Probability and statistics Books

Book SynopsisAn Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables.With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research.Table of ContentsI The basics 1. Getting started 2. Getting help—and more 3. Command syntax II Data management 4. Variables 5. Getting data in and out of Stata 6. Adding explanatory text to data 7. Calculations 8. Commands affecting data structure 9. Taking good care of your data III Analysis 10. Description and simple analysis 11. Regression analysis 12. Time-to-event data 13. Power, precision, and sample-size analysis 14. Measurement and diagnosis 15. Miscellaneous IV Graphs 16. Graphs V Advanced topics 17. Advanced topics

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Book SynopsisInterpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and pwcompare commands. This new edition reflects new and enhanced features added to Stata, most importantly the ability to label statistical output using value labels associated with factor variables. As a result, output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature, emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally, this second edition illustrates other new features, such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.Table of ContentsIntroduction; Continuous predictors: Linear; Continuous predictors: Polynomials; Continuous predictors: Piecewise models; Continuous by continuous interactions; Continuous by continuous by continuous interactions; Categorical predictors; Categorical by categorical interactions; Categorical by categorical by categorical interactions; Linear by categorical interactions; Polynomial by categorical interactions; Piecewise by categorical interactions; Continuous by continuous by categorical interactions; Continuous by categorical by categorical interactions; Multilevel models; Time as a continuous predictor; Time as a categorical predictor; Nonlinear models; Complex survey data

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Book SynopsisStrange happenstances and chance encounters have puzzled us for centuries. This fun and fascinating book takes readers on a journey through the mathematics behind coincidences both famous and never-before-examined. From peculiar patterns in geometry and calculus to the famous Waring Problem, and other astonishing numerical curiosities, The Call of Coincidence begins by examining the mathematical properties that underpin everything there is. Next, author Owen O’Shea – along with fictional guides Charlie Chance and the enigmatic Dr. Moogle – reveals surprising connections and correlations throughout history, including numerical coincidences behind the reign of King Richard III, the sinking of the SS Edmund Fitzgerald, the 1996 FIFA World Cup, and much, much more. By investigating the properties, puzzles, and problems within, you will gain a newfound appreciation for the beautiful simplicity of mathematics in its many forms. Featuring surprising trivia gems alongside serious questions like why there is something rather than nothing, readers will be enriched by this exploration of remarkable number coincidences and the mathematics that make them possible – and probable.

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Book SynopsisThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.Trade Review“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)Table of Contents1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.

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Book SynopsisThis book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects. Some applications in bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow.Table of ContentsDescriptive Statistics.- Measures of Location.- Measures of Spread.- Measures of Skewness and Kurtosis.

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Book SynopsisUncertainty in Optimization.- Information Structures and Feasibility.- Modeling the Objective Function.- Scenario Tree Generation, With Michal Kaut and Jamie Fairbrother.- High-Dimentional Dependent Randomness. With Zhaoxia Guo and Michal Kaut.- Service Network Design, With Arnt-Gunnar Lium and Teodor Gabriel Crainic.- A Multi-dimensional Newsboy Problem with Substitution, With Hajnalka Vaagen.- References.- Index.

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Book SynopsisThis is the fifth edition of a very successful textbook on clinical trials methodology, written by recognized leaders who have long and extensive experience in all areas of clinical trials. The three authors of the first four editions have been joined by two others who add great expertise. A chapter on regulatory issues has been included and the chapter on data monitoring has been split into two and expanded. Many contemporary clinical trial examples have been added. There is much new material on adverse events, adherence, issues in analysis, electronic data, data sharing and international trials.This book is intended for the clinical researcher who is interested in designing a clinical trial and developing a protocol. It is also of value to researchers and practitioners who must critically evaluate the literature of published clinical trials and assess the merits of each trial and the implications for the care and treatment of patients. The authors use numerous examples of published clinical trials to illustrate the fundamentals.The text is organized sequentially from defining the question to trial closeout. One chapter is devoted to each of the critical areas to aid the clinical trial researcher. These areas include pre-specifying the scientific questions to be tested and appropriate outcome measures, determining the organizational structure, estimating an adequate sample size, specifying the randomization procedure, implementing the intervention and visit schedules for participant evaluation, establishing an interim data and safety monitoring plan, detailing the final analysis plan and reporting the trial results according to the pre-specified objectives.Although a basic introductory statistics course is helpful in maximizing the benefit of this book, a researcher or practitioner with limited statistical background would still find most if not all the chapters understandable and helpful. While the technical material has been kept to a minimum, the statistician may still find the principles and fundamentals presented in this text useful. Trade Review“This book aims to assist investigators in improving the quality of their clinical trials and protocols by discussing fundamental concepts with examples and in-depth review of the literature. … This is a valuable resource for students, clinicians, and researchers who are interested in designing a clinical trial or in critically appraising the published literature on clinical trials.” (Pooja Sethi, Doody’s Book Reviews, December, 2015)Table of ContentsIntroduction to Clinical Trials.- Ethical Issues.- What is the Question?.- Study Population.- Basic Study Design.- The Randomization Process.- Blinding.- Sample Size.- Baseline Assessment.- Recruitment of Study Participants.- Data Collection and Quality Control.- Assessment and Reporting of Harm.- Assessment of Health Related Quality of Life.- Participant Adherence.- Survival Analysis.- Monitoring Committee Structure & Function.- Statistical Methods Used in Interim Monitoring.- Issues in Data Analysis.- Closeout.- Reporting and Interpreting of Results.- Multicenter Trials.- Regulatory Issues.

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Book SynopsisThis textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.Trade Review“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. … a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students.” (Xiannong Meng, Computing Reviews, September, 2017)“This textbook on practical data analytics unites fundamental principles, algorithms, and data. … this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors’ research experience in data science concepts and the teaching skills in various areas. … The text is eminently suitable for self-study and an exceptional resource for practitioners.” (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017) Table of ContentsIntroduction.- Data Mapping and Data Dictionaries.- Scalable Algorithms and Associative Statistics.- Hadoop and MapReduce.- Data Visualization.- Linear Regression Methods.- Healthcare Analytics.- Cluster Analysis.- k-Nearest Neighbor Prediction Functions.- The Multinomial Naive Bayes Prediction Function.- Forecasting.- Real-time Analytics.

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Book SynopsisStochastic processes are tools used widely by statisticians and researchers working in the mathematics of finance. This book for self-study provides a detailed treatment of conditional expectation and probability, a topic that in principle belongs to probability theory, but is essential as a tool for stochastic processes. The book centers on exercises as the main means of explanation.Trade ReviewThis book fulfils its aim of providing good and interesting material for advanced undergraduate study. The Times Higher Education Supplement This is probably one of the best books to begin learning about the sometimes complex topic of stochastic calculus and stochastic processes from a more mathematical approach. Some literature are often accused of unnecessarily complicating the subject when applied to areas of finance. With this book you are allowed to explore the rigorous side of stochastic calculus, yet maintain a physical insight of what is going on. The authors have concentrated on the most important and useful topics that are encountered in common physical and financial systems www.quantnotes.com Table of Contents1. Review of Probability.- 1.1 Events and Probability.- 1.2 Random Variables.- 1.3 Conditional Probability and Independence.- 1.4 Solutions.- 2. Conditional Expectation.- 2.1 Conditioning on an Event.- 2.2 Conditioning on a Discrete Random Variable.- 2.3 Conditioning on an Arbitrary Random Variable.- 2.4 Conditioning on a ?-Field.- 2.5 General Properties.- 2.6 Various Exercises on Conditional Expectation.- 2.7 Solutions.- 3. Martingales in Discrete.- 3.1 Sequences of Random Variables.- 3.2 Filtrations.- 3.3 Martingales.- 3.4 Games of Chance.- 3.5 Stopping Times.- 3.6 Optional Stopping Theorem.- 3.7 Solutions.- 4. Martingale Inequalities and Convergence.- 4.1 Doob’s Martingale Inequalities.- 4.2 Doob’s Martingale Convergence Theorem.- 4.3 Uniform Integrability and L1 Convergence of Martingales.- 4.4 Solutions.- 5. Markov Chains.- 5.1 First Examples and Definitions.- 5.2 Classification of States.- 5.3 Long-Time Behaviour of Markov Chains: General Case.- 5.4 Long-Time Behaviour of Markov Chains with Finite State Space.- 5.5 Solutions.- 6. Stochastic Processes in Continuous Time.- 6.1 General Notions.- 6.2 Poisson Process.- 6.2.1 Exponential Distribution and Lack of Memory.- 6.2.2 Construction of the Poisson Process.- 6.2.3 Poisson Process Starts from Scratch at Time t.- 6.2.4 Various Exercises on the Poisson Process.- 6.3 Brownian Motion.- 6.3.1 Definition and Basic Properties.- 6.3.2 Increments of Brownian Motion.- 6.3.3 Sample Paths.- 6.3.4 Doob’s Maximal L2 Inequality for Brownian Motion.- 6.3.5 Various Exercises on Brownian Motion.- 6.4 Solutions.- 7. Itô Stochastic Calculus.- 7.1 Itô Stochastic Integral: Definition.- 7.2 Examples.- 7.3 Properties of the Stochastic Integral.- 7.4 Stochastic Differential and Itô Formula.- 7.5 Stochastic Differential Equations.- 7.6 Solutions.

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Book SynopsisThis book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art!The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. This book is a translation of the original German 2nd edition Statistik angewandt mit Excel by Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.Table of ContentsPart 1 - Basic knowledge and tools to apply statistics.- Statistics is fun.- Excel: A brief introduction and the statistical possibilities.- Part 2 - Describe, nothing but describe.- Mean values: How people and objects behave on average.- Scatter: The deviation from average behavior.- Graphs: The possibility to represent data visually.- Correlation: Of the correlation.- Ratio and index numbers: The chance to generate new things from old knowledge.- Part 3 - From Few to All.- Of Data and the Truth.- Hypotheses: Just a specification of the question.- Normal distribution and other test distributions.- Hypothesis testing: What is Valid?.- Part 4 - Procedures for Testing Hypotheses.- The Mean Test.- The Test for Difference of Means in Independent Samples.- The Test for Difference of Means in Dependent Samples.- The Analysis of Variance for Testing for Group Differences in More than Two Groups.- The Test for Correlation in Metric, Ordinal, and Nominal Data.- Further Test Procedures for Nominal Variables.- Summary Part IV - Overview of testing procedures.- Part 5 - Regression analysis.- The linear single regression.- The multiple regression analysis.- Part 6 - What's next.- Brief report on a research question.- Further statistical procedures.- Interesting and further statistics books.- Another data set to practice on - Intern of a company.- Appendix.

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Book SynopsisThe book provides an introduction to advanced topics in stochastic processes and related stochastic analysis, and combines them with a sound presentation of the fundamentals of financial mathematics. It is wide-ranging in content, while at the same time placing much emphasis on good readability, motivation, and explanation of the issues covered. Financial mathematical topics are first introduced in the context of discrete time processes and then transferred to continuous-time models. The basic construction of the stochastic integral and the associated martingale theory provide fundamental methods of the theory of stochastic processes for the construction of suitable stochastic models of financial mathematics, e.g. using stochastic differential equations. Central results of stochastic analysis such as the Itô formula, Girsanov's theorem and martingale representation theorems are of fundamental importance in financial mathematics, e.g. for the risk-neutral valuation formula (Black-Scholes formula) or the question of the hedgeability of options and the completeness of market models. Chapters on the valuation of options in complete and incomplete markets and on the determination of optimal hedging strategies conclude the range of topics.Advanced knowledge of probability theory is assumed, in particular of discrete-time processes (martingales, Markov chains) and continuous-time processes (Brownian motion, Lévy processes, processes with independent increments, Markov processes). The book is thus suitable for advanced students as a companion reading and for instructors as a basis for their own courses.This book is a translation of the original German 1st edition Stochastische Prozesse und Finanzmathematik by Ludger Rüschendorf, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com) and in a subsequent editing, improved by the author. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.Table of ContentsOption pricing in models in discrete time.- Scorohod's embedding theorem and Donsker's theorem.- Stochastic integration.- Elements of stochastic analysis.- Option pricing in complete and incomplete markets.- Utility optimization, minimum distance martingales, and utility indiff.- Variance-minimum hedging.

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Book SynopsisThis 2nd edition compendium contains and explains essential statistical formulas within an economic context. Expanded by more than 100 pages compared to the 1st edition, the compendium has been supplemented with numerous additional practical examples, which will help readers to better understand the formulas and their practical applications. This statistical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice.The topics presented include, but are not limited to: statistical signs and symbols, descriptive statistics, empirical distributions, ratios and index figures, correlation analysis, regression analysis, inferential statistics, probability calculation, probability distributions, theoretical distributions, statistical estimation methods, confidence intervals, statistical testing methods, the Peren-Clement index, and the usual statistical tables.Given its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.Table of ContentsStatistical Signs and Symbols.- Descriptive Statistics.- Inferential Statistics.- Probability Calculation.- Statistical Tables.- Bibliography.- Index.

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Book SynopsisModelling with the Itô integral or stochastic differential equations has become increasingly important in various applied fields, including physics, biology, chemistry and finance. However, stochastic calculus is based on a deep mathematical theory.This book is suitable for the reader without a deep mathematical background. It gives an elementary introduction to that area of probability theory, without burdening the reader with a great deal of measure theory. Applications are taken from stochastic finance. In particular, the Black-Scholes option pricing formula is derived. The book can serve as a text for a course on stochastic calculus for non-mathematicians or as elementary reading material for anyone who wants to learn about Itô calculus and/or stochastic finance.Trade Review"This book under review can be determined as a very successful work ... the author's choice of the material is done with good taste and expertise ... It can be strongly recommended to graduate students and practitioners in the field of finance and economics." Mathematics Abstracts, 2000 "... this is a well-written book, which makes the difficult object of mathematical finance easy to understand also for non-mathematicians. It might be useful for economics students and all practitioners in the field of finance who are interested in the mathematical methodology behind the Black-Scholes model." Statistical Papers, 2000Table of ContentsPreliminaries - basic concepts from probability theory; stochastic processes; Brownian motion; conditional expectation; Martingales; the stochastic integral - the Riemann and Riemann-Stieltjes; integrals; the Ito integral; the Ito lemma; the Stratonovich and other integrals; stochastic differential equations - deterministic differential equations; Ito stochastic differential equations; the general linear differential equation; numerical solution; applications of stochastic calculus in finance - the Black-Scholes option-pricing formula; a useful technique - change of measure. Appendices: modes of convergence; inequalities; non-differentiability and unbounded variation of Brownian sample paths; proof of the existence of the general Ito stochastic integral; the Radon-Nikodym theorem; proof of the existence and uniqueness of the conditional expectation.

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Book SynopsisThe main objective of this text is to facilitate a student's smooth learning transition from a course on probability to its applications in various areas. To achieve this goal, students are encouraged to experiment numerically with problems requiring computer solutions.

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Book SynopsisThis book provides an undergraduate-level introduction to discrete and continuous-time Markov chains and their applications, with a particular focus on the first step analysis technique and its applications to average hitting times and ruin probabilities. It also discusses classical topics such as recurrence and transience, stationary and limiting distributions, as well as branching processes. It first examines in detail two important examples (gambling processes and random walks) before presenting the general theory itself in the subsequent chapters. It also provides an introduction to discrete-time martingales and their relation to ruin probabilities and mean exit times, together with a chapter on spatial Poisson processes. The concepts presented are illustrated by examples, 138 exercises and 9 problems with their solutions.Table of ContentsProbability Background.- Gambling Problems.- Random Walks.- Discrete-Time Markov Chains.- First Step Analysis.- Classification of States.- Long-Run Behavior of Markov Chains.- Branching Processes.- Continuous-Time Markov Chains.- Discrete-Time Martingales.- Spatial Poisson Processes.- Reliability Theory.

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Book SynopsisThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning. Table of ContentsChapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.

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Book SynopsisA Waterstones Best Popular Science Book of 2023'Delightfully clear and vivid to read...A splendid book! Philip Pullman'Absolutely fascinating' James O'Brien'An exceptional book - readable, funny and more needed than ever' Dr Chris van Tulleken, bestselling author of Ultra-Processed PeopleAre you more likely to become a professional footballer if your surname is Ball?· How can you be one hundred per cent sure you will win a bet?· Why did so many Pompeiians stay put while Mount Vesuvius was erupting?· How do you prevent a nuclear war?Ever since the dawn of human civilisation, we have been trying to make predictions about what's in store for us. We do this on a personal level, so that we can get on with our lives efficiently (should I hang my laundry out to dry, or will it rain?). But we also have to predict on a much larger scale, often for the good of our broader society (how can we spot economic downturns or prevent terrorist attacks?). For just as long, we have been getting it wrong. From religious oracles to weather forecasters, and from politicians to economists, we are subjected to poor predictions all the time. Our job is to separate the good from the bad. Unfortunately, the foibles of our own biology - the biases that ultimately make us human - can let us down when it comes to making rational inferences about the world around us. And that can have disastrous consequences.How to Expect the Unexpected will teach you how and why predictions go wrong, help you to spot phony forecasts and give you a better chance of getting your own predictions correct.Trade ReviewA vivid, wide-ranging and delightful guide to the light and the dark side of prediction * Tim Harford, bestselling author of How to Make the World Add Up *Kit Yates presents maths as it should be taught to everyone: accessible, fun, stimulating, and deeply relevant to our lives. Spend some time with this book and you're likely to make better judgements and decisions, to see through the charlatans and snake-oil salespeople - and perhaps even to fool yourself a little less. * Philip Ball, author of the award-winning Critical Mass *Fascinating and fun. From the everyday to global challenges, Kit Yates explores how changing your mind - so often thought to be a weakness - is the best life skill we can all acquire. A brilliant book * Professor Alice Roberts *Yates' writing is a beacon of clarity sorely needed in a complicated and confusing world. How do we overcome our biases, understand coincidences or tackle the unreliability of our intuition? With bountiful familiar examples, he effortlessly overturns so many of our deep-rooted wrong-headed notions gently and persuasively. I'll be quoting from this book * Jim Al-Khalili *I'm a Yates fan. His style is all-clarity-no-bullshit * Aperiodical *Seriously good * Caroline Lucas MP *Absolutely fascinating * James O'Brien *An exceptional book - readable, funny and more needed than ever * Dr Chris van Tulleken, bestselling author of Ultra-Processed People *Yates' writing style imbues the subjects covered with an infectious enthusiasm, artfully dispelling the dry, stuffy perceptions many people have of maths * Physics World *HOW TO EXPECT THE UNEXPECTED is fascinating and (very much to the point) delightfully clear and vivid to read. Like many people, I like reading about maths without actually knowing how to do it, and part of the pleasure of reading this came from its many examples from everyday life. A splendid book! * Philip Pullman *

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Book SynopsisFrom the stock market to covid-19, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models-from linear regression to random walks and far beyond-that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. Now culminating in an examination of how to use the multi-model approach to think about pandemics like covid-19, The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

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Book SynopsisThis book promises to be the definitive guide to the field. It provides a highly sophisticated yet exceptionally clear explanation of game theory, with a host of applications to legal issues.Trade ReviewGame Theory and the Law promises to be the definitive guide to the field. It provides a highly sophisticated yet exceptionally clear explanation of game theory, with a host of applications to legal issues. The authors have not only synthesized the existing scholarship, but also created the foundation for the next generation of research in law and economics. -- Daniel A. Farber, University of Minnesota Law SchoolThe most comprehensive and encompassing treatment of this approach… [This] is the first nontechnical, modern introduction to how (noncooperative) game theory can be applied specifically to legal analysis… Game Theory and the Law is a user-friendly analysis of concrete, numerical examples, rather than a theoretical presentation of abstract concepts. The authors introduce and explain, with actual legal cases or hypotheticals, the salient issues of modern game theory. This breadth of coverage is remarkable. This is not just a textbook; it is also something of a research monograph, introducing many new models attributable to the authors alone. -- Peter H. Huang * Jurimetrics Journal *Game Theory and the Law is an important book. It is important in the sense that it will serve as a catalyst for an expanded use of game-theoretic models in the study of law. It will be a book that people will one day recognize as having had a considerable influence on its field. And it will receive the praise that accompanies such influence. Happily, such influence will be beneficial to the field of law and such praise will be richly deserved, because Game Theory and the Law is an extremely intelligent and thoughtful text… One of the features of the book that is most striking (and, for my part, most welcome) is the thoughtful and sensible manner in which they approach the use of game theory. Unlike many proponents of game-theoretic analysis, they do not present it as the only legitimate approach to social-scientific analysis. The authors present game theory as a powerful tool that can be used along with other approaches to enhance our understanding of the role of law in social life… The persuasiveness of their general argument for the utility of game theory derives from a combination of the power of their insights along with the sensibility of their analysis. The book is written in a clear, concise and interesting manner. Its bibliographic references render it a source book for additional research in both game theory and law. This is a book that should be read by scholars of law in particular and scholars of political behavior in particular. -- Jack Knight * Law and Politics Book Review *Table of ContentsPreface Introduction: Understanding Strategic Behavior Bibliographic Notes Simultaneous Decisionmaking and the Normal Form Game The Normal Form Game Using Different Games to Compare Legal Regimes The Nash Equilibrium Civil Liability, Accident Law, and Strategic Behavior Legal Rules and the Idea of Strict Dominance Collective Action Problems and the Two-by-Two Game The Problem of Multiple Nash Equilibria Summary Bibliographic Notes Dynamic Interaction and the Extensive Form Game The Extensive Form Game and Backwards Induction A Dynamic Model of Preemption and Strategic Commitment Subgame Perfection Summary Bibliographic Notes Information Revelation, Disclosure Laws, and Renegotiation Incorporating Beliefs into the Solution Concept The Perfect Bayesian Equilibrium Solution Concept Verifiable Information, Voluntary Disclosure, and the Unraveling Result Disclosure Laws and the Limits of Unraveling Observable Information, Norms, and the Problem of Renegotiation Optimal Incentives and the Need for Renegotiation Limiting the Ability of Parties to Renegotiate Summary Bibliographic Notes Signaling, Screening, and Nonverifiable Information Signaling and Screening Modeling Nonverifiable Information Signals and the Effects of Legal Rules Information Revelation and Contract Default Rules Screening and the Role of Legal Rules Summary Bibliographic Notes Reputation and Repeated Games Backwards Induction and Its Limits Infinitely Repeated Games, Tacit Collusion, and Folk Theorems Reputation, Predation, and Cooperation Summary Bibliographic Notes Collective Action, Embedded Games, and the Limits of Simple Models Collective Action and the Role of Law Embedded Games Understanding the Structure of Large Games Collective Action and Private Information Collective Action Problems in Sequential Decisionmaking Herd Behavior Summary Bibliographic Notes Noncooperative Bargaining Modeling the Division of Gains from Trade Legal Rules as Exit Options Bargaining and Corporate Reorganizations Collective Bargaining and Exit Options Summary Bibliographic Notes Bargaining and Information Basic Models of the Litigation Process Modeling Separate Trials for Liability and Damages Information and Selection Bias Discovery Rules and Verifiable Information Summary Bibliographic Notes Conclusion: Information and the Limits of Law Notes References Glossary Index

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Book SynopsisLearning Data Science is the first book to cover foundational skills in both programming and statistics that encompass the entire data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data.

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Book SynopsisHow do you remember more and forget less?How can you earn more and become more creative just by moving house?And how do you pack a car boot most efficiently?This is your shortcut to the art of the shortcut.Mathematics is full of better ways of thinking, and with over 2,000 years of knowledge to draw on, Oxford mathematician Marcus du Sautoy interrogates his passion for shortcuts in this fresh and fascinating guide. After all, shortcuts have enabled so much of human progress, whether in constructing the first cities around the Euphrates 5,000 years ago, using calculus to determine the scale of the universe or in writing today's algorithms that help us find a new life partner.As well as looking at the most useful shortcuts in history such as measuring the circumference of the earth in 240 BC to diagrams that illustrate how modern GPS works Marcus also looks at how you can use shortcuts in investing or how to learn a musical instrument to memory techniques. He talks to, among many, the Trade Review‘enjoyably clever …with vividly illustrated chapters about the real-world applications of algebra, geometry, probability theory…It’s Du Sautoy, in the end, who provides the wisest commentary’ Steven Poole, Guardian ‘If you thought Maths was all about long stuff, like long division and long multiplication and taking a long, long time to figure things out, Marcus du Sautoy shows that it's just the opposite. Full of humour, stories and the lightest of touches, this is a sight-seeing tour of some of the world's greatest neat dodges, unexpected turns and useful cut-throughs. Prepare to be caught short’ Michael Rosen ‘This book will change the way you look at the world. It's chock full of stories, ideas and clever tricks – I loved it. Marcus is a maestro at making big ideas come alive – he deserves his place alongside Richard Dawkins, E. O. Wilson and Carlo Rovelli in the pantheon of great modern science writers’ Rohan Silva, CEO and founder of Second Home ‘If mathematics has proved anything, it is that shortcuts can change the world. Marcus du Sautoy has come up with a smart, well written and entertaining guide to the connecting tunnels, underpasses and other tricks to traverse the trials of everyday life’ Roger Highfield, author, broadcaster and Science Director at the Science Museum ‘The joy of du Sautoy’s book isn’t really the art of the real-world shortcut at all. It is the romp through mathematical ideas, from place value to non Euclidean geometry to probability theory…There are vivid historical examples of scientists and others using mathematical ideas to solve problems’ Tim Harford, Financial Times

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Book SynopsisThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.Table of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.

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Book SynopsisPraise for the First Edition An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . . -Choice This is an important book, which will appeal to statisticians working on survival analysis problems. -Biometrics A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook. -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, SecoTrade Review“...a welcome addition to the literature on survival analysis...for a unified and thorough reference of classical theory and models, this book is an excellent choice.” (Journal of the American Statistical Association, March 2004) "This book is a role-model for other who are planning to write books…every statistician and applied researcher ought to have this book in their collection." (Journal of Statistical Computation and Simulation, October 2003) "...expanded and updated with recent research...a valuable reference...this book...merits a place on the bookshelf of anyone concerned with the analysis of lifetime data from any field. (Technometrics, Vol. 45, No. 3, August 2003) "...updated version of the popular text...this excellent book will serve as either a reference or a graduate-level textbook." (Short Book Reviews, Vol. 23, No. 2, August 2003) "...excellent...provides a wealth of information for those familiar with the area." (Pharmaceutical Research, Vol. 20, No. 9, September 2003) "...the author's aim is to cover lifetime data analysis without concentrating exclusively on any field of applications...he succeeds quite well..." (Zentralblatt Math, 2003) “...rewritten to reflect new developments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003) "Compared with the large number of other good textbooks in the this field, this is one of the best. I highly recommend that all applied statisticians add this volume to their libraries." (Applied Clinical Trials, May 2003)Table of ContentsBasic Concepts and Models. Observation Schemes, Censoring and Likelihood. Some Nonparametric and Graphical Procedures. Inference Procedures for Parametric Models. Inference procedures for Log-Location-Scale Distributions. Parametric Regression Models. Semiparametric Multiplicative Hazards Regression Models. Rank-Type and Other Semiparametric Procedures for Log-Location-Scale Models. Multiple Modes of Failure. Goodness of Fit Tests. Beyond Univariate Survival Analysis. Appendix A. Glossary of Notation and Abbreviations. Appendix B. Asymptotic Variance Formulas, Gamma Functions and Order Statistics. Appendix C. Large Sample Theory for Likelihood and Estimating Function Methods. Appendix D. Computational Methods and Simulation. Appendix E. Inference in Location-Scale Parameter Models. Appendix F. Martingales and Counting Processes. Appendix G. Data Sets. References.

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Book SynopsisThis classic on games and how to play them intelligently is being re-issued in a new, four volume edition. This book has laid the foundation to a mathematical approach to playing games. The wise authors wield witty words, which wangle wonderfully winning ways. In Volume 1, the authors do the Spade Work, presenting theories and techniques to "dissect" games of varied structures and formats in order to develop winning strategies.Trade Review" ""Winning Ways is an absolute must have for those who are interested in mathematical game theory. It is sure to please any fan of recreational mathematics or simply anyone who is interested in games and how to play them well."" -Jacob McMillen, Math Horizons, November 2005 ""This new edition confirms the status of the book as a standard reference, which it will continue to be for at least another decade."" -Adhemar Bultheel, Bulletin of the Belgian Mathematical Society , December 2005"Table of ContentsPreface to Second Edition, Preface, Spade-Work!, 1. WhoseGame?, 2. Finding the Correct Number is Simplicity Itself, 3. Some Harder Games and How to Make Them Easier, 4. Taking and Breaking, 5. Numbers, Nimbers and Numberless Wonders, 6. The Heat of Battle, 7. Hackenbush, 8. It’s a Small Small Small Small World, Index

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Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThe second edition of this very successful and authoritative set of tables still benefits from clear typesetting, which makes the figures easy to read and use. It has, however, been improved by the addition of new tables that provide Bayesian confidence limits for the binomial and Poisson distributions, and for the square of the multiple correlation coefficient, which have not been previously available. The intervals are the shortest possible, consistent with the requirement on probability. Great care has been taken to ensure that it is clear just what is being tabulated and how the values may be used; the tables are generally capable of easy interpolation. The book contains all the tables likely to be required for elementary statistical methods in the social, business and natural sciences. It will be an essential aid for teachers, researchers and students in those subjects where statistical analysis is not wholly carried out by computers.Trade Review'This is an excellent book offered at an unusually low price of £3.50. Any forensic scientist who analyses data will be well advised to ensure that a copy is always close to hand.' Journal of the Forensic Science Society' … very extensive...clear well explained tables.' P. J. Avery, British Journal of Biomedical Science' … these are among the best available and they are well set out.' P. Sprent, Journal of Applied EcologyTable of Contents1. The binomial distribution function; 2. The Poisson distribution function; 3. Binomial coefficients; 4. The normal distribution function; 5. Percentage points of the normal distribution; 6. Logarithms of factorials; 7. The chi-squared distribution function; 8. Percentage points of the chi-squared distribution; 9. The t-distribution function; 10. Percentage points of the t-distribution; 11. Percentage points of Behrens' distribution; 12. Percentage points of the F-distribution; 13. Percentage points of the correlation coefficient r when rho = 0; 14. Percentage points of Spearman's S; 15. Percentage points of Kendall's K; 16. The z-transformation of the correlation coefficient; 17. The inverse of the z-transformation; 18. Percentage points of the distribution of the number of runs; 19. Upper percentage points of the two-sample Kolmogorov–Smirnov distribution; 20 Percentage points of Wilcoxon's signed-rank distribution; 21. Percentage points of the Mann–Whitney distribution; 22A. Expected values of normal order statistics (normal scores); 22B. Sums of squares of normal scores; 23. Upper percentage points of the one-sample Kolmogorov–Smirnov distribution; 24. Upper percentage points of Friedmann's distribution; 25. Upper percentage points of the Kruskal–Wallis distribution; 26. Hypergeometric probabilities; 27. Random sampling numbers; 28. Random normal deviates; 29. Bayesian confidence limits for a binomial parameter; 30. Bayesian confidence limits for a Poisson mean; 31. Bayesian confidence limits for the square of a multiple correlation coefficient; A note on interpolation; Constants.

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Book SynopsisTrade Review"This textbook contains and describes all the tools one needs to plan and to carry out a simulation study as well as to analyze its results." --J.Wolters, zbMATH Open "It presents the statistics needed to analyze simulated data and to validate the simulation model. In this edition, several new topics are included as well as a number of new exercises." --Vigirdas Mackevicius, zbMATH OpenTable of Contents1. Introduction 2. Elements of Probability 3. Random Numbers 4. Generating Discrete Random Variables 5. Generating Continuous Random Variables 6. The Multivariate Normal Distribution and Copulas 7. The Discrete Event Simulation Approach 8. Statistical Analysis of Simulated Data 9. Variance Reduction Techniques 10. Additional Variance Reduction Techniques 11. Statistical Validation Techniques 12. Markov Chain Monte Carlo Methods

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Book SynopsisRare events such as earthquakes, tsunamis, and floods fortunately do not occur every day, but when they do, their effects are devastating. Such rare events are particularly important in understanding and characterizing global warming and climate changes. In addition to natural catastrophes, rare events such as big financial crashes also play a significant role in the economy. In the absence of predictive models, the best way forward is to analyse the statistics of these extreme events and draw conclusions about the probability of their occurrences.Extreme value statistics (EVS) and the statistics of records in a random sequence are examples of a truly interdisciplinary topic, spanning from statistics and mathematics on one side to physics of disordered systems on the other. They have tremendous importance and practical applications in a wide variety of fields, such as climate science, finance, spin-glasses, and random matrices.Statistics and mathematical literature have explored the su

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Book SynopsisProbability and statistics are subjects fundamental to data analysis, which, in turn, is essential for efficient artificial intelligence.

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Book SynopsisTrade ReviewYes, mediation is an important topic. It has longed been used in the social sciences especially psychology. Of late there has been interest in many different fields including economics, sociology, epidemiology, political science and education, among other fields. Tyler VanderWeele is very qualified to author this book. He has contributed important work to the development of this topic and is a talented and careful researcher. I think there is potential for adoption in graduate courses in the social and biomedical sciences. I also think it could be widely purchased by applied researchers as a reference. I recommend publication. * Luke Keele, Associate Professor, Department of Political Science, Penn State University *Mediation is about understanding pathways between a treatment and an outcome that lead to the outcome, i.e., mechanisms. Mechanisms are a central thing in science and statisticians have been providing new principled methods for studying these topics over especially the last 10 years. Especially in the social and behavioral sciences and in epidemiology there has been great interest in these methods, and the methodology the author wants to write about is the new stuff from the last 10 years. [VanderWeele] is the key player in statistical literature these days. He's a good communicator… Primary market: applied researchers doing mediation in epidemiology, social and behavioral sciences. Secondary market: applied statisticians teaching causal inference and/or working in the area." " * Michael Sobel, Dept Sociology, Columbia *Table of ContentsPART I: MEDIATION ANALYSIS ; Chapter 1. Explanation and Mechanism ; Chapter 2. Mediation: Introduction and Regression-Based Approaches ; Chapter 3. Sensitivity Analysis for Mediation ; Chapter 4. Mediation Analysis with Survival Data ; Chapter 5. Multiple Mediators ; Chapter 6. Mediation Analysis with Time-Varying Exposures and Mediators ; Chapter 7. Selected Topics in Mediation Analysis ; Chapter 8. Other Topics Related to Intermediates ; PART II: INTERACTION ANALYSIS ; Chapter 9. An Introduction to Interaction Analysis ; Chapter 10. Mechanistic Interaction ; Chapter 11. Bias Analysis for Interactions ; Chapter 12. Interaction in Genetics: Independence and Boosting Power ; Chapter 13. Power and Sample-Size Calculations for Interaction Analysis ; PART III: SYNTHESIS AND SPILLOVER EFFECTS ; Chapter 14. A Unification of Mediation and Interaction ; Chapter 15. Social Interactions and Spillover Effects ; Chapter 16. Mediation and Interaction: Future and Context ; Appendix. Technical Details and Proofs ; References

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Book SynopsisThe quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.Trade ReviewShort and to the point, uncluttered, unfancy, free of the faux rigor of most modern finance textbooks, written by a practitioner, that hits most of the essential principles of quantitative finance. * Emanuel Derman, author of My Life as a Quant *The author writes elegantly, and combines precision of expression with topical real-world examples in a way that makes this an exceptional work. * Frank Kelly, University of Cambridge *It is all too rare to find clear thinking, based on first principles, combined with practical understanding of financial markets. This is precisely what Stephen Blyth offers, drawing equally on his mathematical and statistical training and his career in quantitative finance. This book beautifully explains both the profound implications of no-arbitrage theory for the prices of fixed-income derivative securities, and also the pitfalls in practical applications. * John Y Campbell, Harvard University *Table of ContentsI INTRODUCTION AND PRELIMINARIES; II FORWARDS, SWAPS AND OPTIONS; III REPLICATION, RISK-NEUTRALITY AND THE FUNDAMENTAL THEOREM; IV INTEREST RATE OPTIONS; V THROUGH CONTINUOUS TIME

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Book SynopsisOffering accessible and nuanced coverage, Richard W. Hamming discusses theories of probability with unique clarity and depth. Topics covered include the basic philosophical assumptions, the nature of stochastic methods, and Shannon entropy. One of the best introductions to the topic, The Art of Probability is filled with unique insights and tricks worth knowing.Table of ContentsProbability * Introduction * Models in General * The Frequency Approach Rejected * The Single Event Model * Symmetry as the Measure of Probability * Independence * Subsets of a Sample Space * Conditional Probability * Randomness * Critique of the Model Some Mathematical Tools * Permutations * Combinations * The Binomial DistributionBernoulli Trials * Random Variables, Mean and the Expected Value * The Variance * The Generating Function * The Weak Law of Large Numbers * The Statistical Assignment of Probability * The Representation of Information Methods for Solving Problems * The Five Methods * The Total Sample Space and Fair Games * Enumeration * Historical Approach * Recursive Approach * Recursive Approach * The Method of Random Variables * Critique of the Notion of a Fair Game * Bernoulli Evaluation * Robustness * InclusionExclusion Principle Countably Infinite Sample Spaces * Introduction * Bernoulli Trials * On the Strategy to be Adopted * State Diagrams * Generating Functions of State Diagrams * Expanding a Rational Generating Function * Checking the Solution * Paradoxes Continuous Sample Spaces * A Philosophy of the Real Number System * Some First Examples * Some Paradoxes * The Normal Distribution * The Distribution of Numbers * Convergence to the Reciprocal Distribution * Random Times * Dead Times * Poisson Distribution in Time * Queing Theorem * Birth and Death Systems * Summary Uniform Probability Assignments Maximum Entropy * What is Entropy? * Shannons Entropy * Some Mathematical Properties of the Entropy Function * Some Simple Applications * The Maximum Entropy Principle Models of Probability * General Remarks * Maximum Likelihood in a Binary Choice * Von Mises Probability * The Mathematical Approach * The Statistical Approach * When The Mean Does Not Exist * Probability as an Extension of Logic * Di Finetti * Subjective Probability * Fuzzy Probability * Probability in Science * Complex Probability Some Limit Theorems * The Biomial Approximation for the case p=1/2 * Approximation by the Normal Distribution * Another Derivation of the Normal Distribution * Random Times * The Zipf Distribution * Summary An Essay on Simulation

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Book SynopsisExplains how life insurance corporations shaped how we understand American life spans and Americans as risks

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Book Synopsis

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Book SynopsisThis book presents a concise introduction to the theory underlying the analysis of the main types of spatial data. It includes examples to illustrate the topics, including R code for their implementation, as well as exercises to support course teaching and self-study. Trade Review"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los Angeles"This book provides a concise and readable introduction to the three main areas of spatial statistics: random fields, areal data and spatial point processes. Although the focus is on the basic underlying theory, extensive analyses of real data are provided including R code. Suitable as a text or for self-study, each major chapter includes exercises and solutions. A valuable resource for students and researchers in statistics and related fields looking to learn some of the basic theory underlying spatial statistics."- Michael Stein, University of Chicago"The book is a concise introduction to spatial statistics mostly from a mathematical point of view. It devotes a chapter to each of the main classes of spatial statistics settings, point referenced data and interpolation, areal data and spatial point processes. Each chapter contain all the main definitions and theorem (with proofs) for relevant models, and some of the inference methods for each of these spatial statistics settings. The chapters end with worked through R-examples and nice and useful pointers to the literature. The book expect the reader to be both mathematical and statistical mature, and most examples are mathematical. I think this can be a nice introduction and reference book for PhD students specializing in spatial statics, and it can also work as a supporting textbook for a mathematically orientated master level course in spatial statistics."- Ingelin Steinsland, Norwegian University of Science and Technology"Theory of Spatial Statistics: A Concise Introduction is an excellent introductory resource to all three subfields in spatial statistics: geostatistics, areal data, and point processes. The book is well-organized and self-contained, covering the key knowledge of spatial statistics in a unified manner. It describes the mathematical foundations of the related statistical theory with rigorous proofs. Each chapter contains detailed illustrative examples using R packages to exemplify the methodologies applied to some well-known data sets. The book is suitable as a textbook for both graduate and advanced undergraduate students who want to learn the basis of the fast-growing areas of spatial statistics. I like the idea of providing exercises and detailed solutions so that readers can assess their learning outcomes. This book will also be of interest to practitioners of applied statistics from various disciplines as a reference book."- Yang Li, University of Minnesota Duluth"This text provides an excellent introduction to spatial statistics, including some important theoretical results, as well as practical implementation of the methodologies discussed. The modeling approaches are naturally separated into three groups depending on the type of data at hand, i.e., gridded, area unit and mapped point pattern data. The author has managed to incorporate in the text the most commonly used approaches in the literature, along with their corresponding applications. One particularly useful feature is the illustration of R packages to fit these models. Moreover, the inclusion of solutions to theoretical problems offers a nice resource to refer to and utilize in teaching graduate courses on spatial statistics and point processes. In addition, the theoretical results presented make for a nice blend between theory and application. Overall, the book is well written and will be a welcomed addition to the library of any researcher in spatial statistics."- Athanasios (Sakis) Christou Micheas, University of Missouri-Columbia"This book surveys the main topics in spatial statistics, including modeling random fields, variogram estimation, hierarchical models, and spatial point processes...It is amazing how much information van Lieshout is able to convey so concisely and compactly. She is simply masterful at explaining very difficult, intricate, and important concepts, models and statistical methods in an eloquent way...The chapters are wonderfully well organized and cover an ideal list of core topics in the statistical analysis of the most common and important forms of spatial data. Perhaps the best part of the book are the worked examples, which aid the reader new to this material and help crystalize what these statistical models and methods are prescribing...It is clear that an enormous amount of effort went into these worked examples, though as with the theoretical topics, van Lieshout explains everything so clearly and concisely that she makes the applications and R coding look easy, and in some cases almost trivial...(The book) is a remarkable fusion of the most important topics in the field, both theoretical and applied, presented beautifully and eloquently with the utmost care and precision, and so concisely that it all fits into a small handbook. I would strongly recommend this book for anyone teaching a one-semester graduate level course in spatial statistics."- Frederic P. Schoenberg, University of California at Los AngelesTable of Contents1. Introduction. 2. Random field modelling and interpolation. 3. Models and inference for areal unit data. 4. Spatial point processes. Appendix: Solutions to theoretical exercises

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Book SynopsisMultilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; <Trade Review"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. Heck and Thomas have once again presented a must-have reference book to get the job done. This edition’s use of four different software packages and additional easy-to-follow illustrative examples enhance what was already a superb resource for both students and researchers." – George A. Marcoulides, University of California, Santa Barbara, USA Table of ContentsPreface 1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Methods for Examining Individual and Organizational Change 6. Multilevel Models with Categorical Variables 7. Multilevel Structural Equation Variables 8. Multilevel Latent Growth and Mixture Models 9. Data Consideration in Examining Multilevel Models

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Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend

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Book SynopsisWithout question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of studentsâ anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS . Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses.Key features of the text:â Step-by-step instruction and screenshotsâ Designed to be hands-on with the user performing the analyses alongside the text on their computer as they read through each chapterâ Call-out boxes provided, highlighting important information as appropriateâ SPSS output explained, with written results provided using the popular, widely recognized APA formatâ End-of-chapter exercises included, allowing for additional practiceâ SPSS data sets available on the publisherâs websiteNew to the Fourth Edition:â Fully updated to SPSS 28â Updated screenshots in full color to reflect changes in the SPSS software system (version 28)â Exercises updated with up-to-date examplesâ Exact p-values provided (consistent with APA recommendations)Table of ContentsPart I: Introduction to SPSS, Descriptive Statistics, Graphical Procedures of Data, and Reliability Using Coefficient Alpha 1. Introduction to SPSS, 2. Descriptive Statistics: Frequencies, Measures of Central Tendency, and Measures of Variability, 3. Graphical Procedures, 4. Reliability (As Measured by Coefficient Alpha); Part II: Inferential Statistics 5. The One-Sample t Test, 6. The Independent-Samples t Test, 7. The Dependent-Samples t Test, 8. The One-Way Between Subjects Analysis of Variance (ANOVA), 9. The Two-Way Between Subjects Analysis of Variance (ANOVA), 10. The One-Way Within Subjects Analysis of Variance (ANOVA), 11. The One-Between–One-Within Subjects Analysis of Variance (ANOVA), 12. The Pearson r Correlation Coefficient, 13. Simple Linear Regression, 14. Multiple Linear Regression, 15. The Chi-Square Goodness of Fit Test, 16. The Chi-Square Test of Independence; Appendix A. Data Transformations and Other Procedures Appendix B. Solutions to Chapter Exercises

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Book SynopsisInterpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. Features of the ninth edition: Covers a broad array of basic statistical concepts, including topics drawn from the New Statistics Up-to-date journal excerpts reflecting contemporary styles in statistical reporting Strong emphasis on data visualization Ancillary materials include data sets with almost two hours of accompanying tutorial videos, which will help students and instructors apply lessons from the book to real-life scenarios About this book Each of the 63 exercises in the book contain three central components: 1) an introduction to a statistical concept, 2) a brief excerpt from a published research article that uses the statistical concept, and 3) a set of questions (with answers) that guides students into deeper learning about the concept. The questioTrade ReviewThe 9th edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USAThe ninth edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USATable of Contents1. Basic Descriptions of the Data: Measurement and Frequency 2. Describing the Data 3. Displaying Data: Visualizing What is There 4. Finding Relationships: Association and Prediction 5. Group Differences with Normal Distributions 6. Nonparametric Tests for Group Differences 7. Test Construction

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