On 20 October 2015, the global mathematical community will celebrate World Statistics Day. Supported and promoted by the United Nations, this day marks the achievements and ongoing work of statisticians whose data influences decision-makers and policies that affect millions of people. In honour of this, we present a reading list of OUP books and journal articles that have helped to advance the understanding of these mathematical concepts.
Analyzing Wimbledon, by Franc Klaassen and Jan R. Magnus
The world’s most famous tennis tournament offers statisticians insight into examining probabilities. This study attempts to answer many questions, including whether an advantage is given to the person who serves first, whether new balls influence gameplay, or whether previous champions win crucial points. Looking at a unique data set of 100,000 points played at Wimbledon, Klaassen and Magnus illustrate the amazing power of statistical reasoning.
‘Asking About Numbers: Why and How,’ by Stephen Ansolabehere, Marc Meredith, and Erik Snowberg, published in Political Analysis
How can designing quantitative standardized questions for surveys yields findings that can later be linked to statistical models? The authors offer a full analysis about why quantitative questions are feasible and useful, particularly for the study of economic voting.
This textbook demonstrates how statistical models are used to evaluate retail credit risk and to generate automated decisions. Aimed at graduate students in business, statistics, economics, and finance, the book introduces likely situations where credit scoring might be applicable, before presenting a practical guide and real-life examples on how credit scoring can be learned to implement on the job. Little prior knowledge is assumed, making this textbook the first stop for anyone learning the intricacies of credit scoring.
‘Big data and precision’ by D. R. Cox, published in Biometrika
Professor D.R. Cox of Nuffield College, Oxford, explores issues around big data, statistical procedure, and precision, in addition to oulining a fairly general representation of the accretion of error in large systems.
The New Statistics with R: An Introduction for Biologists, by Andy Hector
This introductory text to statistical reasoning helps biologists learn how to manipulate their data sets in R. The text begins by explaining the classical techniques of linear model analysis and consequently provides real-world examples of its application. With all the analyses worked in R, the open source programming language for statistics and graphics, and the R scripts included as support material, Hector presents an easy-to-use textbook for students and professionals with all levels of understanding of statistics.
‘Housing Wealth and Retirement Timing’ by Martin Farnham and Purvi Sevak, published in CESifo Economic Studies
Having found that rising house prices cause people to revise their planned retirement age, Farnham and Sevak explore movements in the housing market and the implications for labour-supply.
An Introduction to Medical Statistics, by Martin Bland
Every medical student needs to have a firm understanding of medical statistics and its uses throughout training to become doctor. The fourth edition of An Introduction to Medical Statistics aims to do just that, summarising the key statistical methods by drawing on real-life examples and studies carried out in clinical practice. The textbook also includes exercises to aid learning, and illustrates how correctly employed medical data can improve the quality of research published today.
‘Getting policy-makers to listen to field experiments’ by Paul Dolan and Matteo M. Galizzi, published in Oxford Review of Economic Policy
On the premise that the greater use of field experiment findings would lead to more efficient use of scarce resources, this paper from Dolan and Galizzi considers what could be done to address this issue, including a consideration of current obstacles and misconceptions.
Stochastic Analysis and Diffusion Processes, by Gopinath Kallianpur and P. Sundar
Building the basic theory and offering examples of important research directions in stochastic analysis, this graduate textbook provides a mathematical introduction to stochastic calculus and its applications. Written as a guide to important topics in the field and including full proofs of all results, the book aims to render a complete understanding of the subject for the reader in preparation for research work.
‘Statistical measures for evaluating protected group under-representation’ by Joseph L. Gastwirth, Wenjing Xu, and Qing Pan, published in Law, Probability & Risk
The authors explore the conflicting inferences drawn from the same data in the cases of People v. Bryant and Ambrose v. Booker. Based on their full analysis, they argue that when assessing statistics on the demographic mix of jury pools for legal significance, courts should consider the possible reduction in minority representation that can occur in the peremptory challenge proceedings.
Bayesian Theory and Applications, edited by Paul Damien, Petros Dellaportas,
Nicholas G. Polson, and David A. Stephens
Beginning by introducing the foundations of Bayesian theory, this volume proceeds to detail developments in the field since the 1970s. It includes an explanatory chapter for each conceptual advance followed by journal-style chapters presenting applications, targeting those studying statistics at every level.
‘Representative Surveys in Insecure Environments: A Case Study of Mogadishu, Somalia,’ by Jesse Driscoll and Nicholai Lidow, published in Journal of Survey Statistics and Methodology
How do we get accurate statistics from politically unstable areas? This paper discusses the challenges of conducting a representative survey in Somalia and the opportunities for improving future data collection efforts in these insecure environments.
Talking about random processes in real-life is tricky, as the world has no memories of those processes which depend only on the current state of the system and not on its previous history. This book is driven by the underlying Kolmogorov probability equations for population size. It’s the first title on stochastic population processes that focuses on practical application. It is not intended as a text for pure-minded mathematicians who require deep theoretical understanding, but for researchers who want to answer real questions.
Image Credit: Statistics by Simon Cunningham. CC BY 2.0 via Flickr.