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Statistical: Ten Easy Ways to Avoid Being Misled By Numbers
by Anthony Reuben'Refreshingly clear and engaging' Tim Harford'Delightful . . . full of unique insights' Prof Sir David SpiegelhalterThere'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 £1bn 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 £350m bus really mean?How can we tell if the headline 'Public pensions cost you £4,000 a year' is correct?Does snow really cost the UK economy £1bn 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.
Statistical Analysis of Financial Data: With Examples In R (Chapman & Hall/CRC Texts in Statistical Science)
by James GentleStatistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
Statistical Analysis of Management Data
by Hubert GatignonStatistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures. Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.
Statistical Analysis of Operational Risk Data (SpringerBriefs in Statistics)
by Danilo Carità Francesco Martinelli Giovanni De LucaThis concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
Statistical Analysis of Reliability Data (Chapman And Hall/crc Texts In Statistical Science Ser. #27)
by Martin J. CrowderWritten for those who have taken a first course in statistical methods, this book takes a modern, computer-oriented approach to describe the statistical techniques used for the assessment of reliability.
Statistical Analysis with Excel For Dummies
by Joseph SchmullerBecome a stats superstar by using Excel to reveal the powerful secrets of statistics Microsoft Excel offers numerous possibilities for statistical analysis—and you don’t have to be a math wizard to unlock them. In Statistical Analysis with Excel For Dummies, fully updated for the 2021 version of Excel, you’ll hit the ground running with straightforward techniques and practical guidance to unlock the power of statistics in Excel. Bypass unnecessary jargon and skip right to mastering formulas, functions, charts, probabilities, distributions, and correlations. Written for professionals and students without a background in statistics or math, you’ll learn to create, interpret, and translate statistics—and have fun doing it! In this book you’ll find out how to: Understand, describe, and summarize any kind of data, from sports stats to sales figures Confidently draw conclusions from your analyses, make accurate predictions, and calculate correlations Model the probabilities of future outcomes based on past data Perform statistical analysis on any platform: Windows, Mac, or iPad Access additional resources and practice templates through Dummies.com For anyone who’s ever wanted to unleash the full potential of statistical analysis in Excel—and impress your colleagues or classmates along the way—Statistical Analysis with Excel For Dummies walks you through the foundational concepts of analyzing statistics and the step-by-step methods you use to apply them.
Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry
by Richard K. Burdick David J. Leblond Lori B. Pfahler Jorge Quiroz Leslie Sidor Kimberly Vukovinsky Lanju ZhangThis book examines statistical techniques that are critically important to Chemistry, Manufacturing, and Control (CMC) activities. Statistical methods are presented with a focus on applications unique to the CMC in the pharmaceutical industry. The target audience consists of statisticians and other scientists who are responsible for performing statistical analyses within a CMC environment. Basic statistical concepts are addressed in Chapter 2 followed by applications to specific topics related to development and manufacturing. The mathematical level assumes an elementary understanding of statistical methods. The ability to use Excel or statistical packages such as Minitab, JMP, SAS, or R will provide more value to the reader. The motivation for this book came from an American Association of Pharmaceutical Scientists (AAPS) short course on statistical methods applied to CMC applications presented by four of the authors. One of the course participants asked us for a good reference book, and the only book recommended was written over 20 years ago by Chow and Liu (1995). We agreed that a more recent book would serve a need in our industry. Since we began this project, an edited book has been published on the same topic by Zhang (2016). The chapters in Zhang discuss statistical methods for CMC as well as drug discovery and nonclinical development. We believe our book complements Zhang by providing more detailed statistical analyses and examples.
Statistical Applications for Environmental Analysis and Risk Assessment
by Joseph OfungwuStatistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "ready-made" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes:* Descriptions of basic statistical concepts and principles in an informal style that does not presume prior familiarity with the subject* Detailed illustrations of statistical applications in the environmental and related water resources fields using real-world data in the contexts that would typically be encountered by practitioners* Software scripts using the high-powered statistical software system, R, and supplemented by USEPA's ProUCL and USDOE's VSP software packages, which are all freely available* Coverage of frequent data sample issues such as non-detects, outliers, skewness, sustained and cyclical trend that habitually plague environmental data samples* Clear demonstrations of the crucial, but often overlooked, role of statistics in environmental sampling design and subsequent exposure risk assessment.
Statistical Arbitrage
by Andrew PoleWhile statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.
Statistical Capacity Building
by Thomas K. Morrison Zia Abbasi Noel Atcherley Jaroslav Ku Era Graham L. SlackIMF technical assistance provided by the Statistics Department -- toward assisting IMF member countries in developing the ability to provide reliable and comparable economic and financial data on a timely basis to policymakers and markets -- has increased more than fourfold over the past decade. This assistance has proven critical in countries' building their statistical capacity so as to come into line with international data standards in an increasingly globalized and electronically interconnected world. Statistical Capacity Building: Case Studies and Lessons Learned presents four case studies drawn from experience in three countries in transition to the market, two of which were also in postconflict situations, in the 1990s and early 2000s: Cambodia, Bosnia and Herzegovina, and Ukraine. Issues of setting, institutional and statistical arrangements, strategies, and implementation are examined, and lessons drawn.
Statistical Computing with R, Second Edition (Chapman & Hall/CRC The R Series)
by Maria L. RizzoComputational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. This second edition continues to encompass the traditional core material of computational statistics, with an
Statistical Data Analysis and Entropy (Behaviormetrics: Quantitative Approaches to Human Behavior #3)
by Nobuoki EshimaThis book reconsiders statistical methods from the point of view of entropy, and introduces entropy-based approaches for data analysis. Further, it interprets basic statistical methods, such as the chi-square statistic, t-statistic, F-statistic and the maximum likelihood estimation in the context of entropy. In terms of categorical data analysis, the book discusses the entropy correlation coefficient (ECC) and the entropy coefficient of determination (ECD) for measuring association and/or predictive powers in association models, and generalized linear models (GLMs). Through association and GLM frameworks, it also describes ECC and ECD in correlation and regression analyses for continuous random variables. In multivariate statistical analysis, canonical correlation analysis, T2-statistic, and discriminant analysis are discussed in terms of entropy. Moreover, the book explores the efficiency of test procedures in statistical tests of hypotheses using entropy. Lastly, it presents an entropy-based path analysis for structural GLMs, which is applied in factor analysis and latent structure models. Entropy is an important concept for dealing with the uncertainty of systems of random variables and can be applied in statistical methodologies. This book motivates readers, especially young researchers, to address the challenge of new approaches to statistical data analysis and behavior-metric studies.
Statistical Decision Problems
by Michael Zabarankin Stan UryasevStatistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more. The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.
Statistical Inference as a Bargaining Game
by Eduardo LeyA report from the International Monetary Fund.
Statistical Inference for Financial Engineering
by Masanobu Taniguchi Tomoyuki Amano Hiroaki Ogata Hiroyuki TaniaiThis monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e. g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e. g. , discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc. , which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.
Statistical Issues in Allocating Funds by Formula
by Panel on Formula AllocationsIn 2000, the federal government distributed over $260 billion of funding to state and local governments via 180 formula programs. These programs promote a wide spectrum of economic and social objectives, such as improving educational outcomes and increasing accessibility to medical care, and many are designed to compensate for differences in fiscal capacity that affect governments’ abilities to address identified needs. Large amounts of state revenues are also distributed through formula allocation programs to counties, cities, and other jurisdictions. Statistical Issues in Allocating Funds by Formula identifies key issues concerning the design and use of these formulas and advances recommendations for improving the process. In addition to the more narrow issues relating to formula design and input data, the book discusses broader issues created by the interaction of the political process and the use of formulas to allocate funds.Statistical Issues in Allocating Funds by Formula is only up-to-date guide for policymakers who design fund allocation programs. Congress members who are crafting legislation for these programs and federal employees who are in charge of distributing the funds will find this book indispensable.
Statistical Learning and Data Science
by Fionn Murtagh Mireille Gettler Summa Léon Bottou Bernard Goldfarb Catherine Pardoux Myriam TouatiData analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor
Statistical Learning of Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization)
by Francesca Greselin Laura Deldossi Luca Bagnato Maurizio VichiThis book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.
Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python
by Basilio de Braganca Pereira Calyampudi Radhakrishna Rao Fabio Borges de OliveiraStatistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
The Statistical Method in Economics and Political Science: A Treatise on the Quantitative and Institutional Approach to Social and Industrial Problems (Routledge Library Editions: Econometrics #4)
by P. Sargant FlorenceOriginally published in 1929. This balanced combination of fieldwork, statistical measurement, and realistic applications shows a synthesis of economics and political science in a conception of an organic relationship between the two sciences that involves functional analysis, institutional interpretation, and a more workmanlike approach to questions of organization such as division of labour and the control of industry. The treatise applies the test of fact through statistical analysis to economic and political theories for the quantitative and institutional approach in solving social and industrial problems. It constructs a framework of concepts, combining both economic and political theory, to systematically produce an original statement in general terms of the principles and methods for statistical fieldwork. The separation into Parts allows selective reading for the methods of statistical measurement; the principles and fallacies of applying these measures to economic and political fields; and the resultant construction of a statistical economics and politics. Basic statistical concepts are described for application, with each method of statistical measurement illustrated with instances relevant to the economic and political theory discussed and a statistical glossary is included.
Statistical Methods and Applications in Forestry and Environmental Sciences (Forum for Interdisciplinary Mathematics)
by Girish Chandra Raman Nautiyal Hukum ChandraThis book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling, small area estimation, calibration approach-based estimators, design of experiments, multivariate techniques, Internet of Things, and ridge regression methods. It also covers the history of the implementation of statistical techniques in Indian forestry and the National Forest Inventory of India.The book is a valuable resource for applied statisticians, students, researchers, and practitioners in the forestry and environment sector. It includes real-world examples and case studies to help readers apply the techniques discussed. It also motivates academicians and researchers to use new technologies in the areas of forestry and environmental sciences with the help of software like R, MATLAB, Statistica, and Mathematica.
Statistical Methods and Applications in Insurance and Finance
by M'Hamed Eddahbi El Hassan Essaky Josep VivesThis book is theoutcome of the CIMPA School on Statistical Methods and Applications inInsurance and Finance, held in Marrakech and Kelaat M'gouna (Morocco) in April2013. It presents two lectures and seven refereed papers from the school,offering the reader important insights into key topics. The first of thelectures, by Frederic Viens, addresses risk management via hedging in discreteand continuous time, while the second, by Boualem Djehiche, reviews statisticalestimation methods applied to life and disability insurance. The refereed papersoffer diverse perspectives and extensive discussions on subjects includingoptimal control, financial modeling using stochastic differential equations,pricing and hedging of financial derivatives, and sensitivity analysis. Eachchapter of the volume includes a comprehensive bibliography to promote furtherresearch.
Statistical Methods for Organizational Research: Theory and Practice
by Chris DewberryThis clearly written textbook clarifies the concepts underpinning descriptive and inferential statistics in organizational research. Acting as much more than a theoretical reference tool, step-by-step it guides readers through the various key stages of successful data analysis.Covering everything from introductory descriptive statistics to advanced inferential techniques such as ANOVA, multiple and logistic regression and factor analysis, this is one of the most comprehensive textbooks available. Using examples directly relevant to organizational research it includes practical advice on such topics as the size of samples required in research studies, using and interpreting SPSS, and writing up results. In helping readers to develop a sound understanding of statistical methods, rather than focusing on complex formulas and computations, this outstanding textbook is as appropriate for those who wish to refresh their knowledge as those new to the subject area.
Statistical Methods for Practice and Research: A Guide to Data Analysis Using SPSS (Response Books)
by Ajai S Gaur Sanjaya S GaurThere is a growing trend these days to use statistical methods to comprehend and explain various situations and phenomena in different disciplines. Managers, social scientists and practicing researchers are increasingly collecting information and applying scientific methods to analyze the data. The ability to use statistical methods and tools becomes a crucial skill for the success of such efforts. This book is designed to assist students, managers, academics and researchers in solving statistical problems using SPSS and to help them understand how they can apply various statistical tools for their own research problems. SPSS is a very powerful and user friendly computer package for data analyses. It can take data from most other file types and generate tables, charts, plots, and descriptive statistics, and conduct complex statistical analyses. After providing a brief overview of SPSS and basic statistical concepts, the book covers: - Descriptive statistics - t-tests, chi-square tests and ANOVA - Correlation analysis - Multiple and logistics regression - Factor analysis and testing scale reliability - Advanced data handling Illustrated with simple, practical problems, and screen shots, this book outlines the steps for solving statistical problems using SPSS. Although the illustrations are based on version 16.0 of SPSS, users of the earlier versions will find the book equally useful and relevant. Written in a reader-friendly, non-technical style, this book will serve as a companion volume to any statistics textbook.
Statistical Methods for the Information Professional: A Practical, Painless Approach to Understanding, Using, and Interpreting Statistics (Asist Monograph Ser.)
by Liwen VaughanIn this unique and useful book, the author clearly explains the statistical methods used in information science research, focusing on basic logic rather than mathematical intricacies. Her emphasis is on the meaning of statistics, when and how to apply them, and how to interpret the results of statistical analysis. Through the use of real-world examples, she shows how statistics can be used to improve services, make better decisions, and conduct more effective research. More than 80 helpful figures and tables, seven appendices, bibliography, and an index are included.