Probability and statistics Books

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Book SynopsisA Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representationthe generalized regression model (GRM)of various types of regression models. It also uses a likelihood-based approach for performing statistical inference from statistical evidence consisting of data and its statistical model. Provides a Large Collection of Models The book encompasses a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox regression model. The author also explains methods of constructing confidence regions, profile likelihood-based confidence intervals, and likelihood ratio tests. Uses Statistical Inference Package to Make Inferences on Real-Valued Parameter Functions OfferinTable of ContentsIntroduction. Likelihood-Based Statistical Inference. Generalized Regression Model.General Linear Model.Nonlinear Regression Model. Generalized Linear Model.Binomial and Logistic Regression Models.Poisson Regression Model.Multinomial Regression.Other Generalized Linear Regressions Models.Other Generalized Regression Models. Appendices.

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Book SynopsisA book in the Systems Evaluation, Prediction, and Decision-Making Series, Systems Evaluation: Methods, Models, and Applications covers the evolutionary course of systems evaluation methods, clearly and concisely. Outlining a wide range of methods and models, it begins by examining the method of qualitative assessment. Next, it describes the process and methods for building an index system of evaluation and considers the compared evaluation and the logical framework approach, analytic hierarchy process (AHP), and the data envelopment analysis (DEA) relative efficiency evaluation method.Unique in its emphasis on the practical applications of systems evaluation methods and models, the book introduces several new evaluation models of grey system, including general grey incidence model, grey incidence models based on similarity and closeness, grey cluster evaluation based on triangular whitenization functions, and multi-attribute grey target decision modeTable of ContentsIntroduction 1 Common System Evaluation Methods and Models 2 Grey System Evaluation Models 3 Postevaluation of Road–Bridge Construction: Case Study of Lianxu Highway in China 4 Efficiency Evaluations of Scientific and Technological Activities 5 Evaluation of Energy Saving in China 6 International Cooperation Project Selection

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Book SynopsisPick up any hydrology textbook and it will not be long before you encounter pages listing sequences of equations representing complex mathematical concepts. Students and practitioners of hydrology will not find this very helpful, as their aim, generally, is to study and understand hydrology, and not to find themselves confronted with material that even students of mathematics would find challenging. Often, equations appear to be copied and pasted into hydrological texts in an attempt to give a more rigorous scientific basis to the narrative. However, they are commonly wrong, poorly explained, without context or background, and more likely to confuse and distance the reader than to enlighten and engage them in the topic. Understanding Mathematical and Statistical Techniques in Hydrology provides full and detailed expositions of such equations and mathematical concepts, commonly used in hydrology. In contrast to other hydrological texts, instead of presenting abstract Table of ContentsPreface vii How to use this book x 1 Fundamentals 1 1.1 Motivation for this book 1 1.2 Mathematical preliminaries 2 2 Statistical modelling 19 2.1 The Central European Floods, August 2002 19 2.2 Extreme value analysis 22 2.3 Simple methods of return period estimation 22 2.4 Return periods based on distribution fitting 25 2.5 Techniques for parameter estimation 30 2.6 Bayesian parameter estimation 30 2.7 Resampling methods: bootstrapping 31 3 Mathematics of hydrological processes 34 3.1 Introduction 34 3.2 Algebraic and difference equation methods 34 3.3 Methods involving exponentiation 36 3.4 Rearranging model equations 36 3.5 Equations with iterated summations and products 38 3.6 Methods involving differential equations 41 3.7 Methods involving integrals 43 4 Techniques based on data fitting 45 4.1 Experimental and observed data 45 4.2 Rating curves 46 4.3 Regression with two or more independent variables 49 4.4 Demonstration of decaying quantities 51 4.5 Analysis based on harmonic functions 52 5 Time series data 55 5.1 Introduction 55 5.2 Characteristics of time series data 55 5.3 Testing for time dependence 57 5.4 Testing for trends 58 5.5 Frequency analysis 59 5.6 Other analysis methods 60 5.7 Smoothing and filtering 60 5.8 Linear smoothing and filtering methods 61 5.9 Nonlinear filtering methods 64 5.10 Time series modelling 66 5.11 Hybrid time series/process-based models 67 5.12 Detecting non-stationarity 69 6 Measures of model performance, uncertainty and stochastic modelling 71 6.1 Introduction 71 6.2 Quantitative measures of performance 71 6.3 Comparing measures 73 6.4 The Nash–Sutcliffe method 75 6.5 Stochastic modelling 76 6.6 Monte Carlo simulations 77 6.7 Non-uniform Monte Carlo sampling 79 6.8 Uncertainty in hydrological modelling 81 6.9 Uncertainty in combined models 82 6.10 Assessing uncertainty given observed data: Bayesian methods 83 Glossary 86 Index 88

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Book Synopsis1 Introduction.- 1.1 The Classical Sampling Theorem.- 1.2 Non-Uniform Sampling and Frames.- 1.3 Outline of the Book.- 2 On the Transmission Capacity of the Ether and Wire in Electrocommunications.- I Sampling, Wavelets, and the Uncertainty Principle.- 3 Wavelets and Sampling.- 4 Embeddings and Uncertainty Principles for Generalized Modulation Spaces.- 5 Sampling Theory for Certain Hilbert Spaces of Bandlimited Functions.- 6 Shannon-Type Wavelets and the Convergence of Their Associated Wavelet Series.- II Sampling Topics from Mathematical Analysis.- 7 Non-Uniform Sampling in Higher Dimensions: From Trigonometric Polynomials to Bandlimited Functions.- 8 The Analysis of Oscillatory Behavior in Signals Through Their Samples.- 9 Residue and Sampling Techniques in Deconvolution.- 10 Sampling Theorems from the Iteration of Low Order Differential Operators.- 11 Approximation of Continuous Functions by RogosinskiType Sampling Series.- III Sampling Tools and Applications.- 12 Fast Fourier Transforms for Nonequispaced Data: A Tutorial.- 13 Efficient Minimum Rate Sampling of Signals with Frequency Support over Non-Commensurable Sets.- 14 Finite-and Infinite-Dimensional Models for Oversampled Filter Banks.- 15 Statistical Aspects of Sampling for Noisy and Grouped Data.- 16 Reconstruction of MRI Images from Non-Uniform Sampling and Its Application to Intrascan Motion Correction in Functional MRI.- 17 Efficient Sampling of the Rotation Invariant Radon Transform.- References.Trade Review"The introduction (Chapter 1) gives an excellent overview of the history and development of sampling theory. It shows that the WSK sampling theory has roots in many classical areas of mathematics, such as harmonic analysis, number theory, and interpolation theory. Many famous mathematicians, such as Cauchy, Borel, Hadamard, and de la Vallee-Poussin contributed directly or indirectly to its development. The introduction then proceeds to show how sampling theory is connected to more recent topics in mathematical analysis, such as wavelets, Gabor systems, density theorems, frames, and sampling in locally compact abelian groups." —Mathematical Reviews "Engineers and mathematicians working in wavelets, signal processing, and harmonic analysis, as well as scientists and engineers working on applications as varied as medical imaging and synthetic aperture radar, will find the book to be a modern and authoritative guide to sampling theory." —Publicationes MathematicaeTable of ContentsIntroduction, On the transmission capacity of the 'ether' and wire in electrocommunications, Part I: Sampling, wavelets, and the uncertainty principle, Wavelets and sampling, Embeddings and uncertainty principles for generalized modulation spaces, Sampling theory for certain hilbert spaces of bandlimited functions, Shannon-type wavelets and the convergence of their associated wavelet series, Part II: Sampling topics from mathematical analysis, Non-uniform sampling in higher dimensions: From trigonometric polynomials to bandlimited functions, The analysis of oscillatory behavior in signals through their samples, Residue and sampling techniques in deconvolution, Sampling theorems from the iteration of low order differential operators, Approximation of continuous functions by Rogosinski-Type sampling series, Part III: Sampling tools and applications, Fast fourier transforms for nonequispaced data: A tutorial, Efficient minimum rate sampling of signals with frequency support over non-commensurable sets, Finite and infinite-dimensional models for oversampled filter banks, Statistical aspects of sampling for noisy and grouped data, Reconstruction of MRI images from non-uniform sampling, application to Intrascan motion correction in functional MRI, Efficient sampling of the rotation invariant radon transform

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Book SynopsisSolve the DVA/FVA Overlap Issue and Effectively Manage Portfolio Credit RiskCounterparty Risk and Funding: A Tale of Two Puzzles explains how to study risk embedded in financial transactions between the bank and its counterparty. The authors provide an analytical basis for the quantitative methodology of dynamic valuation, mitigation, and hedging of bilateral counterparty risk on over-the-counter (OTC) derivative contracts under funding constraints. They explore credit, debt, funding, liquidity, and rating valuation adjustment (CVA, DVA, FVA, LVA, and RVA) as well as replacement cost (RC), wrong-way risk, multiple funding curves, and collateral. The first part of the book assesses today's financial landscape, including the current multi-curve reality of financial markets. In mathematical but model-free terms, the second part describes all the basic elements of the pricing and hedging framework. Taking a more practical slant, the third pTrade Review"… a fresh take on mitigation of counterparty risk … [the book] gives a ground-up approach for analysis and managing of risks associated with non-payment of promised cash flows due to the default by a party in an over-the-counter derivative transaction. It should be of value to researchers, graduate students, financial quants, managers in banks, CVA desks, and members of supervisory bodies."—hedgeweek.com, July 2014"The landscape of the rates and credit markets has changed so drastically since the 2008 crisis that older textbooks are barely relevant and, from an analytic perspective, appropriate methods have to be rethought from scratch. The present volume is one of the best contributions in this direction, featuring a clear description of the various ‘value adjustments,’ new models for portfolio credit risk, a unified analytic framework based on BSDEs, and detailed treatment of numerical methods."—Mark Davis, Imperial College London"Understanding the subtle interconnections between credit and funding is key to a modern valuation of derivatives. This timely contribution, written by world-class academics who are also well-recognized experts in the field, offers a rigorous and comprehensive treatment of the main theories underpinning the new valuation principles. Numerical examples are also provided to help the reader grasp key concepts and ideas of the advanced models and techniques here presented. Overall, an excellent textbook. Brigo’s dialogue is the icing on the cake."—Fabio Mercurio, Head of Derivatives Research, Bloomberg LP"A big hooray for this book on CVA, DVA, FVA/LVA, RVA, TVA, and other three letter acronyms (TLA!)."—Peter Carr, PhD, Managing Director, Morgan Stanley, and Executive Director, NYU Courant Master of Science Program in Mathematics in FinanceTable of ContentsFinancial Landscape. Model-Free Developments. Reduced-Form BSDE Modeling. Dynamic Copula Models. Further Developments. Mathematical Appendix. Index.

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Book SynopsisEstimate and Interpret Results from Ordered Regression ModelsOrdered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption.The authors first introduce the three parallel ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneousTrade Review"The book is intended to be a starter for somebody not familiar with the subject. It was written primarily for social scientists (published in the CRC Statistics in the Social and Behavioral Sciences Series) and as such, it can be read easily without any statistical pre-requisites beyond very basic Statistics and some working knowledge of logistic regression. Nevertheless, the book is certainly useful far beyond the social sciences themselves – in particular for epidemiologists, medical researchers and also statisticians of students of Statistics/Biostatistics who want to learn basic facts about ordered regression and perhaps motivate further study of this interesting field. The style of exposition is quite informal and intuitive."~International Society for Clinical BiostatisticsTable of ContentsIntroduction. Parallel Models. Partial Models. Nonparallel Models. Testing the Parallel Regression Assumption. Extensions. References. Index.

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Book SynopsisA User''s Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book.The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random Table of ContentsWhat Is Analytics? Introducing R—An Analytics Software. Reporting Data. Statistical Graphics and Visual Analytics. Probability. Random Variables and Probability Distributions. Continuous Random Variables. Statistical Inference. Regression for Predictive Model Building. Decision Trees. Data Mining and Multivariate Methods. Modeling Time Series Data for Forecasting.

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Book SynopsisStatistics is of ever-increasing importance in Science and Technology and this book presents the essentials of the subject in a form suitable either as the basis of a course of lectures or to be read and/or used on its own.Table of Contents1 Introduction or ‘What is statistics?’.- 2 The presentation of data.- 3 Probability, its meaning, real and theoretical populations.- 4 Basic properties of the normal distribution.- 5 Some properties of sampling distributions.- 6 Applications of normal sampling theory; significance tests.- 7 Normal sampling theory: test for difference between several sample means, analysis of variance, design of experiments.- 8 Normal sampling theory: estimation of ‘parameters’ by confidence intervals, by maximum likelihood.- 9 The binomial distribution: laws of probability, applications of the binomial distribution, the multinomial distribution.- 10 The Poisson, negative exponential, and rectangular distributions.- 11 The ?2 test for ‘goodness of fit’: test for ‘association’.- 12 Fitting lines and curves to data, least squares method.- 13 Regression curves and lines, correlation coefficient, normal bivariate distribution.- 14 Some distribution-independent (or ‘distribution-free’ or ‘non-parametric’) tests.- 15 Note on sampling techniques and quality control.- 16 Some problems of practical origin.- Answers.

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Book SynopsisWhat is Big Data, and why should you care?Big data knows where you''ve been and who your friends are. It knows what you like and what makes you angry. It can predict what you''ll buy, where you''ll be the victim of crime and when you''ll have a heart attack. Big data knows you better than you know yourself, or so it claims.But how well do you know big data?You''ve probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun?Yes. Yes, you can.Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book.Starting with the basics what IS data? And what makes it big? Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of ThiTrade ReviewA superb explanation of how we got to today. * Evening Standard *Harkness has the perfect combination of wit, charm and statistical insight to crunch big data. It's the book about stats, life and big data we've all been waiting for. -- Matt Parker, Stand-up MathematicianHarkness raises some very big questions indeed, not just about the grandiose claims of the big data evangelists, but also about how in the age of universal surveillance we can defend the concept of privacy. * The Herald *A wonderful collection of fascinating data stories, all told in Timandra's smart and chatty style. But this book also asks the important questions. If big data brings new opportunities, just what are the implications? -- Hannah Fry, author and mathematicianA brilliant guide to our brave new world. -- Brian CleggThis book is a great read – full of interesting stories and fun interviews. But it is not just another credulous tale of technological wonders – Harkness is suitably sceptical of the hype about data analytics, and serious about the challenges is brings. -- David Spiegelhalter, author and mathematicianTable of ContentsIntroduction: What is it? Where did it come from? 1: What Is Data? And what makes it big? 2: Death and Taxes. And Babies. 3: Thinking Machines What Has Big Data Done For Us? 4: Big Business 5: Big Science 6: Big Society 7: Data Driven Democracy Big Ideas? 8: Big Brother 9: Who Do We Think You Are? 10: Are You A Data Point Or A Human Being? Appendix - things you can do to keep your data private Acknowledgements

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Book SynopsisActuarial Models: The Mathematics of Insurance, Second Edition thoroughly covers the basic models of insurance processes. It also presents the mathematical frameworks and methods used in actuarial modeling. This second edition provides an even smoother, more robust account of the main ideas and models, preparing students to take exams of the Society of Actuaries (SOA) and the Casualty Actuarial Society (CAS).New to the Second Edition Revises all chapters, especially material on the surplus process Takes into account new results and current trends in teaching actuarial modeling Presents a new chapter on pension models Includes new problems from the 2011-2013 CAS examinations Like its best-selling, widely adopted predecessor, this edition is designed for students, actuaries, mathematicians, and researchers interested in insurance processes and economic and social models. The authTable of ContentsPreliminary Facts from Probability and Interest. Comparison of Random Variables. Preferences of Individuals. An Individual Risk Model for a Short Period. A Collective Risk Model for a Short Period. Random Processes and Their Applications I. Random Processes and Their Applications II. Global Characteristics of the Surplus Process. Survival Distributions. Life Insurance Models. Annuity Models. Premiums and Reserves. Pensions Plans. Risk Exchange: Reinsurance and Coinsurance. Appendix. References. Answers to Exercises. Index.

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Book SynopsisEquivalence: Elizabeth L. Scott at Berkeley is the compelling story of one pioneering statistician's relentless twenty-year effort to promote the status of women in academe and science. Part biography and part microhistory, the book provides the context and background to understand Scott's masterfulness at using statistics to help solve societal problems. In addition to being one of the first researchers to work at the interface of astronomy and statistics and an early practitioner of statistics using high-speed computers, Scott worked on an impressively broad range of questions in science, from whether cloud seeding actually works to whether ozone depletion causes skin cancer. Later in her career, Scott became swept up in the academic women's movement. She used her well-developed scientific research skills together with the advocacy skills she had honed, in such activities as raising funds for Martin Luther King Jr. and keeping Free Speech Movement students outTrade Review "This book is an amazing tour de force." ~ Juliet Shaffer, University of California-Berkeley"What an intriguing life Scott led!" ~ Deborah Bennett, Lawrence Livermore National Laboratory, Ret. "The details of what was done when in response to situations are revealing and instructive. We should all have access to her story." ~ Brian Yandell, University of Wisconsin"The way in which Scott was able to continue her research while simultaneously serving the University system through her gender discrimination work is exemplary and should be inspirational to the academic women of today. Women are still recognised as being under-represented at higher levels of academia, particularly in science, even though it is now 50 years after Scott commenced her investigations! Men and women who are interested in the history of statistics and in the history of gender equity in universities will want to own this book. There is inspiration to be gained and lessons to be learnt by those who still face gender inequity in academia today." ~ Alice Richardson, ANU College of Medicine, Canberra"Equivalence tells the captivating story of statistician Elizabeth L. Scott, who was a trail blazer for all women in academia, and especially in statistics . . . During her entire time in the Statistics Department, she overlapped with only four other women . . . It is a story of the love, passion, and commitment exhibited by Betty throughout her personal and professional life. It also illustrates the love, passion, and commitment of the author (statistician Amanda Golbeck) for telling Betty’s story. . . Reading Equivalence was an eye-opening experience for me. Having received my PhD in Statistics in 1978, the book helped me place my academic career in a larger context. It felt somewhat like I had boarded a train part way through a treacherous journey, and only slowly came to realize the hardships the passengers had faced before reaching my embarkation point. It brought back memories of some of my early experiences . . ." ~ Jessica Utts, American Statistician"This book is an amazing tour de force." ~ Juliet Shaffer, University of California-Berkeley"What an intriguing life Scott led!" ~ Deborah Bennett, Lawrence Livermore National Laboratory, Ret. "The details of what was done when in response to situations are revealing and instructive. We should all have access to her story." ~ Brian Yandell, University of Wisconsin"The way in which Scott was able to continue her research while simultaneously serving the University system through her gender discrimination work is exemplary and should be inspirational to the academic women of today. Women are still recognised as being under-represented at higher levels of academia, particularly in science, even though it is now 50 years after Scott commenced her investigations! Men and women who are interested in the history of statistics and in the history of gender equity in universities will want to own this book. There is inspiration to be gained and lessons to be learnt by those who still face gender inequity in academia today." ~ Alice Richardson, ANU College of Medicine, Canberra"Equivalence tells the captivating story of statistician Elizabeth L. Scott, who was a trail blazer for all women in academia, and especially in statistics . . . During her entire time in the Statistics Department, she overlapped with only four other women . . . It is a story of the love, passion, and commitment exhibited by Betty throughout her personal and professional life. It also illustrates the love, passion, and commitment of the author (statistician Amanda Golbeck) for telling Betty’s story. . . Reading Equivalence was an eye-opening experience for me. Having received my PhD in Statistics in 1978, the book helped me place my academic career in a larger context. It felt somewhat like I had boarded a train part way through a treacherous journey, and only slowly came to realize the hardships the passengers had faced before reaching my embarkation point. It brought back memories of some of my early experiences . . ." ~ Jessica Utts, American StatisticianTable of ContentsIntroduction. Framing the local research questions (1968). West Point and the field artillery family (ancestry I). Collecting, managing, and summarizing the local data (1969). Aunt Phoebe the astronomer (ancestry II). Reporting the local data (1970). Becoming an outlier. Using the local data for advocacy (1971). 10,000 hours of professional practice. Regressing national data (1972). The UC - Berkeley department of statistics. Focusing on salary data (1973). With Jerzy Neyman. Advocating for data quality improvement (1974). Loyalty oath, civil rights, free speech. Using statistical reasoning toward affirmative action (1975). Productivity as a statistical scientist. Creating the salary evaluation kit (1976). Influencing academic salaries (1977). Continuing efforts to further the careers of academic women (1978 - 1981). After Neyman. (1982 - 1988).

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Trade ReviewFrom the book reviews:“This text has resulted from the author’s teaching of introductory statistics to engineering students in the USA. Dealing both with the theoretical aspects of statistical methods and the need to implement software that engineers are familiar with, the book is a delight to read. … I recommend the book to any one intending to use either MATLAB or/and WinBUGS for statistical modelling and analysis.” (Carl M. O’Brien, International Statistical Review, Vol. 81 (3), 2014)“Although there are many engineering statistics books, this is the first one I have seen devoted to bioengineering. It is a very comprehensive book with many good features. … I would say that Statistics for Bioengineering Sciences would make a wonderful text for a first course in statistics for biomedical engineering students and is a great reference for engineers and statisticians.” (Michael R. Chernick, Technometrics, Vol. 55 (1), February, 2013)Table of ContentsIntroduction.- The Sample and Its Properties.- Probability, Conditional Probability, and Bayes' Rule.- Sensitivity, Specificity, and Relatives.- Random Variables.- Normal Distribution.- Point and Interval Estimators.- Bayesian Approach to Inference.- Testing Statistical Hypotheses.- Two Samples.- ANOVA and Elements of Experimental Design.- Distribution-Free Tests.- Goodness-of-Fit Tests.- Models for Tables.- Correlation.- Regression.- Regression for Binary and Count Data.- Inference for Censored Data and Survival Analysis.- Bayesian Inference Using Gibbs Sampling - BUGS Project.

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Book SynopsisWinner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against intelligent adversaries. Many examples throughout illustrate the application of the ARA approach to a variety of games and strategic situations.Focuses on the recent subfield of decision analysis, ARA Compares ideas from decision theory and game theoryUses multi-agent influence diagrams (MAIDs) throughout to help readers visualize complex information structuresApplies the ARA approach to simultaneous games, auctions, sequential games, and defend-attack gamesContains an extended case study based on a real application in railway security, whichTrade Review"This well-written and concise text is an introduction to the field of adversarial risk analysis (ARA), which is a form of decision and risk analysis which incorporates uncertainty and game theory to model strategies of an adversary…There is an appropriate amount of detail throughout the book, making it suitable for a reference text as well as a book which may be read cover to cover and it is both thought provoking and enlightening."—Matthew Craven, Plymouth University, Journal of the Royal Statistical Society, Series A, January 2017 "Here, Banks (Duke Univ.), Rios (IBM), and Insua (ICMAT-CSIC, Spain) identify three categories of uncertainty for the strategist: aleatory uncertainty—nondeterminism of outcomes even after players make choices; epistemic uncertainty—hidden information concerning opponents' preferences, beliefs, and capabilities; and concept uncertainty—hidden information concerning opponents' strategies. Adversarial risk analysis, a new field with roots in modern efforts to defeat terrorism, provides a framework, in principle, to cope with these uncertainties. Solving the models seems generally intractable, but the heart of the book, the first of its kind, offers exemplary case studies. Summing up: Recommended. Lower-division undergraduates and above; informed general audiences."—D. V. Feldman, University of New Hampshire, Durham, USA, for CHOICE, March 2016 Table of ContentsGames and Decisions. Simultaneous Games. Auctions. Sequential Games. Variations on Sequential Defend-Attack Games. A Security Case Study. Other Issues. Solutions to Selected Exercises. References. Index.

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Book SynopsisThis book provides engineers and scientists with a single source introduction to the concepts, models, and case studies for making credible reliability assessments. It satisfies the need for thorough discussions of several fundamental subjects.Section I contains a comprehensive overview of assessing and assuring reliability that is followed by discussions of: Concept of randomness and its relationship to chaos Uses and limitations of the binomial and Poisson distributions Relationship of the chi-square method and Poisson curves Derivations and applications of the exponential, Weibull, and lognormal models Examination of the human mortality bathtub curve as a template for componentsSection II introduces the case study modeling of failure data and is followed by analyses of: 5 sets of ideal Weibull, lognormal, and normal failure data 83 sets of actual (real) failure data The intent of the modeling was Table of ContentsSelected Topics. Overview of Reliability Assessments. Concept of Random. Probability & Sampling. Reliability Functions. Reliability Model: Exponential. Reliability Models: Weibull & Lognormal. Bathtub Curves for Humans & Components. Introduction & Case Studies. Introduction to Case Study Modeling of Failure Data.

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Book SynopsisPrepare Your Students for Statistical Work in the Real WorldStatistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statistical inference as well as the statistical methods necessary for real-world applications. Students will understand how to collect and analyze data and think critically about the results.New to the Sixth Edition Many new and updated exercises based on contemporary engineering and scientific-related studies and real data More statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software Introduction of the case studies at the beginning of each chapter Streamlined materiTrade Review"A salient feature of this book is the clarity with which many statistical concepts have been presented. A very nice blend of theory and applications. It contains a wealth of illustrative examples and problem sets. All the important concepts have been highlighted; real-life data has been extensively used throughout the book. Students will find it very appealing and useful on their way to learning the basic statistical concepts and tools."—Dharam V. Chopra, Wichita State University "I like the problems because they are all based on engineering applications of probability and statistics. I especially like the problems at the end of chapters because students have to think more to solve them. I favor problems that require calculations because engineers are problem solvers." —Charles H. Reilly, University of Central Florida "I think this text is one of the best I have seen when it comes down to real data sets. The authors successfully included small and large real data sets from various real-world problems in engineering, mathematical sciences, and natural sciences."—Edward J. Danial, Morgan State University Table of ContentsIntroduction. Descriptive Statistics. Probability. Discrete Random Variables. Continuous Random Variables. Bivariate Probability Distributions and Sampling Distributions. Estimation Using Confidence Intervals. Tests of Hypotheses. Categorical Data Analysis. Simple Linear Regression. Multiple Regression Analysis. Model Building. Principles of Experimental Design. The Analysis of Variance for Designed Experiments. Nonparametric Statistics. Statistical Process and Quality Control. Product and System Reliability.

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Book SynopsisDynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software.The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference.Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is Trade Review"The properties of this book may be summarized in two words: rich and concise. . . this is a very well-written book that manages to cover a lot of ground in a remarkably succinct way. . . I can highly recommend the book."—Per Kragh Andersen, International Society for Clinical Biostatistics"I think that those whose research is or will be in the area of dynamical biostatistics would benefit from having a copy on their shelves."—Alice M. Richardson, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Australia"This book aims at describing methods of biostatistics modeling, in particular, dynamical model approaches for statisticians, as well as serving as a textbook for postgraduate students... The balance between theory and application is appropriate for both researchers performing biostatistics modeling and for students taking graduate level courses… The book is concisely written so that it covers a wide range of basic and dynamic models and modeling approaches with examples. Statisticians in the industry may feel a large part of the book too technical but can use the book for reference and may also benefit from the rich examples and R-codes, some with translation to SAS, in the appendix." —Pharmaceutical Statistics"One of my favorite features of this book is that the same three examples are used throughout, and all the approaches discussed are applied to these examples. This allows readers to identify the similarities and differences of various techniques. Furthermore, it is a good way for readers to learn so-called data mining since diverse information can be mined by applying different statistical methods to the same dataset . . . I believe this book will be a successful text for graduate level courses focusing on dynamical biostatistical models that analyze time-dependent data. Its presentation of conventional methods is very effective."~Hongjian Zhu, Table of ContentsIntroduction. Classical Biostatistical Models: Inference. Survival Analysis. Models for Longitudinal Data. Advanced Biostatistical Models: Extensions of Mixed Models. Advanced Survival Models. Multistate Models. Joint Models for Longitudinal and Time-to-Event Data. The Dynamic Approach to Causality. Appendix: Software. Index.

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Book SynopsisUnderstand the Strategic Behavior in Queueing SystemsRational Queueing provides one of the first unified accounts of the dynamic aspects involved in the strategic behavior in queues. It explores the performance of queueing systems where multiple agents, such as customers, servers, and central managers, all act but often in a noncooperative manner.The book first addresses observable queues and models that assume state-dependent behavior. It then discusses other types of information in queueing systems and compares observable and unobservable variations and incentives for information disclosure. The next several chapters present relevant models for the maximization of individual utilities, social welfare, and profits. After covering queueing networks, from simple parallel servers to general network structures, the author describes models for planned vacations and forced vacations (such as breakdowns). Focusing on supply chaiTrade Review"This unique bibliographical volume by a foremost researcher in the area of strategic queues, or game theoretical analysis of queueing systems, provides a complete panorama of the extensive literature that was published in this area in recent years, spanning the period since the by-now classical monograph of Hassin and Haviv (2003) up to the present day. With over 700 references, individual papers are typically summarized in a paragraph or two, highlighting their essential contribution. The surveyed articles are arranged within a thoughtful subject classification, which makes it easy to focus on topics of interest, while the chronological ordering within each subject clearly displays the progression of ideas and results. Anyone who works in the area of strategic queues should have this book handy on his or her bookshelf. A newcomer may first study the above-mentioned monograph and come back to the present book to fully realize the state of the art."- Nahum Shimkin, Professor of Electrical Engineering, Technion"Rational Queueing provides a comprehensive and authoritative survey of recent research on strategic behavior in queueing systems. Focusing on advances in the field since the publication of the classic To Queue or Not to Queue, the author brings the reader up to date by summarizing and organizing over 700 papers from multiple disciplines in a unifying framework. This book will serve as an indispensable reference for researchers, students, and teachers in this area."- Philipp Afèche, Associate Professor of Operations Management, Rotman School of Management, University of Toronto"Rational Queueing is the book that defines and summarizes the important area of operations research that combines queueing with game theory. This is a must-have reference for anyone that is interested in game-theoretic considerations in queueing models. It will be immensely useful as an introduction for beginners and as a reference for mature researchers in the area. Professor Hassin has done a superb job. The book can be used for independent study, but also as the main source for graduate seminars on production, service, and operations management."- Antonis Economou, Department of Mathematics, University of Athens"This really is a must read for researchers in game theory, from those just starting out in this field through to more established researchers. It is also a valuable teaching resource, covering both undergraduate topics through to a comprehensive and state-of-the-art review of modern trends in the subject for postgraduate students."- Paul Harper, Professor of Operational Research and Deputy Head of School of Mathematics, Cardiff University"With the gross domestic product (GDP) in the service sector accounting for over 90 percent of the whole GDP in many countries and areas such as the United States and Hong Kong, service management is becoming increasingly important. Different from a manufacturing system, a service system has direct contact with customers, a feature that has inspired an increasing trend on studying service management with strategic customers. This book provides a timely and inclusive summary of studies on strategic customer behavior and games among service providers in recent years. I was amazed by two main features of the book. One, the number of papers surveyed is enormous. With over 700 papers surveyed, the book covers a wide range of topics on games among customers and games among service providers. In particular, it surveys those cutting-edge topics in the field such as bounded-rationality and loss-aversion customer behavior. Two, the reviews on papers are extremely accurate. I heard from many researchers that the author of the book, Prof. Refael Hassin, contacted them individually, asking for feedback on the review of their work. I was also requested to provide comments on my work during the writing of this book. These steps assure the high quality of the book. This book can serve as an excellent reference book for researchers and graduate students in the fields of operations management, operations research, industry engineering, civil engineering, and computer science. This is a must-have book and is greatly helpful and handy for researchers in fields involving strategic customers."- Dr. Pengfei Guo, Associate Professor and Associate Head, Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University"In this book, the author gives an extensive survey on queueing systems in which the agents interact to maximize certain goals. The book is a follow-up of a book on strategic queueing co-authored by the author and M. Haviv [To queue or not to queue: equilibrium behavior in queueing systems, Internat. Ser. Oper. Res. Management Sci., 59, Kluwer Acad. Publ., Boston, MA, 2003; MR2006433]. [..] This book is excellent reading for any researcher interested in the state of the art of rational queueing. It provides an overview on recent work combining queueing systems and game theory."- Judith Timmer, Mathematical Reviews, January 2017"This unique bibliographical volume by a foremost researcher in the area of strategic queues, or game theoretical analysis of queueing systems, provides a complete panorama of the extensive literature that was published in this area in recent years, spanning the period since the by-now classical monograph of Hassin and Haviv (2003) up to the present day. With over 700 references, individual papers are typically summarized in a paragraph or two, highlighting their essential contribution. The surveyed articles are arranged within a thoughtful subject classification, which makes it easy to focus on topics of interest, while the chronological ordering within each subject clearly displays the progression of ideas and results. Anyone who works in the area of strategic queues should have this book handy on his or her bookshelf. A newcomer may first study the above-mentioned monograph and come back to the present book to fully realize the state of the art."- Nahum Shimkin, Professor of Electrical Engineering, Technion"Rational Queueing provides a comprehensive and authoritative survey of recent research on strategic behavior in queueing systems. Focusing on advances in the field since the publication of the classic To Queue or Not to Queue, the author brings the reader up to date by summarizing and organizing over 700 papers from multiple disciplines in a unifying framework. This book will serve as an indispensable reference for researchers, students, and teachers in this area."- Philipp Afèche, Associate Professor of Operations Management, Rotman School of Management, University of Toronto"Rational Queueing is the book that defines and summarizes the important area of operations research that combines queueing with game theory. This is a must-have reference for anyone that is interested in game-theoretic considerations in queueing models. It will be immensely useful as an introduction for beginners and as a reference for mature researchers in the area. Professor Hassin has done a superb job. The book can be used for independent study, but also as the main source for graduate seminars on production, service, and operations management."- Antonis Economou, Department of Mathematics, University of Athens"This really is a must read for researchers in game theory, from those just starting out in this field through to more established researchers. It is also a valuable teaching resource, covering both undergraduate topics through to a comprehensive and state-of-the-art review of modern trends in the subject for postgraduate students."- Paul Harper, Professor of Operational Research and Deputy Head of School of Mathematics, Cardiff University"With the gross domestic product (GDP) in the service sector accounting for over 90 percent of the whole GDP in many countries and areas such as the United States and Hong Kong, service management is becoming increasingly important. Different from a manufacturing system, a service system has direct contact with customers, a feature that has inspired an increasing trend on studying service management with strategic customers. This book provides a timely and inclusive summary of studies on strategic customer behavior and games among service providers in recent years. I was amazed by two main features of the book. One, the number of papers surveyed is enormous. With over 700 papers surveyed, the book covers a wide range of topics on games among customers and games among service providers. In particular, it surveys those cutting-edge topics in the field such as bounded-rationality and loss-aversion customer behavior. Two, the reviews on papers are extremely accurate. I heard from many researchers that the author of the book, Prof. Refael Hassin, contacted them individually, asking for feedback on the review of their work. I was also requested to provide comments on my work during the writing of this book. These steps assure the high quality of the book. This book can serve as an excellent reference book for researchers and graduate students in the fields of operations management, operations research, industry engineering, civil engineering, and computer science. This is a must-have book and is greatly helpful and handy for researchers in fields involving strategic customers."- Dr. Pengfei Guo, Associate Professor and Associate Head, Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University"The book summarizes a big amount of actual results concerning the combination of queueing theory and game theory. It indicates the results of over 700 papers appearing in the bibliography in this area and very accurately refers their contents reecting the most actual achievements in a wide range of topics on games among customers and service providers. It can highly be recommended to researchers, teachers and students working in this area, even one can say it is an indispensable handbook for them."- L□aszl□o Lakatos (Budapest)"In this book, the author gives an extensive survey on queueing systems in which the agents interact to maximize certain goals. The book is a follow-up of a book on strategic queueing co-authored by the author and M. Haviv [To queue or not to queue: equilibrium behavior in queueing systems, Internat. Ser. Oper. Res. Management Sci., 59, Kluwer Acad. Publ., Boston, MA, 2003; MR2006433]. [..] This book is excellent reading for any researcher interested in the state of the art of rational queueing. It provides an overview on recent work combining queueing systems and game theory."- Judith Timmer, Mathematical Reviews, January 2017Table of ContentsIntroduction. Observable queues. Information. Customer decisions. Social optimization and cooperation. Monopoly. Competition. Routing in queueing networks. Supply chains, outsourcing, and contracting. Vacations. Bounded rationality. Bibliography. Indices.

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Book SynopsisA properly structured financial model can provide decision makers with a powerful planning tool that helps them identify the consequences of their decisions before they are put into practice. Introduction to Financial Models for Management and Planning, Second Edition enables professionals and students to learn how to develop and use computer-based models for financial planning. This volume provides critical tools for the financial toolbox, then shows how to use them tools to build successful models.Table of ContentsAn Overview of Financial Planning and Modeling. Part I: Tools for Financial Planning and Modeling: Financial Analysis. The Tools for Financial Planning I: Financial Analysis. Appendix A: Using Names in the Excel Spreadsheet. Appendix B: Constructing a Data Table. The Tools for Financial Planning II: Growth and Cash Flows. Part II: Tools for Financial Planning and Modeling: Simulation. Financial Statement Simulation. Monte Carlo Simulation. Part III: Introduction to Forecasting Methods. Forecasting I: Time Trend Extrapolation. Forecasting II: Econometric Forecasting. Forecasting III: Smoothing Data for Forecasts. Part IV: A Closer Look at the Details of a Financial Model. Modeling Value. Modeling Long-Term Assets. Debt Financing. Modeling Working Capital Accounts. PART V: Modeling Security Prices and Investment Portfolios. Modeling Security Prices. Constructing Optimal Security Portfolios. Options. Part VI: Optimization Models. Optimization Models for Financial Planning. Planning and Managing Working Capital with LP. References. Index

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Book SynopsisPraise for the first edition:Given the author's years of experience as a statistician and as a founder of the first DMC in pharmaceutical industry trials, I highly recommend this booknot only for experts because of its cogent and organized presentation, but more importantly for young investigators who are seeking information about the logistical and philosophical aspects of a DMC. -S. T. Ounpraseuth, The American Statistician In the first edition of this well-regarded book, the author provided a groundbreaking and definitive guide to best practices in pharmaceutical industry data monitoring committees (DMCs). Maintaining all the material from the first edition and adding substantial new material, Data and Safety Monitoring Committees in Clinical Trials, Second Edition is ideal for training professionals to serve on their first DMC as well as for experienced clinical and biostatistical DMC members, sponsor and regTrade Review "The book by Dr. Herson is written amazingly well. The book concentrates on pharmaceutical industrysponsored confirmatory clinical trials and can serve as excellent sources of knowledge for all the aspects of data safety monitoring committee (DMC) activities. A great feature of the book is the “DMCounselor” section at the end of each chapter, covering nearly 70 questions with answers, of which about 33% are for a DMC Chair, 30% for the DMC Biostatistician member, 30% for a DMC physician member, and the rest are for others (e.g., project manager)."~ Daniel Jia, Journal of Biopharmaceutical Statistics"Nicely written and readable cover-to-cover, the author walks through every facet of a Data Monitoring Committee (DMC) beginning with an overview of their past and current place in drug development, to how they are organized and interfaced with other committees, and on to the specifics of how a typical meeting is split into an open and closed session. From there it moves on to clinical issues, including how SAEs are categorized, statistical methods, including Bayesian and frequentist conditional power calculations, common biases and pitfalls, and guidance for how DMC decisions are made. It concludes with emerging issues due to new clinical trial designs mandated by the FDA to speed up the drug development process."~Donna Pauler Ankerst, BiometricsTable of ContentsPreface to the Second Edition. Introduction. Organization of a Safety Monitoring Program for a Confirmatory Trial. Meetings. Clinical Issues. Statistical Issues. Biases and Pitfalls. DMC Decisions. Emerging issues. Appendix.

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Book SynopsisThis would be an excellent book for undergraduate, graduate and beyond.The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data. having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions. Michelle Pantoya, Texas Tech UniversityMeasurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. UncertaTable of ContentsTABLE OF CONTENTS CHAPTER 1 INTRODUCTION * What Is This Book About? *Units *Physical Constants and Their Uncertainties *Dimensionless Quantities *Software *Topics Covered *References *Problems *CHAPTER 2 ASPECTS OF R * Getting R *Using R *Getting Help *Libraries and Packages *Variables *Vectors *Arithmetic *Data Frames *Exporting Data *Importing Data *Internal Mathematical Functions *Writing Your Own Functions *Plotting Mathematical Functions *Loops *Making Decisions *Scripts *Reading Data from Websites *Matrices and Linear Algebra *Some Useful Functions and Operations *Data Frames *Vectors *Probability and Statistics *Plotting *Matrices and Linear Algebra *Data/Functions/Libraries/Packages *Various *References *Problems *CHAPTER 3 STATISTICS * Populations and Samples *Mean, Median, Standard Deviation, and Variance of a Sample *Covariance and Correlation *Visualizing Data *Histograms *Box Plots *Plotting Data Sets *Some Plotting Parameters and Commands *Estimating Population Statistics *Confidence Interval for the Population Mean Using Student's t Variables *Confidence Interval for the Population Variance Using Chi-Square Variables *Confidence Interval Interpretation *Comparing the Means of Two Samples *Testing Data for Normality *Outlier Identification *Modified Thompson Technique *Chauvenet's Criterion *References *Problems *CHAPTER 4 CURVE FITS * Linear Regression *Nonlinear Regression *Kernel Smoothing *References *Problems *CHAPTER 5 UNCERTAINTY OF A MEASURED QUANTITY * What Is Uncertainty? *Random Variables *Measurement Uncertainties *Elemental Systematic Errors *Normal Distributions *Uniform Distributions *Triangular Distributions *Coverage Factors *References *Problems *CHAPTER 6 UNCERTAINTY OF A RESULT CALCULATED USING EXPERIMENTAL DATA * Taylor Series Approach *Coverage Factors *The Kline-McClintock Equation *Balance Checks *References *Problems *CHAPTER 7 TAYLOR SERIES UNCERTAINTY OF A LINEAR REGRESSION CURVE FIT…………………………………………………………………………………………. * Curve-fit Expressions………………………………………………………………………. *Cases to Consider…………………………………………………………………………... *Case 1: No Errors and No Correlations *Case 2: Random Errors Only *Case 3: Random and Systematic Errors *General Linear Regression Theory *Uncertainties in Regression Coefficients *Evaluating Uncertainties with Built-in R functions *References *Problems *CHAPTER 8 MONTE CARLO METHODS * Overall Monte Carlo Approach *Random Number Generation *Accept/Reject Method *Inverse-cdf Method *Random Sampling *Uncertainty of a Measured Variable *Bootstrapping with Internal Functions in R *Monte Carlo Convergence Criteria *Uncertainty of a Result Calculated Using Experimental Data *Uncertainty Bands for Linear Regression Curve Fits *Uncertainty Bands for a Curve Fit with Kernel Smoothing *References *Problems *CHAPTER 9 THE BAYESIAN APPROACH * Bayes Theorem for Probability Density Functions *Bayesian Estimation of the Mean and Standard Deviation of a Normal Population *References *Problems *APPENDIX PROBABILITY DENSITY FUNCTIONS * Univariate pdfs *Normal Distribution *Uniform Distribution *Triangular Distribution *Student's t Distribution *Chi-Square Distribution *Multivariate pdfs *Marginal Distributions *References *

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Book SynopsisExam board: Cambridge Assessment International EducationLevel: A-levelSubject: MathematicsFirst teaching: September 2018First exams: Summer 2020Endorsed by Cambridge Assessment International Education to provide full support for Paper 4 of the syllabus for examination from 2020.Take mathematical understanding to the next level with this accessible series, written by experienced authors, examiners and teachers.- Improve confidence as a mathematician with clear explanations, worked examples, diverse activities and engaging discussion points. - Advance problem-solving, interpretation and communication skills through a wealth of questions that promote higher-order thinking. - Prepare for further study or life beyond the classroom by applying mathematics to other subjects and modelling real-world situations.- Reinforce learning with opportunities for digital practice via links to the Mathematics in Education and Industry's (MEI) Integral platform in the eBooks.**To have full access to the eBooks and Integral resources you must be subscribed to both Boost and Integral. To trial our eBooks and/or subscribe to Boost, visit: www.hoddereducation.co.uk/boost; to view samples of the Integral resources and/or subscribe to Integral, visit integralmaths.org/internationalPlease note that the Integral resources have not been through the Cambridge International endorsement process. Answers to exercise questions are on Cambridge Extras: www.hoddereducation.co.uk/cambridgeextrasThis book covers the syllabus content for Further Probability and Statistics, including continuous random variables, inference using normal and t-distributions, chi-squared tests, non-parametric tests and probability generating functions.

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Book SynopsisExam board: Cambridge Assessment International EducationLevel: A LevelSubject: MathematicsFirst teaching: September 2018First exams: Summer 2020Reinforce learning and deepen understanding of the key concepts covered in the latest syllabus; an ideal course companion or homework book for use throughout the course.- Develop and strengthen skills and knowledge with a wealth of additional exercises that perfectly supplement the Student's Book. - Build confidence with extra practice for each lesson to ensure that a topic is thoroughly understood before moving on. - Ensure students know what to expect with hundreds of rigorous practice and exam-style questions. - Keep track of students' work with ready-to-go write-in exercises. - Save time with all answers available for free online: www.hoddereducation.co.uk/cambridgeextras.This book covers the syllabus content for Probability and Statistics 2, including the Poisson distribution, linear combinations of random variables, continuous random variables, sampling and estimation and hypothesis tests. This title has not been through the Cambridge Assessment International Education endorsement process.Available in this series:Five textbooks fully covering the latest Cambridge International AS & A Level Mathematics syllabus (9709) are accompanied by a Workbook, and Student and Whiteboard eTextbooks.Pure Mathematics 1: Student Textbook (ISBN 9781510421721), Student eTextbook (ISBN 9781510420762), Whiteboard eTextbook (ISBN 9781510420779), Workbook (ISBN 9781510421844)Pure Mathematics 2 and 3: Student Textbook (ISBN 9781510421738), Student eTextbook (ISBN 9781510420854), Whiteboard eTextbook (ISBN 9781510420878), Workbook (ISBN 9781510421851)Mechanics: Student Textbook (ISBN 9781510421745), Student eTextbook (ISBN 9781510420953), Whiteboard eTextbook (ISBN 9781510420977), Workbook (ISBN 9781510421837)Probability & Statistics 1: Student Textbook (ISBN 9781510421752), Student eTextbook (ISBN 9781510421066), Whiteboard eTextbook (ISBN 9781510421097), Workbook (ISBN 9781510421875)Probability & Statistics 2: Student Textbook (ISBN 9781510421776), Student eTextbook (ISBN 9781510421158), Whiteboard eTextbook (ISBN 9781510421165), Workbook (9781510421882)

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Book SynopsisBreaking through the World of Statistics introduces students to statistics and processes that organize, summarize, and apply data to authentic situations. Instruction within the text is based on the principle of learning-by-doing. Each chapter first presents a specific theory or process for problem-solving, and then applies that theory or process to real-world problems.The content features information drawn from diverse disciplines including business and sports. Over the course of the text, students will learn about statistics and their measurement, data organization, descriptive statistics, and probability, both discrete and continuous. They will also study sampling and sampling distributions, confidence intervals, hypothesis testing, analysis of the variance, and both simple and multiple linear regression.The book has been developed to demonstrate that statistics is not a single discipline, but rather has broad applications for numerous areas of life and work. Breaking through the World of Statistics is designed for introductory statistics courses and to successfully prepare students for intermediate-level study.

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Book SynopsisProbability plays an essential role in making decisions in areas such as business, politics, and sports, among others. Professor Rabinowitz, based on many years of teaching, has created a textbook suited for classroom use as well as for self-study that is filled with hundreds of carefully chosen examples based on real-world case studies about sports, elections, drug testing, legal cases, population growth, business, and more. His approach is innovative, practical, and entertaining. Elementary Probability with Applications will serve to enhance classroom instruction, as well as benefit those who want to review the basics of probability at their own pace. The text is used at several colleges and for some high school classes.Trade Review" ""The chosen approach is practical and entertaining. The book is a useful tool for teachers and for anybody interested in basic ideas and applications of classical probability theory."" -EMS Newsletter, June 2005 ""This book would be excellent for a minicourse at a higher level of high school as well as a semester course at the college level."" -Larry White, NCTM, October 2005 ""This book was surprisingly refreshing and did a great job using real situations to motivate techniques for calculating discrete probabilities. . ."" -Technometrics, August 2006 ""This is a very elementary book on probability which has the merits of clear exposition and ... a host of interesting applications, not just the balls-in-urns variety, but to machine reliability, juries, cryptography, birth control, airline management, and sport."" -D.V. Lindley, The Mathematical Gazette, November 2006 ""This book is one of the most elementary introductions to probability available."" -Nieuw Archief voor Wiskunde, March 2008 The writing style is quite pleasant, and many interesting examples are given. These applications are, in my opinion, the most interesting part of the book, and include the usual examples from card and other games, but also examples like the frequency of letters in English or detection of drug users. -R. Van Der Hofstad, Nieuw Archief voor Wiskunde, March 2008"Table of ContentsBasic Concepts in Probability Sample Spaces, Events, and Probabilities Simulations Complementary Events and Mutually Exclusive Events Some Probability Rules Problem Solving Problems Conditional Probability and the Multiplication Rule Conditional Probability Multiplication Rule Problems Independence Independence A Technique for Finding P(A or B or C or ...) Problems Combining the Addition and Multiplication Rules Combining the Addition and Multiplication Rules Bayes' Formula Trees Problems Combining the Addition and Multiplication Rules-Applications Simpson's Paradox Randomized Response Designs Applications in Cryptology Hardy-Weinberg Principle Problems Random Variables, Distributions, and Expected Values Random Variables, Distributions, and Expected Values Problems Joint Distributions and Conditional Expectations Joint Distributions Independent Random Variables Conditional Distributions Conditional Expectations Problems Sampling without Replacement Counting Formula Probabilities for Sampling without Replacement Problems Sampling with Replacement Binomial Model Problems Sampling with Replacement (Continued) Binomial Model (Continued) Problems Binomial Tests Introduction Binomial Tests Problems Appendix Short Answers to Selected Exercises Bibliography Index

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Book SynopsisIn the view of many probabilists, author Anatolii Puhalskii's research results stand among the most significant achievements in the modern theory of large deviations. In fact, his work marked a turning point in the depth of our understanding of the connections between the large deviation principle (LDP) and well-known methods for establishing weak convergence results.Large Deviations and Idempotent Probability expounds upon the recent methodology of building large deviation theory along the lines of weak convergence theory. The author develops an idempotent (or maxitive) probability theory, introduces idempotent analogues of martingales (maxingales), Wiener and Poisson processes, and Ito differential equations, and studies their properties. The large deviation principle for stochastic processes is formulated as a certain type of convergence of stochastic processes to idempotent processes. The author calls this large deviation convergence.The approach to establishing large deviation convergence uses novel compactness arguments. Coupled with the power of stochastic calculus, this leads to very general results on large deviation asymptotics of semimartingales. Large and moderate deviation asymptotics are treated in a unified manner. Starting with the foundations of idempotent measure theory and culminating in applications to large deviation asymptotics of queueing systems, Large Deviations and Idempotent Probability offers an outstanding opportunity to examine both the development of a remarkable approach and recently discovered results as presented by one of the foremost leaders in the field.Table of ContentsIDEMPOTENT PROBABILITY THEORY: Idempotent Probability Measures. Maxingales. LARGE DEVIATION CONVERGENCE: Large Deviation Convergence in Tihonov Spaces. The Method of Finite-Dimensional Distributions. The Method of the Maxingale Problem. APPLICATIONS.

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Book SynopsisThis volume discusses an important area of statistics and highlights the most important statistical advances. It is divided into four sections: statistics in the life and medical sciences, business and social science, the physical sciences and engineering, and theory and methods of statistics.Trade Review"This impressive volume is highly recommended for all statisticians as well as anybody who is interested in applying statistics in any science."- Perceptual and Motor Skills, December 2001 "Even though statistics is used in different disciplines in different contexts, this volume reveals an underlying cohesiveness of the field. With the growth of computer power and storage, researchers are analyzing large and complex data sets, leading to the need for new methods of model selection. Material is presented clearly and informatively. Extensive author and subject indexes. A valuable addition to academic libraries."--CHOICE Magazine, May 2002Table of ContentsIntroduction. Statistics in the Life and Medical Sciences. Statistics in Business and Social Science. Statistics in the Physical Sciences and Engineering. Theory and Methods. Author Index. Subject Index.

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Book SynopsisExamines Critical Decisions for Transitioning Lab Science to a Clinical SettingThe development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the patients is decreasing. As a result, translational medicine (TM) is becoming increasingly important in the healthcare industry – a means of maximizing the consideration and use of information collected as compounds transition from initial lab discovery, through pre-clinical testing, early clinical trials, and late confirmatory studies that lead to regulatory approval of drug release to patients. Translational Medicine: Strategies and Statistical Methods suggests a process for transitioning from the initial lab discovery to the patient’s bedside with minimal disconnect and offers a comprehensive review of statistical design and methodology commonly employed in this bench-to-bedside research. Documents Alternative Research Approaches for Faster and More Accurate Data Judgment CallsElaborating on how to introduce TM into clinical studies, this authoritative work presents a keen approach to building, executing, and validating statistical models that consider data from various phases of development. It also delineates a truly translational example to help bolster understanding of discussed concepts. This comprehensive guide effectively demonstrates how to overcome obstacles related to successful TM practice. It contains invaluable information for pharmaceutical scientists, research executives, clinicians, and biostatisticians looking to expedite successful implementation of this important process.Trade Review"This is one of the few books published on the strategies for TM. The chapters are carefully coordinated and can be read on their own; they are independent but are still well coordinated. The book contains a good outline of the different aspects of TM and contains a series of good examples. ... this book will provide interesting reading for readers from a variety of backgrounds. Overall, this book contains a good foundation for anyone working in this area." -Richardus Vonk, Pharmaceutical Statistics "... chapters follow the natural order from discovery research to final implementation to intermediate validation." -Erik Cobo, International Society for Clinical BiostatisticsTable of ContentsTranslational Medicine: Strategies and Statistical Methods. Strategic Concepts in Translational Medicine. Design and Analysis Approaches for Discovery Translational Medicine. Biomarker Development. Targeted Clinical Trials. Statistical Methods in Translational Medicine. NonparametricMethods in Translational Research. Model Selection/Validation. Translation in Clinical Information between Populations—Bridging Studies. Translation in Clinical Technology—Traditional Chinese Medicine. Index.

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Book Synopsis

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Book SynopsisBayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata’s data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.Trade Review"… the first comprehensive guide to employing Bayesian methods using Stata statistical software. … until this book, there has been no unified presentation of how to implement Bayesian methods using Stata. … A nice feature of the book is the use of real data … I recommend it for Stata users who wish to employ Bayesian modeling within the Stata environment."—International Statistical Review, 2015Table of ContentsList of figures. List of tables. Preface. Acknowledgments. The problem of priors. Evaluating the posterior. Metropolis–Hastings. Gibbs sampling. Assessing convergence. Validating the Stata code and summarizing the results. Using WinBUGS for model fitting. Model checking. Model selection. Further case studies. Writing Stata programs for specific Bayesian analysis. A Standard distributions. References . Author index . Subject index.

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Book SynopsisOne Hundred Nineteen Stata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003.Table of ContentsIntroducing Stata tips. Stata tip 1: The eform() option of regress. Stata tip 2: Building with floors and ceilings. Stata tip 3: How to be assertive. Stata tip 4: Using display as an online calculator. Stata tip 5: Ensuring programs preserve dataset sort order. Stata tip 6: Inserting awkward characters in the plot. Stata tip 7: Copying and pasting under Windows. Stata tip 8: Splitting time-span records with categorical time-varying covariates. Stata tip 9: Following special sequences. Stata tip 10: Fine control of axis title positions. Stata tip 11: The nolog option with maximum-likelihood modeling commands. Stata tip 12: Tuning the plot region aspect ratio. Stata tip 13: generate and replace use the current sort order. Stata tip 14: Using value labels in expressions. Stata tip 15: Function graphs on the fly. Stata tip 16: Using input to generate variables. Stata tip 17: Filling in the gaps. Stata tip 18: Making keys functional. Stata tip 19: A way to leaner, faster graphs. Stata tip 20: Generating histogram bin variables. Stata tip 21: The arrows of outrageous fortune. Stata tip 22: Variable name abbreviation. Stata tip 23: Regaining control over axis ranges. Stata tip 24: Axis labels on two or more levels. Stata tip 25: Sequence index plots. Stata tip 26: Maximizing compatibility between Macintosh and Windows. Stata tip 27: Classifying data points on scatter plots. Stata tip 28: Precise control of dataset sort order. Stata tip 29: For all times and all places. Stata tip 30: May the source be with you. Stata tip 31: Scalar or variable? The problem of ambiguous names. Stata tip 32: Do not stop. Stata tip 33: Sweet sixteen: Hexadecimal formats and precision problems. Stata tip 34: Tabulation by listing. Stata tip 35: Detecting whether data have changed. Stata tip 36: Which observations? Stata tip 37: And the last shall be first. Stata tip 38: Testing for groupwise heteroskedasticity. Stata tip 39: In a list or out? In a range or out? Stata tip 40: Taking care of business. Stata tip 41: Monitoring loop iterations. Stata tip 42: The overlay problem: Offset for clarity. Stata tip 43: Remainders, selections, sequences, extractions: Uses of the modulus. Stata tip 44: Get a handle on your sample. Stata tip 45: Getting those data into shape. Stata tip 46: Step we gaily, on we go. Stata tip 47: Quantile–quantile plots without programming. Stata tip 48: Discrete uses for uniform(). Stata tip 49: Range frame plots. Stata tip 50: Efficient use of summarize. Stata tip 51: Events in intervals. Stata tip 52: Generating composite categorical variables. Stata tip 53: Where did my p-values go? Stata tip 54: Post your results. Stata tip 55: Better axis labeling for time points and time intervals. Stata tip 56: Writing parameterized text files. Stata tip 57: How to reinstall Stata. Stata tip 58: nl is not just for nonlinear models. Stata tip 59: Plotting on any transformed scale. Stata tip 60: Making fast and easy changes to files with filefilter. Stata tip 61: Decimal commas in results output and data input. Stata tip 62: Plotting on reversed scales. Stata tip 63: Modeling proportions. Stata tip 64: Cleaning up user-entered string variables. Stata tip 65: Beware the backstabbing backslash. Stata tip 66: ds—A hidden gem. Stata tip 67: J() now has greater replicating powers. Stata tip 68: Week assumptions. Stata tip 69: Producing log files based on successful interactive commands. Stata tip 70: Beware the evaluating equal sign. Stata tip 71: The problem of split identity, or how to group dyads. Stata tip 72: Using the Graph Recorder to create a pseudograph scheme. Stata tip 73: append with care! Stata tip 74: firstonly, a new option for tab2. Stata tip 75: Setting up Stata for a presentation. Stata tip 76: Separating seasonal time series. Stata tip 77: (Re)using macros in multiple do-files. Stata tip 78: Going gray gracefully: Highlighting subsets and downplaying substrates. Stata tip 79: Optional arguments to options. Stata tip 80: Constructing a group variable with specified group sizes. Stata tip 81: A table of graphs. Stata tip 82: Grounds for grids on graphs. Stata tip 83: Merging multilingual datasets. Stata tip 84: Summing missings. Stata tip 85: Looping over nonintegers. Stata tip 86: The missing() function. Stata tip 87: Interpretation of interactions in nonlinear models. Stata tip 88: Efficiently evaluating elasticities with the margins command. Stata tip 89: Estimating means and percentiles following multiple imputation. Stata tip 90: Displaying partial results. Stata tip 91: Putting unabbreviated varlists into local macros. Stata tip 92: Manual implementation of permutations and bootstraps. Stata tip 93: Handling multiple y axes on twoway graphs. Stata tip 94: Manipulation of prediction parameters for parametric survival regression. Stata tip 95: Estimation of error covariances in a linear model. Stata tip 96: Cube roots. Stata tip 97: Getting at ρ’s and σ’s. Stata tip 98: Counting substrings within strings. Stata tip 99: Taking extra care with encode. Stata tip 100: Mata and the case of the missing macros. Stata tip 101: Previous but different. Stata tip 102: Highlighting specific bars. Stata tip 103: Expressing confidence with gradations. Stata tip 104: Added text and title options. Stata tip 105: Daily dates with missing days. Stata tip 106: With or without reference. Stata tip 107: The baseline is now reported. Stata tip 108: On adding and constraining. Stata tip 109: How to combine variables with missing values. Stata tip 110: How to get the optimal k-means cluster solution. Stata tip 111: More on working with weeks. Stata tip 112: Where did my p-values go? (Part 2) Stata tip 113: Changing a variable’s format: What it does and does not mean. Stata tip 114: Expand paired dates to pairs of dates. Stata tip 115: How to properly estimate the multinomial probit model with heteroskedastic errors. Stata tip 116: Where did my p-values go? (Part 3). Stata tip 117: graph combine—Combining graphs. Stata tip 118: Orthogonalizing powered and product terms using residual centering. Stata tip 119: Expanding datasets for graphical ends.

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Book SynopsisMeta-analysis allows researchers to combine the results of several studies into a unified analysis that provides an overall estimate of the effect of interest. This collection of articles from the Stata Journal and Stata Technical Bulletin will be indispensable to researchers who wish to conduct meta-analyses using Stata and learn about the full range of user-written Stata meta-analysis commands. With these articles and the associated Stata software, you gain access to the statistical methods behind the rapid increase in the number of meta-analyses reported in the social and medical literature.Collectively, the articles provide a detailed description of a range of meta-analytic methods. They show how to conduct and interpret meta-analyses; how to produce highly flexible graphical displays; how to use meta-regression; how to examine bias; how to conduct individual participant data meta-analysis; and how to conduct multivariate meta-analysis. This edition also contains three articles on network metaanalysis, a major recent development in meta-analysis methodology.Table of ContentsMeta-analysis in Stata: metan, metaan, metacum, and metap. Meta-regression: metareg. Investigating bias in meta-analysis: metafunnel, confunnel, metabias, metatrim, and extfunnel. Multivariate meta-analysis: metandi, mvmeta. Network meta-analysis: indirect, network package, network graphs package.

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Book SynopsisThis volume is a sometimes serious and sometimes whimsical retrospective of Stata, its development, and its use over the last 30 years.The view from the inside opens with an essay by Bill Gould, Stata's president and cofounder, that discusses the challenges and concepts that guided the design and implementation of Stata. This is followed by an interview of Bill by Joe Newton that discusses Bill's early interest in computing, his early work on a program for matching prom dates in the days when you had to purchase time on computers, and further exploration of the guiding principles behind Stata. Finally, Sean Becketti, Stata's first employee, delves into the not-to-be-missed culture of Stata in its infancy.The view from the outside comprises 14 essays by prominent researchers and members of the Stata community. Most discuss Stata's use and evolution in disciplines such as behavioral sciences, business, economics, epidemiology, time series, political science, public health, public policy, veterinary epidemiology, and statistics. Some take a sweeping overview. Others are more intimate personal recollections. Mostly, we simply wanted to celebrate the relationship between Stata users and Stata software. We hope that this volume holds something interesting for everyone.Table of ContentsA VISION FROM INSIDE. Initial thoughts. A conversation with William Gould. A VISION FROM OUTSIDE. Then and now. 25 years of Statistics with Stata. Stata’s contribution to epidemiology. The evolution of veterinary epidemiology. On learning introductory biostatistics and Stata. Stata and public health in Ohio. Statistics research and Stata in my life. Public policy and Stata. Microeconometrics and Stata over the past 30 years. Stata enabling state-of-the-art research in accounting, business, and finance. Stata and political science. New tools for psychological researchers. History of Stata.

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Book SynopsisHealth Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.After the overview chapters, the book provides excellent introductions to a series of topics aimed specifically at those analyzing healthcare expenditure and use data. The basic topics of linear regression, the generalized linear model, and log and Box-Cox models are covered with a tight focus on the problems presented by these data. Using this foundation, the authors cover the more advanced topics of models for continuous outcome with mass points, count models, and models for heterogeneous effects. Finally, they discuss endogeneity and how to address inference questions using data from complex surveys.The authors use their formidable experience to guide readers toward useful methods and away from less recommended ones. Their discussion of "health econometric myths" and the chapter presenting a framework for approaching health econometric estimation problems are especially useful for this aspect.Table of ContentsIntroduction Framework MEPS data The linear regression model: Specification and checks Generalized linear models Log and Box–Cox models Models for continuous outcomes with mass at zero Count models Models for heterogeneous effects Endogeneity Design effects

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Book SynopsisThe Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge".The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp. Table of ContentsThe mechanics of using Mata. A programmer’s tour of Mata. Mata’s programming statements. Mata’s expressions. Mata’s variable types. Mata’s strict option and Mata’s pragmas. Mata’s function arguments. Programming example: n_choose_k() three ways. Mata’s structures. Programming example: Linear regression. Mata’s classes. Programming example: Linear regression 2. Better variable types. Programming constants. Mata’s associative arrays. Programming example: Sparse matrices. Programming example: Sparse matrices, continued. The Mata Reference Manual. Writing Mata code to add new commands to Stata. Mata’s storage type for complex numbers. How Mata differs from C and C++. Three-dimensional arrays (advanced use of pointers).

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Book SynopsisStata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research.Table of ContentsIntroducing Stata tips Stata tip 120: Certifying subroutines, M. L. Buis Stata tip 121: Box plots side by side, N. J. Cox Stata tip 122: Variable bar widths in two-way graphs, B. Jann Stata tip 123: Spell boundaries, N. J. Cox Stata tip 124: Passing temporary variables to subprograms, M. L. Buis Stata tip 125: Binned residual plots for assessing the fit of regression models for binary outcomes, J. Kasza Stata tip 126: Handling irregularly spaced high-frequency transactions data, C. F. Baum and S. Bibo Stata tip 127: Use capture noisily groups, R. B. Newson Stata tip 128: Marginal effects in log-transformed models: A trade application, L. J. Uberti Stata tip 129: Efficiently processing textual data with Stata’s new Unicode features, A. Koplenig Stata tip 130: 106610 and all that: Date variables that need to be fixed., N. J. Cox Stata tip 131: Custom legends for graphs that use translucency, T. P. Morris Stata tip 132: Tiny tricks and tips on ticks, N. J. Cox and V. Wiggins Stata tip 133: Box plots that show median and quartiles only, N. J. Cox Stata tip 134: Multiplicative and marginal interaction effects in nonlinear models, W. H. Dow, E. C. Norton, and J. T. Donahoe Stata tip 135: Leaps and bounds, M. L. Buis Stata tip 136: Between-group comparisons in a scatterplot with weighted markers, A. Musau Stata tip 137: Interpreting constraints on slopes of rank-deficient design matrices, D. Christodoulou Stata tip 138: Local macros have local scope, N. J. Cox Stata tip 139: The by() option of graph can work better than graph combine, N. J. Cox Stata tip 140: Shorter or fewer category labels with graph bar, N. J. Cox Stata tip 141: Adding marginal spike histograms to quantile and cumulative distribution plots, N. J. Cox Stata tip 142: joinby is the real merge m:m, D. Mazrekaj and J. Wursten Stata tip 143: Creating donut charts in Stata, A. Musau Stata tip 144: Adding variable text to graphs that use a by() option, N. J. Cox Stata tip 145: Numbering weeks within months, N. J. Cox Stata tip 146: Using margins after a Poisson regression model to estimate the number of events prevented by an intervention, M. Falcaro, R. B. Newson, and P. Sasieni Erratum: Stata tip 145: Numbering weeks within months, N. J. Cox Stata tip 147: Porting downloaded packages between machines, R. B. Newson Stata tip 148: Searching for words within strings, N. J. Cox Stata tip 149: Weighted estimation of fixed-effects and first-differences models, J. Gardner Stata tip 150: When is it appropriate to xtset a panel dataset with panelvar only? C. Lazzaro Stata tip 151: Puzzling out some logical operators, N. J. Cox Stata tip 152: if and if: When to use the if qualifier and when to use the if command, N. J. Cox and C. B. Schechter

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Book SynopsisFranz Buscha's book, Graphs Everyone Should Know and How to Create Them in Stata, is written for anyone who uses Stata to make graphs. Beginners will find a complete collection of tools for effectively visualizing their data and results. Experienced Stata users are certain to learn some new tricks as well.The chapters of the book are organized into four main sections: graphs for univariate data, graphs for bivariate data, graphs for multivariate data, and special graphs. Each chapter introduces a type of graph, explains when and why it is useful for visualizing a particular kind of data, demonstrates how to create that graph using Stata, and shows a few variations. The special graph section covers topics such as how to create maps, plot equations, create animated graphs, and create other specialty graphs.Readers will find it easy to learn to make graphs by example. Buscha demonstrates most graphs using datasets that are installed with your copy of Stata, so it is straightforward to follow along. He also clearly pairs each graph with the command used to create it in a box just above the graph. If you find a graph that you wish to create with your own data, you can take the command from the box and replace the variable names in the example with your own variable names.Buscha's book has two unique features that distinguish it from other books about Stata graphs. First, the book's goal is to clearly demonstrate how to effectively visualize different kinds of data and results from models using only necessary features. It focuses on important options but does not discuss all the options available for customizing each graph. Second, the book introduces many community-contributed graph commands that are freely available and can be downloaded from the internet. Readers may be unaware of these commands before finding them in this book, and they can learn how to use them quickly rather than spend time trying to write custom code for themselves.Graphs Everyone Should Know and How to Create Them in Stata is a reference you will use again and again as you visualize different types of data. You will quickly find the graphs that are applicable to your data and the Stata commands necessary to create them.

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Book SynopsisWhy can't we tickle ourselves? Which properties give you the best chance of winning Monopoly? What would happen if you fell into a black hole? Is it possible to hurt your brain if you think too much? In this entertaining and enlightening tour of day-to-day life, award-winning writer and scientist Robert Matthews tackles everything from the puzzling maths of odd socks to the real 'string theory' mystery: how does string acquire all those unwanted knots?Trade Review"simply fabulous." Jon

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