'Fascinating . . . timely' Daily Mail 'Refreshingly clear and engaging' Tim Harford 'Delightful . . . full of unique insights' Prof Sir David Spiegelhalter There's no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not. Do missed appointments really cost the NHS GBP1bn per year? What's the difference between the mean gender pay gap and the median gender pay gap? How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate? What did the Vote Leave campaign's GBP350m bus really mean? How can we tell if the headline 'Public pensions cost you GBP4,000 a year' is correct? Does snow really cost the UK economy GBP1bn per day? But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the world around us. Written by Anthony Reuben, the BBC's first head of statistics, Statistical is an accessible and empowering guide to challenging the numbers all around us.

This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.

Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasising statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning in the classroom while emphasising intuitive concepts of probability and teaching students to make informed business decisions. The 14th Edition continues to highlight the importance of ethical behaviour in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States. This package includes MyLab. For courses in Business Statistics. A classic text for accuracy and statistical precision Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions. In this regard, it emphasizes an understanding of the assumptions that are necessary for professional analysis. In particular, it has greatly expanded the number of applications that utilize data from applied policy and research settings. The Ninth Edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics. This revision recognizes the globalization of statistical study and in particular the global market for this book. Reach every student by pairing this text with MyLab Statistics MyLab (TM) is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student. MyLab Statistics should only be purchased when required by an instructor. Please be sure you have the correct ISBN and Course ID. Instructors, contact your Pearson representative for more information.

This book includes discussions related to solutions of such tasks as: probabilistic description of the investment function; recovering the income function from GDP estimates; development of models for the economic cycles; selecting the time interval of pseudo-stationarity of cycles; estimating characteristics/parameters of cycle models; analysis of accuracy of model factors. All of the above constitute the general principles of a theory explaining the phenomenon of economic cycles and provide mathematical tools for their quantitative description. The introduced theory is applicable to macroeconomic analyses as well as econometric estimations of economic cycles.

The methodological needs of environmental studies are unique in the breadth of research questions that can be posed, calling for a textbook that covers a broad swath of approaches to conducting research with potentially many different kinds of evidence. Fully updated to address new developments such as the effects of the internet, recent trends in the use of computers, remote sensing, and large data sets, this new edition of Research Methods for Environmental Studies is written specifically for social science-based research into the environment. This revised edition contains new chapters on coding, focus groups, and an extended treatment of hypothesis testing. The textbook covers the best-practice research methods most used to study the environment and its connections to societal and economic activities and objectives. Over five key parts, Kanazawa introduces quantitative and qualitative approaches, mixed methods, and the special requirements of interdisciplinary research, emphasizing that methodological practice should be tailored to the specific needs of the project. Within these parts, detailed coverage is provided on key topics including the identification of a research project, hypothesis testing, spatial analysis, the case study method, ethnographic approaches, discourse analysis, mixed methods, survey and interview techniques, focus groups, and ethical issues in environmental research. Drawing on a variety of extended and updated examples to encourage problem-based learning and fully addressing the challenges associated with interdisciplinary investigation, this book will be an essential resource for students embarking on courses exploring research methods in environmental studies.

Places, Towns and Townships is an excellent resource for anyone in need of data for all of the nation's cities, towns, townships, villages, and census-designated places in one convenient source. It compiles essential information about places in the United States and the people who live in them such as: * population * housing * income * education * employment * crime * and much more! In addition to the tables, Places, Towns and Townships includes text that describes key findings, figures that call attention to noteworthy trends in data, and rankings of the largest cities by various demographics. Compiled from multiple government sources, the data in this unique reference volume represents the most current and accurate information available. This data will not be updated for several years, making Places, Towns and Townships an invaluable resource in the years to come.

The book describes the theoretical principles of nonstatistical methods of data analysis but without going deep into complex mathematics. The emphasis is laid on presentation of solved examples of real data either from authors' laboratories or from open literature. The examples cover wide range of applications such as quality assurance and quality control, critical analysis of experimental data, comparison of data samples from various sources, robust linear and nonlinear regression as well as various tasks from financial analysis. The examples are useful primarily for chemical engineers including analytical/quality laboratories in industry, designers of chemical and biological processes. Features: Exclusive title on Mathematical Gnostics with multidisciplinary applications, and specific focus on chemical engineering. Clarifies the role of data space metrics including the right way of aggregation of uncertain data. Brings a new look on the data probability, information, entropy and thermodynamics of data uncertainty. Enables design of probability distributions for all real data samples including smaller ones. Includes data for examples with solutions with exercises in R or Python. The book is aimed for Senior Undergraduate Students, Researchers, and Professionals in Chemical/Process Engineering, Engineering Physics, Stats, Mathematics, Materials, Geotechnical, Civil Engineering, Mining, Sales, Marketing and Service, and Finance.

Thorough presentation of the problem of portfolio optimization, leading in a natural way to the Capital Market Theory Dynamic programming and the optimal portfolio selection-consumption problem through time An intuitive approach to Brownian motion and stochastic integral models for continuous time problems The Black-Scholes equation for simple European option values, derived in several different ways A chapter on several types of exotic options and one on material on the management of risk in several contexts

Features: New chapters on Barrier Options, Lookback Options, Asian Options, Optimal Stopping Theorem, and Stochastic Volatility. Contains over 235 exercises, and 16 problems with complete solutions. Added over 150 graphs and figures, for more than 250 in total, to optimize presentation. 57 R coding examples now integrated into the book for implementation of the methods. Substantially class-tested, so ideal for course use or self-study.

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

A thrilling behind-the-scenes exploration of how governments past and present have been led astray by bad data - and why it is so hard to measure things and to do it well.

Our politicians make vital decisions and declarations every day that rely on official data. But should all statistics be trusted? In BAD DATA, House of Commons Library statistician Georgina Sturge draws back the curtain on how governments of the past and present have been led astray by figures littered with inconsistency, guesswork and uncertainty.

Discover how a Hungarian businessman's bright idea caused half a million people to go missing from UK migration statistics. Find out why it's possible for two politicians to disagree over whether poverty has gone up or down, using the same official numbers, and for both to be right at the same time. And hear about how policies like ID cards, super-casinos and stopping ex-convicts from reoffending failed to live up to their promise because they were based on shaky data.

With stories that range from the troubling to the empowering to the downright absurd, BAD DATA reveals secrets from the usually closed-off world of policy-making. It also suggests how - once we understand the human story behind the numbers - we can make more informed choices about who to trust, and when.

Appropriate for one or two term courses in introductory Business Statistics. With Statistics for Management, Levin and Rubin have provided a non-intimidating business statistics textbook that students can easily read and understand. Like its predecessors, the Seventh Edition includes the absolute minimum of mathematical/statistical notation necessary to teach the material. Concepts are fully explained in simple, easy-to-understand language as they are presented, making the text an excellent source from which to learn and teach. After each discussion, readers are guided through real-world examples to show how textbook principles work in professional practice.

Business Statistics narrows the gap between theory and practice by focusing on relevant statistical methods, thus empowering business students to make good, data-driven decisions. Using the latest GAISE (Guidelines for Assessment and Instruction in Statistics Education) report, which included extensive revisions to reflect both the evolution of technology and new wisdom on statistics education, this edition brings a modern edge to teaching business statistics. This includes a focus on the report's key recommendations: teaching statistical thinking, focusing on conceptual understanding, integrating real data with a context and a purpose, fostering active learning, using technology to explore concepts and analyse data, and using assessments to improve and evaluate student learning. By presenting statistics in the context of real-world businesses and by emphasising analysis and understanding over computation, this book helps students be more analytical, prepares them to make better business decisions, and shows them how to effectively communicate results. Samples Preview the detailed table of contents Download a sample chapter from Business Statistics, Global Edition, 4th Edition

Welcome to Economics Express - a series of short books to help you: * take exams with confidence * prepare and deliver successful assignments * understand quickly * revise and prepare effectively. As you embark on your economic journey, this series of books will be your helpful companion. They are not meant to replace your lectures, textbooks, seminars or any other sources suggested by your lecturers. Rather, as you come to an exam or an assignment, they will help you to revise and prepare effectively. Whatever form your assessment might take, each book in the series will help you to build up the skills and knowledge you will need to maximise your performance. Each topic-based chapter will outline the key information and analysis, provide sample questions with responses, and give you the assessment advice and exam tips you will need to produce effective assessments based on these core topics. A companion website provides supporting resources for self testing, assessment, exam practice and answers to questions in the book. Ian Jacques was formerly a senior lecturer at Coventry University. He has considerable experience teaching mathematical methods to students studying economics, business and accounting.

This book covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing). Each chapter starts with the necessary theoretical background, which is followed by a variety of examples. The core examples are based on the content of the respective chapter, while the advanced examples, designed to deepen students' knowledge, also draw on information and material from previous chapters. The enhanced online version helps students grasp the complexity and the practical relevance of statistical analysis through interactive examples and is suitable for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields, e.g. economics, the social sciences etc.

Mastering the basic concepts of mathematics is the key to understanding other subjects such as Economics, Finance, Statistics, and Accounting. Mathematics for Finance, Business and Economics is written informally for easy comprehension. Unlike traditional textbooks it provides a combination of explanations, exploration and real-life applications of major concepts.

Mathematics for Finance, Business and Economics discusses elementary mathematical operations, linear and non-linear functions and equations, differentiation and optimization, economic functions, summation, percentages and interest, arithmetic and geometric series, present and future values of annuities, matrices and Markov chains.

Aided by the discussion of real-world problems and solutions, students across the business and economics disciplines will find this textbook perfect for gaining an understanding of a core plank of their studies.

A variety of different social, natural and technological systems can be described by the same mathematical framework. This holds from Internet to the Food Webs and to the connections between different company boards given by common directors. In all these situations a graph of the elements and their connections displays a universal feature of some few elements with many connections and many with few. This book reports the experimental evidence of these Scale-free networks'' and provides to students and researchers a corpus of theoretical results and algorithms to analyse and understand these features. The contents of this book and their exposition makes it a clear textbook for the beginners and a reference book for the experts.

Panel Data Econometrics: Empirical Applications introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made.

This is the first textbook designed to teach statistics to students in aviation courses. All examples and exercises are grounded in an aviation context, including flight instruction, air traffic control, airport management, and human factors. Structured in six parts, theiscovers the key foundational topics relative to descriptive and inferential statistics, including hypothesis testing, confidence intervals, z and t tests, correlation, regression, ANOVA, and chi-square. In addition, this book promotes both procedural knowledge and conceptual understanding. Detailed, guided examples are presented from the perspective of conducting a research study. Each analysis technique is clearly explained, enabling readers to understand, carry out, and report results correctly. Students are further supported by a range of pedagogical features in each chapter, including objectives, a summary, and a vocabulary check. Digital supplements comprise downloadable data sets and short video lectures explaining key concepts. Instructors also have access to PPT slides and an instructor’s manual that consists of a test bank with multiple choice exams, exercises with data sets, and solutions. This is the ideal statistics textbook for aviation courses globally, especially in aviation statistics, research methods in aviation, human factors, and related areas.

Provides sound knowledge of optimal decision making in statistics and operations research problems. Serves a quick reference by exploring the research literature on the subject with commercial value-added research applications in statistics and operations research. Provides sound knowledge of optimisations and statistical techniques in modelling of real-world problems. Reviews recent developments and contributions in optimal decision-making problems using optimisation and statistical techniques. Provides an understanding of formulations of decision-making problems and their solution procedures. Describes latest developments in modelling of real-world problems and their solution approaches.

This book has become one of the main statistical tools for the analysis of economic and financial data. Designed for both theoreticians and practitioners, this book provides a comprehensive treatment of GMM estimation and inference. All the main statistical results are discussed intuitively and proved formally, and all the inference techniques are illustrated using empirical examples in macroeconomics and finance. 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.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Biophysical Measurement in Experimental Social Science Research is an ideal primer for the experimental social scientist wishing to update their knowledge and skillset in the area of laboratory-based biophysical measurement. Many behavioral laboratories across the globe have acquired increasingly sophisticated biophysical measurement equipment, sometimes for particular research projects or for financial or institutional reasons. Yet the expertise required to use this technology and integrate the measures it can generate on human subjects into successful social science research endeavors is often scarce and concentrated amongst a small minority of researchers. This book aims to open the door to wider and more productive use of biophysical measurement in laboratory-based experimental social science research. Suitable for doctoral students through to established researchers, the volume presents examples of the successful integration of biophysical measures into analyses of human behavior, discussions of the academic and practical limitations of laboratory-based biophysical measurement, and hands-on guidance about how different biophysical measurement devices are used. A foreword and concluding chapters comprehensively synthesize and compare biophysical measurement options, address academic, ethical and practical matters, and address the broader historical and scientific context. Research chapters demonstrate the academic potential of biophysical measurement ranging fully across galvanic skin response, heart rate monitoring, eye tracking and direct neurological measurements. An extended Appendix showcases specific examples of device adoption in experimental social science lab settings.