 216 4-8.1 Prediction Interval 216 4-8.2 Tolerance Intervals for a Normal Distribution 217 4-9 Summary Tables of Inference Procedures for a Single Sample 219 4-10 Testing for Goodness of Fit 219 Chapter 5 Decision Making for Two Samples 230 5-1 Introduction 231 5-2 Inference on the Means of Two Populations, Variances Known 232 5-2.1 Hypothesis Testing on the Difference in Means, Variances Known 233 5-2.2 Type II Error and Choice of Sample Size 234 5-2.3 Confidence Interval on the Difference in Means, Variances Known 235 5-3 Inference on the Means of Two Populations, Variances Unknown 239 5-3.1 Hypothesis Testing on the Difference in Means 239 5-3.2 Type II Error and Choice of Sample Size 246 5-3.3 Confidence Interval on the Difference in Means 247 5-4 The Paired t-Test 252 5-5 Inference on the Ratio of Variances of Two Normal Populations 259 5-5.1 Hypothesis Testing on the Ratio of Two Variances 259 5-5.2 Confidence Interval on the Ratio of Two Variances 263 5-6 Inference on Two Population Proportions 265 5-6.1 Hypothesis Testing on the Equality of Two Binomial Proportions 265 5-6.2 Type II Error and Choice of Sample Size 268 5-6.3 Confidence Interval on the Difference in Binomial Proportions 269 5-7 Summary Tables for Inference Procedures for Two Samples 271 5-8 What if We Have More than Two Samples? 272 5-8.1 Completely Randomized Experiment and Analysis of Variance 272 5-8.2 Randomized Complete Block Experiment 281 Chapter 6 Building Empirical Models 298 6-1 Introduction to Empirical Models 299 6-2 Simple Linear Regression 304 6-2.1 Least Squares Estimation 304 6-2.2 Testing Hypotheses in Simple Linear Regression 312 6-2.3 Confidence Intervals in Simple Linear Regression 315 6-2.4 Prediction of a Future Observation 318 6-2.5 Checking Model Adequacy 319 6-2.6 Correlation and Regression 322 6-3 Multiple Regression 326 6-3.1 Estimation of Parameters in Multiple Regression 326 6-3.2 Inferences in Multiple Regression 331 6-3.3 Checking Model Adequacy 336 6-4 Other Aspects of Regression 344 6-4.1 Polynomial Models 344 6-4.2 Categorical Regressors 346 6-4.3 Variable Selection Techniques 348 Chapter 7 Design of Engineering Experiments 360 7-1 The Strategy of Experimentation 361 7-2 Factorial Experiments 362 7-3 2k Factorial Design 365 7-3.1 22 Design 366 7-3.2 Statistical Analysis 368 7-3.3 Residual Analysis and Model Checking 374 7-3.4 2k Design for k ≥ 3 Factors 376 7-3.5 Single Replicate of a 2k Design 382 7-4 Center Points and Blocking in 2k Designs 390 7-4.1 Addition of Center Points 390 7-4.2 Blocking and Confounding 393 7-5 Fractional Replication of a 2k Design 398 7-5.1 One-Half Fraction of a 2k Design 398 7-5.2 Smaller Fractions: 2k-pFractional Factorial Designs 404 7-6 Response Surface Methods and Designs 414 7-6.1 Method of Steepest Ascent 416 7-6.2 Analysis of a Second-Order Response Surface 418 7-7 Factorial Experiments With More Than Two Levels 424 Chapter 8 Statistical Process Control 438 8-1 Quality Improvement and Statistical Process Control 439 8-2 Introduction to Control Charts 440 8-2.1 Basic Principles 440 8-2.2 Design of a Control Chart 444 8-2.3 Rational Subgroups 446 8-2.4 Analysis of Patterns on Control Charts 447 8-3 X̄ and R Control Charts 449 8-4 Control Charts For Individual Measurements 456 8-5 Process Capability 461 8-6 Attribute Control Charts 465 8-6.1 P Chart (Control Chart for Proportions) and nP Chart 465 8-6.2 U Chart (Control Chart for Average Number of Defects per Unit) and C Chart 467 8-7 Control Chart Performance 470 8-8 Measurement Systems Capability 473 Appendices 483 Appendix A Statistical Tables and Charts 485 Appendix B Bibliography 500 Appendix C* Answers to Selected Exercises 502 Index 511

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Book SynopsisWhile the literature on reinsurance is vast, there is currently no comprehensive treatment of the major actuarial and financial aspects of the subject. Many publications deal with specific aspects of the theory without putting them into a proper perspective. Reinsurance: Actuarial and Statistical Aspects treats the topic differently.Table of ContentsPreface ix 1 Introduction 1 1.1 What is Reinsurance? 1 1.2 Why Reinsurance? 2 1.3 Reinsurance Data 4 1.3.1 Case Study I: Motor Liability Data 5 1.3.2 Case Study II: Dutch Fire Insurance Data 10 1.3.3 Case Study III: Austrian Storm Claim Data 10 1.3.4 Case Study IV: European Flood Risk Data 11 1.3.5 Case Study V: Groningen Earthquakes 12 1.3.6 Case Study VI: Danish Fire Insurance Data 12 1.4 Notes and Bibliography 16 2 Reinsurance Forms and their Properties 19 2.1 Quota-share Reinsurance 19 2.1.1 Some Practical Considerations 20 2.2 Surplus Reinsurance 21 2.3 Excess-of-loss Reinsurance 24 2.3.1 Moment Calculations 25 2.3.2 Reinstatements 27 2.3.3 Further Practical Considerations 29 2.4 Stop-loss Reinsurance 30 2.5 Large Claim Reinsurance 31 2.6 Combinations of Reinsurance Forms and Global Protections 32 2.7 Facultative Contracts 33 2.8 Notes and Bibliography 33 3 Models for Claim Sizes 35 3.1 Tails of Distributions 35 3.2 Large Claims 36 3.3 Common Claim Size Distributions 40 3.3.1 Light-tailed Models 41 3.3.2 Heavy-tailed Models 44 3.4 Mean Excess Analysis 49 3.5 Full Models: Splicing 50 3.6 Multivariate Modelling of Large Claims 52 4 Statistics for Claim Sizes 59 4.1 Heavy or Light Tails: QQ- and Derivative Plots 60 4.2 Large Claims Modelling through Extreme Value Analysis EVA for Pareto-type Tails 63 4.2.1 EVA for Pareto-type Tails 63 4.2.2 General Tail Modelling using EVA 82 4.2.3 EVA under Upper-truncation 91 4.3 Global Fits: Splicing, Upper-truncation and Interval Censoring 97 4.3.1 Tail-mixed Erlang Splicing 97 4.3.2 Tail-mixed Erlang Splicing under Censoring and Upper-truncation 99 4.4 Incorporating Covariate Information 114 4.4.1 Pareto-type Modelling 114 4.4.2 Generalized Pareto Modelling 116 4.4.3 Regression Extremes with Censored Data 119 4.5 Multivariate Analysis of Claim Distributions 123 4.5.1 The Multivariate POT Approach 124 4.5.2 Multivariate Mixtures of Erlangs 125 4.6 Estimation of Other Tail Characteristics 128 4.7 Further Case Studies 132 4.8 Notes and Bibliography 137 5 Models for Claim Counts 139 5.1 General Treatment 139 5.1.1 Main Properties of the Claim Number Process 140 5.2 The Poisson Process and its Extensions 141 5.2.1 The Homogeneous Poisson Process 141 5.2.2 Inhomogeneous Poisson Processes 143 5.2.3 Mixed Poisson Processes 144 5.2.4 Doubly Stochastic Poisson Processes 149 5.3 Other Claim Number Processes 157 5.3.1 The Nearly Mixed Poisson Model 157 5.3.2 Infinitely Divisible Processes 158 5.3.3 The Renewal Model 160 5.3.4 Markov Models 161 5.4 Discrete Claim Counts 161 5.5 Statistics of Claim Counts 164 5.5.1 Modelling Yearly Claim Counts 164 5.5.2 Modelling the Claim Arrival Process 172 5.6 Claim Numbers under Reinsurance 183 5.6.1 Number of Claims under Excess-loss Reinsurance 183 5.7Notes and Bibliography 187 6 Total Claim Amount 189 6.1 General Formulas for Aggregating Independent Risks 189 6.2 Classical Approximations for the Total Claim Size 191 6.2.1 Approximations based on the First Few Moments 191 6.2.2 Asymptotic Approximations for Light-tailed Claims 193 6.2.3 Asymptotic Approximations for Heavy-tailed Claims 198 6.3 Panjer Recursion 199 6.4 Fast Fourier Transform 200 6.5 Total Claim Amount under Reinsurance 201 6.5.1 Proportional Reinsurance 201 6.5.2 Excess-loss Reinsurance 202 6.5.3 Stop-loss Reinsurance 204 6.6 Numerical Illustrations 206 6.7 Aggregation for Dependent Risks 208 6.8 Notes and Bibliography 212 7 Reinsurance Pricing 217 7.1 Classical Principles of Premium Calculation 219 7.2 Solvency Considerations 219 7.2.1 The Ruin Probability 223 7.2.2 One-year Time Horizon and Cost of Capital 226 7.3 Pricing Proportional Reinsurance 228 7.4 Pricing Non-proportional Reinsurance 229 7.4.1 Exposure Rating 229 7.4.2 Experience Rating 232 7.4.3 Aggregate Pure Premium 234 7.5 The Aggregate Risk Margin 235 7.6 Leading and Following Reinsurers 237 7.7 Notes and Bibliography 238 8 Choice of Reinsurance 241 8.1 Decision Criteria 243 8.2 Classical Optimality Results 245 8.2.1 Pareto-optimal Risk Sharing 245 8.2.2 Stochastic Ordering 247 8.2.3 Minimizing Retained Variance 248 8.2.4 Maximizing Expected Utility 251 8.2.5 Minimizing the Ruin Probability 253 8.2.6 Combining Reinsurance Treaties over Subportfolios 8.3 Solvency Constraints and Cost of Capital 259 8.4 Minimizing Other Risk Measures 261 8.5 Combining Reinsurance Treaties 262 8.6 Reinsurance Chains 263 8.7 Dynamic Reinsurance 264 8.8 Beyond Piecewise Linear Contracts 266 8.9 Notes and Bibliography 268 9 Simulation 273 9.1 The Monte Carlo Method 273 9.2 Variance Reduction Techniques 276 9.2.1 Conditional Monte Carlo 277 9.2.2 Importance Sampling 277 9.3 Quasi-Monte Carlo Techniques 283 9.4 Notes and Bibliography 288 10 Further Topics 291 10.1 More on Large Claim Reinsurance 291 10.1.1 The Ordered Claims 291 10.1.2 Large Claim Reinsurance 296 10.1.3 ECOMOR 298 10.2 Alternative Risk Transfer 300 10.2.1 Notes and Bibliography 304 10.3 Reinsurance and Finance 305 10.4 Catastrophic Risk 306 References 309 Index 347

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Book SynopsisPrepare for exams and succeed in your mathematics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in MATHEMATICAL STATISTICS WITH APPLICATIONS, 7th Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples.

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Book SynopsisThis key book is a rigorous account which has Doob's theory of martingales in discrete time as its main theme. A distinguishing feature of this study is its determination to keep the probability flowing at a nice tempo. It achieves this by being selective rather than encyclopaedic, presenting only what is essential to understand the fundamentals.Trade Review'… one of the best introductions to Martingale theory.' Monatshefte für MathematikTable of Contents1. A branching-process example; Part I. Foundations: 2. Measure spaces; 3. Events; 4. Random variables; 5. Independence; 6. Integration; 7. Expectation; 8. An easy strong law: product measure; Part II. Martingale Theory: 9. Conditional expectation; 10. Martingales; 11. The convergence theorem; 12. Martingales bounded in L2; 13. Uniform integrability; 14. UI martingales; 15. Applications; Part III. Characteristic Functions: 16. Basic properties of CFs; 17. Weak convergence; 18. The central limit theorem; Appendices; Exercises.

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Book SynopsisOver the last 25 years K-theory has become an integrated part of the study of C*-algebras. This book gives an elementary introduction to this interesting and rapidly growing area of mathematics. Fundamental to K-theory is the association of a pair of Abelian groups, K0(A) and K1(A), to each C*-algebra A. These groups reflect the properties of A in many ways. This book covers the basic properties of the functors K0 and K1 and their interrelationship. Applications of the theory include Elliott's classification theorem for AF-algebras, and it is shown that each pair of countable Abelian groups arises as the K-groups of some C*-algebra. The theory is well illustrated with 120 exercises and examples, making the book ideal for beginning graduate students working in functional analysis, especially operator algebras, and for researchers from other areas of mathematics who want to learn about this subject.Trade Review'The textbook is a nice introduction to the subject preparing the ground for the study of more advanced texts.' H. Schröder, Zentralblatt für MathematikTable of ContentsPreface; 1. C*-algebra theory; 2. Projections and unitary elements; 3. The K0-group of a unital C*-algebra; 4. The functor K0; 5. The ordered Abelian group K0(A); 6. Inductive limit C*-algebras; 7. Classification of AF-algebras; 8. The functor K1; 9. The index map; 10. The higher K-functors; 11. Bott periodicity; 12. The six-term exact sequence; 13. Inductive limits of dimension drop algebras; References; Table of K-groups; Index of symbols; General index.

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Book SynopsisAnalytical chemists must use a range of statistical tools in their treatment of experimental data to obtain reliable results. Practical Statistics for the Analytical Scientist is a manual designed to help them negotiate the daunting specialist terminology and symbols. Prepared in conjunction with the Department of Trade and Industry''s Valid Analytical Measurement (VAM) programme, this volume covers the basic statistics needed in the laboratory. It describes the statistical procedures that are most likely to be required including summary and descriptive statistics, calibration, outlier testing, analysis of variance and basic quality control procedures. To improve understanding, many examples provide the user with material for consolidation and practice. The fully worked answers are given both to check the correct application of the procedures and to provide a template for future problems. Practical Statistics for the Analytical Scientist will be welcomed by practising analytical chemisTable of ContentsIntroduction: Choosing the Correct Statistics; Descriptive Statistics; Distribution Descriptives; Probability Distributions; Confidence Limits; Accuracy and Precision; Significance Testing; Outlier Tests; The ANalysis Of VAriance; Linear Regression; Polynomial Regression; Repeatability Standard Deviation; Reproducibility Standard Deviation; Analytical Quality Control; Statistical Sampling; Appendices; Subject Index

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Book SynopsisDiscusses methods available for longitudinal data analysis in non-technical language, allowing readers to apply techniques easily to their work. Aimed at non-statisticians and researchers working in medical science and utilising longitudinal studies, the interpretation of the results of various methods of analysis is emphasised.Table of Contents1. Introduction; 2. Continuous outcome variables; 3. Continuous outcome variables – regression based methods; 4. The modelling of time; 5. Models to disentangle the between- and within-subjects relationship; 6. Causality in observational longitudinal studies; 7. Dichotomous outcome variables; 8. Categorical and count outcome variables; 9. Outcome variables with floor or ceiling effects; 10. Analysis of longitudinal intervention studies; 11. Missing data in longitudinal studies; 12. Sample size calculations; 13. Software for longitudinal data analysis.

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Book SynopsisThis graduate level textbook covers classical and modern developments in graph theory and additive combinatorics, presenting arguments as a cohesive whole. Students will appreciate the chapter summaries, many figures and exercises, as well as the complementary set of lecture videos freely available through MIT OpenCourseWare.Trade Review'Yufei Zhao does great mathematics and has an uncanny ability to explain the deepest results with clear understandable prose. For anyone interested in the seminal ideas (and their interrelationships) of recent decades - pseudorandomness, graphons, graph regularity, to name a few - this is the book to read and savor.' Joel Spencer, New York University'This impeccable book should quickly become a classic text in discrete maths. A huge selection of topics is treated elegantly, with beautiful illustrations, and in just the `right' amount of detail to arouse the interest of the reader and leave them well placed to find out more. In particular, the second half of the book is a superb introduction to additive combinatorics, which I will happily recommend to any student in this area.' Ben Green, Oxford University'This charming text gives an accessible introduction to the connected topics of extremal graph theory and modern additive combinatorics. The focus is very strongly on presenting intuition and restricting attention to the simplest possible instances of methods or classes of results, rather than aiming for maximal generality or the strongest statements; instead, references are given for further reading, or for the proofs of important theorems that are only stated here. Being highly suitable for advanced undergraduates or beginning graduate students, it fills a niche that is currently not occupied by other texts in these highly active areas of current mathematical research.' Terry Tao, University of California, Los Angeles'A valuable and readable unified treatment of a fast-moving area of combinatorics from one of the world's experts - sure to become a standard resource.' Jordan Ellenberg, University of Wisconsin-Madison'Yufei Zhao's book is a wonderful book about graph theory, additive combinatorics, and their surprising connections, involving a major theme of modern mathematics: the interplay between structure and randomness. In both areas, the book can take the curious reader, whether an advanced undergraduate or a professional mathematician, on a joyous journey from the very basics to state-of-the-art research. Yufei Zhao himself is a major player in modern research in both these areas and his presentation is a tour de force.' Gil Kalai, Hebrew University of Jerusalem and Reichman University'This is a beautiful treatment of extremal graph theory and additive combinatorics, focusing on the fruitful interplay between the two. The book covers the classical results as well as recent developments in this active area. It is a fascinating manuscript that would appeal to students and researchers with an interest in discrete mathematics, theoretical computer number theory, and related areas.' Noga Alon, Princeton University'This is a wonderful, well-written account of additive combinatorics from the graph theoretic perspective. Zhao skillfully ties in this approach to the usual statements and gives a thorough development of the subject. This book is indispensable for any serious researcher in this area. Beginners will find a thorough account of the subject with plenty of motivation. The more experienced reader will appreciate the authors' insights and elegant development of some difficult ideas.' Andrew Granville, University of MontréalTable of ContentsPreface; Notation and Conventions; Appetizer: triangles and equations; 1. Forbidding a subgraph; 2. Graph regularity method; 3. Pseudorandom graphs; 4. Graph limits; 5. Graph homomorphism inequalities; 6. Forbidding 3-term arithmetic progressions; 7. Structure of set addition; 8. Sum-product problem; 9. Progressions in sparse pseudorandom sets; References; Index.

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Book SynopsisThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results. Trade Review"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia University"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia UniversityTable of Contents1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9. Working with Profile Data. 10. Feature Selection Overview. 11. Greedy Search Methods. 12. Global Search Methods.

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Book SynopsisIf you know a little bit about financial mathematics but donât yet know a lot about programming, then C++ for Financial Mathematics is for you.C++ is an essential skill for many jobs in quantitative finance, but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step, each new idea is motivated and illustrated with concrete financial examples.As employers understand, there is more to programming than knowing a computer language. As well as covering the core language features of C++, this book teaches the skills needed to write truly high quality software. These include topics such as unit tests, debugging, design patterns and data structures. The book teaches everything you neTrade Review"OOP in C++ remains a difficult programming language and paradigm to learn and develop. There remains a shortage of high-quality, easy to follow texts in C++ for mathematical finance. Dr Armstrong has successfully produced a self-contained publication that begins with the basics and patiently guides the student to advanced object-oriented C++ programming in a quant finance setting. The writing style is very user-friendly and immediately reassures the reader, while numerous exercises allow solid progression. The author’s extensive background in academia and as a practitioner is apparent throughout; the latter being a key accomplishment in setting the book apart from others. As a teacher of mathematical and computational finance, Armstrong’s book will feature at the top of my list of recommended textbooks."— Dr. Riaz Ahmad, The Fitch Group and University College London (Departments of Mathematics and Computer Science) "OOP in C++ remains a difficult programming language and paradigm to learn and develop. There remains a shortage of high-quality, easy to follow texts in C++ for mathematical finance. Dr Armstrong has successfully produced a self-contained publication that begins with the basics and patiently guides the student to advanced object-oriented C++ programming in a quant finance setting. The writing style is very user-friendly and immediately reassures the reader, while numerous exercises allow solid progression. The author’s extensive background in academia and as a practitioner is apparent throughout; the latter being a key accomplishment in setting the book apart from others. As a teacher of mathematical and computational finance, Armstrong’s book will feature at the top of my list of recommended textbooks."— Dr. Riaz Ahmad, The Fitch Group and University College London (Departments of Mathematics and Computer Science) Table of ContentsIntroduction. Getting Started. Basic Data Types and Operators. Functions. Flow of Control. Working with Multiple Files. Unit Testing. Using C++ Classes. User-Defined Types. Monte Carlo Pricing in C++. Interfaces. Arrays, Strings, and Pointers. More Sophisticated Classes. The Portfolio Class. Delta Hedging. Debugging and Development ToolsA Matrix Class. An Overview of Templates. The Standard Template Library. Function Objects and Lambda Functions. Threads. Next Steps. Appendix: Risk-Neutral Pricing

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Book SynopsisData Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps and aplies them to creating and delivering production-grade data science projects in Python and R.This bookâs first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organizationâs security, networking,

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Book SynopsisMultiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community.Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by example

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Book SynopsisThis book presents the most commonly used techniques for the most statistical inferences based on fuzzy data. It brings together many of the main ideas used in statistical inferences in one place, based on fuzzy information including fuzzy data. This book covers a much wider range of topics than a typical introductory text on fuzzy statistics. It includes common topics like elementary probability, descriptive statistics, hypothesis tests, one-way ANOVA, control-charts, reliability systems and regression models. The reader is assumed to know calculus and a little fuzzy set theory. The conventional knowledge of probability and statistics is required.Key Features: Includes example in Mathematica and MATLAB. Contains theoretical and applied exercises for each section. Presents various popular methods for analyzing fuzzy data. The book is suitable for students and researchers in statistics, social science, engineering, and economics, and it can be used at graduate and P.h.D level.

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Book SynopsisData Integration, Manipulation and Visualization of Phylogenetic Trees introduces and demonstrates data integration, manipulation and visualization of phylogenetic trees using a suite of R packages, tidytree, treeio, ggtree and ggtreeExtra. Using the most comprehensive packages for phylogenetic data integration and visualization, contains numerous examples that can be used for teaching and learning. Ideal for undergraduate readers and researchers with a working knowledge of R and ggplot2.Key Features: Manipulating phylogenetic tree with associated data using tidy verbs Integrating phylogenetic data from diverse sources Visualizing phylogenetic data using grammar of graphics

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Book SynopsisModelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring.This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data.Features:Presents an accessible account oTable of Contents1. Survival analysis 2. Some non-parametric procedures 3. The Cox regression model 4. Model checking in the Cox regression model 5. Parametric regression models 6. Flexible parametric models 7. Model checking in parametric models 8. Time-dependent variables 9. Interval-censored survival data 10. Frailty models 11. Non-proportional hazards and institutional comparisons 12 Competing risks 13. Multiple events and event history modelling 14. Dependent censoring 15. Sample size requirements for a survival study 16. Bayesian survival analysis 17. Survival Analysis with R

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Book SynopsisThis comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking Table of ContentsPrefaceAcknowledgments1. Introduction: Experiments and Variables2. Probabilities, Likelihood, and Inference3. Fitting Bayesian Regression Models with brms4. Inspecting a ‘Single Group’ of Observations using a Bayesian Multilevel Model5. Comparing Two Groups of Observations: Factors and Contrasts6. Variation in Parameters (‘Random Effects’) and Model Comparison7. Comparing Many Groups, Interactions, and Posterior Predictive Checks8. Varying Variances, More about Priors, and Prior Predictive Checks9. Quantitative Predictors and their Interactions with Factors10. Logistic Regression and Signal Detection Theory Models11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA12. Multinomial and Ordinal Regression13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics

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Book SynopsisSimply stated, this book bridges the gap between statistics and philosophy. It does this by delineating the conceptual cores of various statistical methodologies (Bayesian/frequentist statistics, model selection, machine learning, causal inference, etc.) and drawing out their philosophical implications. Portraying statistical inference as an epistemic endeavor to justify hypotheses about a probabilistic model of a given empirical problem, the book explains the role of ontological, semantic, and epistemological assumptions that make such inductive inference possible. From this perspective, various statistical methodologies are characterized by their epistemological nature: Bayesian statistics by internalist epistemology, classical statistics by externalist epistemology, model selection by pragmatist epistemology, and deep learning by virtue epistemology. Another highlight of the book is its analysis of the ontological assumptions that underpin statistical reasoning, sucTrade Review"Statistics are being used ever more widely in AI, climate studies, medicine and other areas. Yet they are hard to understand both mathematically and conceptually. Jun Otsuka has the answer to this problem. He has a remarkable ability to explain statistical techniques clearly and accurately with a minimal use of mathematics. At the same time he gives lucid discussions of why they work. He deals not only with the long-standing controversy between Bayesianism and classical statistics, but also with such recent topics as causality and deep learning by computers. His book is the perfect guide to those perplexed by statistics."Donald Gillies, University College London Table of ContentsIntroduction 1. The Paradigm of Modern Statistics 2. Bayesian Statistics 3. Classical Statistics 4. Model Selection and Machine Learning 5. Causal Inference 6. The Ontology, Semantics, and Epistemology of Statistics

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Book SynopsisModels for Non-Modelers focuses not on how to design models but on how to understand and critically appraise them. Data and statistical models are widely used in disciplines such as epidemiology, climate science and systems design, but it can be difficult for those without the necessary training to understand and implement them.This book is for non-modelers, especially social scientists. Through extensive examination of some common models both in visual and text form, this book shows these non-modellers how to understand the problems, both in the logic and implementation of such models. It includes in-depth worked examples and boxed text for more technical aspects. It does not require the reader to have in-depth mathematical knowledge. Also working through some common models in epidemiology and climate change scholarship, it examines AI and the problem of causality.This book will be suitable for graduate students and researchers in the social sciences who woulTrade Review"Models for Non-Modelers is a very timely and up-to-date comment on a central theme of scientific work, the use of models. It helps the interested reader to better understand important challenges of knowledge production in an era of critical debate regarding the concept of truth. Furthermore, to understand the research community’s strengths and limitations to produce new knowledge, which can be useful for handling complex environmental, economic and societal challenges like climate change and pandemics. It is highly recommended for a broad audience spanning from the interested public to students and researchers in development studies, environmental sciences, public health, and many other fields of science." -- Per-Olof Östergren, Professor in Social Medicine, Lund University, Sweden"This is a mind-opening critical, and pedagogical, scrutiny of scientific reasoning about the fateful issues of our time, from "Limits to Growth" to climate change and artificial intelligence." --Göran Therborn, Professor Emeritus of Sociology, University of Cambridge, UK"Models walk a fine balance between simplicity and complexity to make it possible for us to summarize, make sense of, and sometimes predict a complicated world. How to illuminate the importance of models for scientists’ work and its communication to the public? Göran Djurfeldt’s answer is to ’teach by illustration.’ Occam’s razor of parsimony guides the author both in his style of presentation and in his approach to models themselves. This book is enlivened by both subject-specific and mathematical nuance at key intersections." -- Christopher Swader, Associate Professor of Sociology, Lund University, SwedenTable of Contents1. Limits to Growth: A Mother of all Models? 2. Epidemiological Models 3. Weather and Climate Models 4. Artificial Intelligence 5. Spatial Data and Models 6. Tragedy of the Commons 7. Conclusions: Whereto from Here?

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Book SynopsisThis book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.Trade Review"Data science is no longer the exclusive domain of computer scientists and engineers. The contributions of other stakeholders are required for taking a holistic approach to the problems that can be addressed by analysing a given dataset. Not only is this likely to lead to better solutions, but also a smoother journey to their implementation, validation and widespread adoption. However, in the same way that a computer scientist should at least gain an operational understanding of the tackled problem, the domain expert should also understand the foundations and correct use of the tools unveiling its solutions. In this context, How to Think about Data Science is an unusual book in that it provides an accessible introduction to this broad and booming discipline without sacrificing the understanding of key questions in data science. I can only recommend this book to those aspiring to acquire this knowledge and mindset."--Pedro J. Ballester, PhD, Senior Lecturer, Imperial College London; Wolfson Fellow, The Royal Society"What is the difference between a regular cook from a renowned chef? A regular cook may follow recipes and create edible dishes, but knowing which ingredients to use and how to combine them, how to cook each one and for how long, and how to finally present them is what makes all the difference. The tools and processes are important for sure, but what really provides value is being able to choose and integrate the right tools, ingredients and processes to create a terrific dish. In data science it is the same: anyone can execute a clustering or build a neural network with default parameters but what matters is to know, given a dataset, what questions can be answered, what algorithms we should use to answer each question and what ethical issues and privacy concerns should be considered; answering these questions would allow a data scientist not just to follow recipes, but to apply the right algorithms to answer the right questions while minimizing potentially discriminating outputs. This book focuses on these relevant questions. If you want to cook a terrific dish, this book will help you."--Jordi Conesa i Caralt, PhD, Associate Professor of Computer Science, Universitat Oberta de Catalunya"Today, big data influences nearly everything we do, and harnessing its enormous power remains a key driver of business analytics, research innovation, cultural revolution, and global politics. This book offers a great gateway to this broad and evolving subject by asking the right questions, introducing concepts clearly and succinctly, and making rational connections between computation and their wide ranging applications. The book also discusses important issues related to data bias, discrimination, data privacy, and security. The final chapter debates the limits of artificial intelligence and the computational, ethical, and philosophical conundrums it presents. Thought-provoking and refreshing – it is a must-read book!"-- Subhajyoti De, PhD, Associate Professor at the Center for Systems and Computational Biology, Rutgers, the State University of New JerseyTable of ContentsA bird’s-eye view and the art of asking questions. Descriptive Analytics. Predictive Analytics. How are predictive models trained and evaluated? Are our algorithms racist, sexist and discriminating? Personal data, privacy and cybersecurity. What are the limits of Artificial Intelligence?

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Book SynopsisSince the emerging discipline of engineering enterprise systems extends traditional systems engineering to develop webs of systems and systems-of-systems, the engineering management and management science communities need new approaches for analyzing and managing risk in engineering enterprise systems. Advanced Risk Analysis in Engineering Enterprise Systems presents innovative methods to address these needs.With a focus on engineering management, the book explains how to represent, model, and measure risk in large-scale, complex systems that are engineered to function in enterprise-wide environments. Along with an analytical framework and computational model, the authors introduce new protocols: the risk co-relationship (RCR) index and the functional dependency network analysis (FDNA) approach. These protocols capture dependency risks and risk co-relationships that may exist in an enterprise. MovinTrade Review"The book develops several topics in risk analysis, including models and measurement of engineering risks, capability portfolio risk analysis and management, functional dependency network analysis, and extreme-event theory. Several chapters present some tools from applied probability and statistics … A reader with main interest in statistics may find in these chapters several ideas about the use of specific statistical tools in applied engineering. Questions and exercises are provided in each chapter to help the reader understand the main topics."—Fabrizio Durante, International Statistical Review, 2014"…excellent references … appropriate for risk engineers or quality professionals wishing to gain a comprehensive understanding of the engineering or mathematics behind advanced risk analysis. … I would certainly recommend this book to anyone working in high-risk, complex environments, such as nuclear, aerospace or explosives."—Paul Naysmith, Quality World"The book is a decidedly unique and rigorous treatment of selected topics in engineering systems risk analysis and management. The narrative is notably modern and clear. The mathematical formalism is comprehensive and advanced while remaining accessible for those involved in engineering complex systems. This is foremost a book of exciting and innovative ideas for the field, exceeding what might easily have been a rote assembly of worn methods or re-introduction of the works of others. It will be of long-standing appeal to practitioners engaged in the analysis of risk in engineering enterprise systems. The book will also appeal to scholars and researchers who will benefit from the advanced and fresh thinking it offers readers. The book will improve the systems engineering community’s ability to address enterprise design risk assessment and management across a system’s lifecycle."—Professor James Lambert, Associate Director, Center for Risk Management of Engineering Systems, University of VirginiaTable of ContentsEngineering Risk Management. Perspectives on Theories of Systems and Risk. Foundations of Risk and Decision Theory. A Risk Analysis Framework in Engineering Enterprise Systems. An Index to Measure Risk Co-Relationships. Functional Dependency Network Analysis. A Decision-Theoretic Algorithm for Ranking Risk Criticality. A Model for Measuring Risk in Engineering Enterprise Systems. Random Processes and Queuing Theory. Extreme Event Theory. Prioritization Systems in Highly Networked Environments. Risks of Extreme Events in Complex Queuing Systems. Appendix. References. Index.

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