Making good decisions under conditions of uncertainty - which is the norm - requires a sound appreciation of the way random chance works. As analysis and modelling of most aspects of the world, and all measurement, are necessarily imprecise and involve uncertainties of varying degrees, the understanding and management of probabilities is central to much work in the sciences and economics. In this Very Short Introduction, John Haigh introduces the ideas of probability and different philosophical approaches to probability, and gives a brief account of the history of development of probability theory, from Galileo and Pascal to Bayes, Laplace, Poisson, and Markov. He describes the basic probability distributions, and goes on to discuss a wide range of applications in science, economics, and a variety of other contexts such as games and betting. He concludes with an intriguing discussion of coincidences and some curious paradoxes. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
The Hidden Mathematics of Sport takes a novel and intriguing look at sport, by exploring the mathematics behind the action. Discover the best tactics for taking a penalty, the pros and cons of being a consistent golfer, the surprising link between boxing and figure skating, the unusual location of England's earliest 'football' game (in a parish church), and the formula for always winning a game of tennis. Whatever your sporting interests, you will find plenty to absorb and amuse you in this entertaining and unique book – and maybe you will even find some new strategies for beating the odds.
This fascinating book explores the mathematics involved in all your favourite sports. The Hidden Mathematics of Sport takes a unique and fascinating look at sport by exploring the mathematics behind the action. You'll discover the best tactics for taking a penalty, the pros and cons of being a consistent golfer, the surprising connection between American football and cricket, the quirky history of league tables, the unusual location of England's earliest 'football' matches and how to avoid marathon tennis matches. Whatever your sporting interests, from boxing to figure skating, from rugby to horse racing, you will find plenty to absorb and amuse you in this insightful book. Word count: 35,000 words
What are the odds against winning the Lotto, The Weakest Link, or Who Wants to be a Millionaire? The answer lies in the science of probability, yet many of us are unaware of how this science works. Every day, people make judgements on a wide variety of situations where chance plays a role, including buying insurance, betting on horse-racing, following medical advice - even carrying an umbrella. In Taking Chances, John Haigh guides the reader round common pitfalls, demonstrates how to make better-informed decisions, and shows where the odds can be unexpectedly in your favour. This new edition has been fully updated, and includes information on top television shows, plus a new chapter on Probability for Lawyers."--BOOK JACKET.
How does mathematics impact everyday events? Through concrete examples from business, sport, games, computing, and society, this book explores the mathematics underpinning our everyday lives. The examples covered in the book include game shows, internet search engines, mortgage payments, drug testing, soccer tournaments, social inequality, voting, and much more. Throughout, the reader's mathematical knowledge is broadened with new topics such as differential equations, eigenvalues of matrices, linear programming, and modular arithmetic. Fully worked examples illustrate the ideas discussed and each chapter includes exercises to develop the reader's understanding. This new edition has been thoroughly updated, and includes a completely new chapter on applications of mathematics to computing. Mathematics in Everyday Life supports beginning university students in science and engineering by offering extra practice in calculus, linear algebra, geometry, trigonometry, elementary number theory, and probability. Students whose degree course includes writing an extended mathematical essay will find many suitable topics here, with pointers to extend and develop the material.
Probability Models is designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics. It describes how to set up and analyse models of real-life phenomena that involve elements of chance. Motivation comes from everyday experiences of probability via dice and cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise. Applications include branching processes, random walks, Markov chains, queues, renewal theory, and Brownian motion. No specific knowledge of the subject is assumed, only a familiarity with the notions of calculus, and the summation of series. Where the full story would call for a deeper mathematical background, the difficulties are noted and appropriate references given. The main topics arise naturally, with definitions and theorems supported by fully worked examples and some 200 set exercises, all with solutions.
Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.
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