With its application-oriented approach, the fifth EMEA edition of Statistics for Business and Economics teaches students the core concepts of statistics in the fields of business, management, and economics, with the needs of the non-mathematician in mind.

The authors interweave statistical methodology with applications of data analysis to enrich students’ understanding of how statistics underpin problem-solving and decision-making. Students develop a computational foundation and learn to use various techniques before moving on to statistical application and interpretation.

At the end of each section, exercises focus on computation and the use of formulas, while application exercises require students to apply what they have learnt to real-world problems.

The text emphasises four themes to support evidence-based management decision making: 1. Setting the statistical landscape in a management context 2. Interpretative decision making based on patterns revealed by exploratory data analyses 3. Statistical decision making guided by the test-based findings of inferential analyses 4. Predictive decision making using statistical modelling evidence. The thread that links them is the role of data analytics as a management decision-support tool. This fifth edition builds on the strengths of the fourth edition by: - Highlighting newer trends in statistical applications in management practice - Strengthening the Excel-based generation of statistical evidence using a custom-built software product, called X-Static - Enhancing the graphic visualisation of statistical evidence. Target market: - Undergraduate students of Management at universities (BCom, BAcc, etc) - Management diploma students working towards professional qualifications at institutions such as the IMM, CSSA, CIMA, IAC, etc. - Postgraduate students (MBA and PGDip) of Management at business schools.

Economic theories can be expressed in words, numbers, graphs and symbols. The existing traditional economics textbooks cover all four methods, but the general focus is often more on writing about the theory and methods, with few practical examples. With an increasing number of universities having introduced mathematical economics at undergraduate level, Basic mathematics for economics students aims to fill this gap in the field. Basic mathematics for economics students begins with a comprehensive chapter on basic mathematical concepts and methods (suitable for self-study, revision or tutorial purposes) to ensure that students have the necessary foundation. The book is written in an accessible style and is extremely practical. Numerous mathematical economics examples and exercises are provided as well as fully worked solutions using numbers, graphs and symbols. Basic mathematics for economics students is aimed at all economics students. It focuses on quantitative aspects and especially complements the three highly popular theoretical economics textbooks, Understanding microeconomics, Understanding macroeconomics and Economics for South African students, all written by Philip Mohr and published by Van Schaik Publishers.

Statistics and quantitative methods are brought to life for social science students in this tutorial course.

,P> This revised edition provides an overview of entry- and intermediate-level statistics, and the material on the accompanying website provides extensive practice. Both the text and the website are structured to make learning self-directed, thus numerous worked examples, exercises, activities and tests are included. The emphasis, throughout, is on practice.

Students are expected to engage with the material and experience multiple aspects of data and statistical analysis. Most of the tutorials include detailed examples of how to conduct analyses in Microsoft Excel, SPSS, or R.

Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.

Statistics and quantitative methods are brought to life for social science students in this tutorial course. This revised edition provides an overview of entry- and intermediate-level statistics, and the accompanying material on the CD provides extensive practice. Both the text and the CD are structured to make learning self-directed, thus numerous worked examples, exercises, activities and tests are included. The emphasis, throughout, is on practice. Students are expected to engage with the material and experience first-hand the multiple aspects of data and statistical analysis.

In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps you discover the nature of statistics and understand its essential role in scientific research.

Moritz Schulz explores counterfactual thought and language: what would have happened if things had gone a different way. Counterfactual questions may concern large scale derivations (what would have happened if Nixon had launched a nuclear attack) or small scale evaluations of minor derivations (what would have happened if I had decided to join a different profession). A common impression, which receives a thorough defence in the book, is that oftentimes we find it impossible to know what would have happened. However, this does not mean that we are completely at a loss: we are typically capable of evaluating counterfactual questions probabilistically: we can say what would have been likely or unlikely to happen. Schulz describes these probabilistic ways of evaluating counterfactual questions and turns the data into a novel account of the workings of counterfactual thought.

Probability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains. A special feature is the authors' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers and the central limit theorem. There is an account of moment generating functions and their applications. The following three chapters are about branching processes, random walks, and continuous-time random processes such as the Poisson process. The final chapter is a fairly extensive account of Markov chains in discrete time. This second edition develops the success of the first edition through an updated presentation, the extensive new chapter on Markov chains, and a number of new sections to ensure comprehensive coverage of the syllabi at major universities.

The chapters in this monograph are contributions from the Advances in Quantum Monte Carlo symposium held at Pacifichem 2010, International Chemical Congress of Pacific Basin Societies. The symposium was dedicated to celebrate the career of James B. Anderson, a notable researcher in the field. Quantum Monte Carlo provides an ab initio solution to the Schroedinger equation by performing a random walk through configuration space in imaginary time. Benchmark calculations suggest that its most commonly-used variant, "fixed-node" diffusion Monte Carlo, estimates energies with an accuracy comparable to that of high-level coupled-cluster calculations. These two methods, each having advantages and disadvantages, are complementary "gold-standards" of quantum chemistry. There are challenges facing researchers in the field, several of which are addressed in the chapters in this monograph. These include improving the accuracy and precision of quantum Monte Carlo calculations; understanding the exchange nodes and utilizing the simulated electron distribution; extending the method to large and/or experimentally-challenging systems; and developing hybrid molecular mechanics/dynamics and Monte Carlo algorithms.

As technology progresses, we are able to handle larger and larger datasets. At the same time, monitoring devices such as electronic equipment and sensors (for registering images, temperature, etc.) have become more and more sophisticated. This high-tech revolution offers the opportunity to observe phenomena in an increasingly accurate way by producing statistical units sampled over a finer and finer grid, with the measurement points so close that the data can be considered as observations varying over a continuum. Such continuous (or functional) data may occur in biomechanics (e.g. human movements), chemometrics (e.g. spectrometric curves), econometrics (e.g. the stock market index), geophysics (e.g. spatio-temporal events such as El Nino or time series of satellite images), or medicine (electro-cardiograms/electro-encephalograms). It is well known that standard multivariate statistical analyses fail with functional data. However, the great potential for applications has encouraged new methodologies able to extract relevant information from functional datasets. This Handbook aims to present a state of the art exploration of this high-tech field, by gathering together most of major advances in this area. Leading international experts have contributed to this volume with each chapter giving the key original ideas and comprehensive bibliographical information. The main statistical topics (classification, inference, factor-based analysis, regression modelling, resampling methods, time series, random processes) are covered in the setting of functional data. The twin challenges of the subject are the practical issues of implementing new methodologies and the theoretical techniques needed to expand the mathematical foundations and toolbox. The volume therefore mixes practical, methodological and theoretical aspects of the subject, sometimes within the same chapter. As a consequence, this book should appeal to a wide audience of engineers, practitioners and graduate students, as well as academic researchers, not only in statistics and probability but also in the numerous related application areas.

The UK's most trusted A level Mathematics resources With over 900,000 copies sold (plus 1.3 million copies sold of the previous edition), Pearson's own resources for Pearson Edexcel are the market-leading and most trusted for AS and A level Mathematics. Our A level Mathematics Statistics and Mechanics Year 1 Practice Book helps you get exam-ready with confidence and practice at the right pace. Coverage: the practice workbooks cover all Pure, Statistics and Mechanics topics Quantity: the most A level question practice available, with over 2,000 extra questions per book Practice at the right pace: start with the essentials, build your skills with various practice questions to make connections between topics, then apply this to exam-style questions at the end of each chapter Get exam-ready with confidence: differentiated questions including 'Bronze, Silver, Gold' in each chapter, and a mixed problem-solving section for each book, will guide and help you to develop the skills you need for your exams Designed to be used flexibly, the practice books are fully mapped to the scheme of work and textbooks so you can use them seamlessly in and out of the classroom and all year round. Use them lesson by lesson, topic by topic, for homework, revision and more - the choice is yours Great value practice materials that are cheaper than photocopying, saves more time than independently sourcing questions and answers, and are all in one place Pearson Edexcel AS and A level Mathematics Statistics and Mechanics Year 1/AS Practice Book matches the Pearson Edexcel exam structure and is fully integrated with Pearson Edexcel's interactive scheme of work. Practice books are also available offering the most comprehensive and flexible AS/A level Maths practice with over 2000 extra questions. Pearson's revision resources are the smart choice for those revising for Pearson Edexcel AS and A level Mathematics - there is a Revision Workbook for exam practice and a Revision Guide for classroom and independent study. Practice Papers Plus+ books contain additional full length practice papers, so you can practice answering questions by writing straight into the book and perfect your responses with targeted hints, guidance and support for every question, including fully worked solutions.

Concentration inequalities for functions of independent random variables is an area of probability theory that has witnessed a great revolution in the last few decades, and has applications in a wide variety of areas such as machine learning, statistics, discrete mathematics, and high-dimensional geometry. Roughly speaking, if a function of many independent random variables does not depend too much on any of the variables then it is concentrated in the sense that with high probability, it is close to its expected value. This book offers a host of inequalities to illustrate this rich theory in an accessible way by covering the key developments and applications in the field.
The authors describe the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented.
A self-contained introduction to concentration inequalities, it includes a survey of concentration of sums of independent random variables, variance bounds, the entropy method, and the transportation method. Deep connections with isoperimetric problems are revealed whilst special attention is paid to applications to the supremum of empirical processes.
Written by leading experts in the field and containing extensive exercise sections this book will be an invaluable resource for researchers and graduate students in mathematics, theoretical computer science, and engineering.

Developed for the new International A Level specification, these new resources are specifically designed for international students, with a strong focus on progression, recognition and transferable skills, allowing learning in a local context to a global standard. Recognised by universities worldwide and fully comparable to UK reformed GCE A levels. Supports a modular approach, in line with the specification. Appropriate international content puts learning in a real-world context, to a global standard, making it engaging and relevant for all learners. Reviewed by a language specialist to ensure materials are written in a clear and accessible style. The embedded transferable skills, needed for progression to higher education and employment, are signposted so students understand what skills they are developing and therefore go on to use these skills more effectively in the future. Exam practice provides opportunities to assess understanding and progress, so students can make the best progress they can.

This is an open access title available under the terms of a CC BY-NC-ND 3.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Explaining Criminal Careers presents a simple but influential theory of crime, conviction and reconviction. The assumptions of the theory are derived directly from a detailed analysis of cohort samples extracted from the Home Office Offenders Index - a unique database which contains records of all criminal (standard list) convictions in England and Wales since 1963. In particular, the theory explains the well-known Age/Crime curve. Based on the idea that there are only three types of offenders, who commit crimes at either high or low (constant) rates and have either a high or low (constant) risk of reoffending, this simple theory makes exact quantitative predictions about criminal careers and age-crime curves. Purely from the birth-rate over the second part of the 20th century, the theory accurately predicts (to within 2%) the prison population contingent on a given sentencing policy. The theory also suggests that increasing the probability of conviction after each offence is the most effective way of reducing crime, although there is a role for treatment programmes for some offenders. The authors indicate that crime is influenced by the operation of the Criminal Justice System and that offenders do not 'grow out' of crime as commonly supposed; they are persuaded to stop or decide to stop after (repeated) convictions, with a certain fraction of offenders desisting after each conviction. Simply imprisoning offenders will not reduce crime either by individual deterrence or by incapacitation. With comprehensive explanations of the formulae used and complete mathematical appendices allowing for individual interpretations and further development of the theory, Explaining Criminal Careers represents an innovative and meticulous investigation into criminal activity and the influences behind it. With clear policy implications and a wealth of original and significant discussions, this book marks a ground-breaking chapter in the criminological debate surrounding criminal careers.

Professor Dominic Welsh has made significant contributions to the fields of combinatorics and discrete probability, including matroids, complexity, and percolation, and has taught, influenced and inspired generations of students and researchers in mathematics. This volume summarizes and reviews the consistent themes from his work through a series of articles written by renowned experts. These articles contain original research work, set in a broader context by the inclusion of review material. As a reference text in its own right, this book will be valuable to academic researchers, research students, and others seeking an introduction to the relevant contemporary aspects of these fields.

Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empirical examples in macroeconomics and finance. Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test and tests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrument asymptotics.

Look at your data Now available with Macmillan's online learning platform Achieve, The Practice of Statistics for Business and Economics (PSBE) helps students develop a working knowledge of data production and interpretation in a business and economics context, giving them the practical tools they need to make data-informed, real-world business decisions from the first day of class. Achieve for The Practice of Statistics for Business and Economics connects the problem-solving approach and real-world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.

An introduction to statistical data mining, Data Analysis and Data Mining is both textbook and professional resource. Assuming only a basic knowledge of statistical reasoning, it presents core concepts in data mining and exploratory statistical models to students and professional statisticians-both those working in communications and those working in a technological or scientific capacity-who have a limited knowledge of data mining. This book presents key statistical concepts by way of case studies, giving readers the benefit of learning from real problems and real data. Aided by a diverse range of statistical methods and techniques, readers will move from simple problems to complex problems. Through these case studies, authors Adelchi Azzalini and Bruno Scarpa explain exactly how statistical methods work; rather than relying on the "push the button" philosophy, they demonstrate how to use statistical tools to find the best solution to any given problem. Case studies feature current topics highly relevant to data mining, such web page traffic; the segmentation of customers; selection of customers for direct mail commercial campaigns; fraud detection; and measurements of customer satisfaction. Appropriate for both advanced undergraduate and graduate students, this much-needed book will fill a gap between higher level books, which emphasize technical explanations, and lower level books, which assume no prior knowledge and do not explain the methodology behind the statistical operations.

Now available with Macmillan's new online learning tool Achieve, Introduction to the Practice of Statistics, 10th edition, prepares students for the application of statistics in the real world by using current examples and encouraging exploration into data analysis and interpretation. The text enforces statistical thinking by providing learning objectives and linked exercises to help students master core statistics concepts and think beyond the calculations.Achieve for Introduction to the Practice of Statistics integrates outcome-based learning objectives and a wealth of examples with assessment in an easy-to-use interface. Students are provided with rich digital resources that solidify conceptual understanding, as well as homework problems with hints, answer-specific feedback, and a fully worked solution.

The UK's most trusted A level Mathematics resources With over 900,000 copies sold (plus 1.3 million copies sold of the previous edition), Pearson's own resources for Pearson Edexcel are the market-leading and most trusted for AS and A level Mathematics. This book covers all the content needed for the optional Edexcel AS and A level Further Statistics 1 exams Enhanced focus on problem-solving and modelling, as well as supporting the large data set and calculators Packed with worked examples with guidance, lots of exam-style questions, practice papers, and plenty of mixed and review exercises Full worked solutions to every question available free and online for quick and easy access. Plus free additional online content with GeoGebra interactives and Casio calculator tutorials Practice books also available offering the most comprehensive and flexible AS/A level Maths practice with over 2000 extra questions Includes access to an online digital edition (valid for 3 years once activated) Pearson Edexcel AS and A level Further Mathematics Further Statistics 1 Textbook + e-book matches the Pearson Edexcel exam structure and is fully integrated with Pearson Edexcel's interactive scheme of work. All of the books in this series focus on problem-solving and modelling, as well as supporting the large data set and calculators. They are packed with worked examples with guidance, lots of exam-style questions, practice papers, and plenty of mixed and review exercises. There are full worked solutions to every question available free and online for quick and easy access. You will also have access to lots of free additional online content with GeoGebra interactives and Casio calculator tutorials. There are separate Pure and Applied textbooks for AS and A level Maths, and a textbook per option for AS and A level Further Maths. Practice books are also available offering the most comprehensive and flexible AS/A level Maths practice with over 2000 extra questions. Pearson's revision resources are the smart choice for those revising for Pearson Edexcel AS and A level Mathematics - there is a Revision Workbook for exam practice and a Revision Guide for classroom and independent study. Practice Papers Plus+ books contain additional full length practice papers, so you can practice answering questions by writing straight into the book and perfect your responses with targeted hints, guidance and support for every question, including fully worked solutions.

Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis. These techniques are leading to biophysically more realistic models. It has also become clear that both neuroscientists and mathematicians profit from collaborations in this exciting research area.
Graduates and researchers in computational neuroscience and stochastic systems, and neuroscientists seeking to learn more about recent advances in the modelling and analysis of noisy neural systems, will benefit from this comprehensive overview. The series of self-contained chapters, each written by experts in their field, covers key topics such as: Markov chain models for ion channel release; stochastically forced single neurons and populations of neurons; statistical methods for parameter estimation; and the numerical approximation of these stochastic models.
Each chapter gives an overview of a particular topic, including its history, important results in the area, and future challenges, and the text comes complete with a jargon-busting index of acronyms to allow readers to familiarize themselves with the language used.

Economic theories can be expressed in words, numbers, graphs and symbols. The existing traditional economics textbooks cover all four methods, but the general focus is often more on writing about the theory and methods, with few practical examples. With an increasing number of universities having introduced mathematical economics at undergraduate level, Basic mathematics for economic students aims to fill this gap in the field. Basic mathematics for economic students begins with a comprehensive chapter on basic mathematical concepts and methods (suitable for self-study, revision or tutorial purposes) to ensure that students have the necessary foundation. The book is written in an accessible style and is extremely practical. Numerous mathematical economics examples and exercises are provided as well as fully worked solutions using numbers, graphs and symbols. Basic mathematics for economic students is aimed at all economics students. It focuses on quantitative aspects and especially complements the two highly popular theoretical economics textbooks Understanding microeconomics and Understanding macroeconomics, both written by Philip Mohr and published by Van Schaik.