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to Stochastic Calculus for Finance A New Didactic Approach With 6 Figures 123 Prof. Dr. Dieter Sondermann Department of Economics University of Bonn Adenauer Allee 24 53113 Bonn, Germany E-mail: sondermann@uni-bonn. de ISBN-10 3-540-34836-0 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34836-8 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speci?cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on micro?lm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springeronline. com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany The use of general descriptive names, registered names, trademarks, etc. in this pub- cation does not imply, even in the absence of a speci?c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera ready by author Cover: Erich Kirchner, Heidelberg Production: LE-T X, Jelonek, Schmidt & Vöckler GbR, Leipzig E SPIN 11769675 Printed on acid-free paper – 42/3100 – 5 4 3 2 1 0 To Freddy, Hans and Marek, who patiently helped me to a deeper understanding of stochastic calculus.

This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.

This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Title from e-book title screen (viewed on Nov. 14, 2005).

This volume is the result of an'Advances in Econometrics'conference held in November of 2002 at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in 1980 and 1982 on robust variance-covariance estimation and quasi-maximum likelihood estimation. It contains 11 papers on a range of related topics including the estimation of possibly misspecified error component and fixed effects panel models, estimation and inference in possibly misspecified quantile regression models, quasi-maximum likelihood estimation of linear regression models with bounded and symmetric errors and quasi-maximum likelihood estimation of models with parameter dependencies between the mean vector and error variance-covariance matrix. Other topics include GMM, HAC, Heckit, asymmetric GARCH, Cross-Entropy, and multivariate deterministic trend estimation and testing under various possible misspecifications.

Financial fluctuations were generally neglected in classical ecnomics and their basic statistical properties have only recently been elucidated in the emerging field of econophysics, a new science that analyzes data using methods developed by statistical physics, such as chaos, fractals, and phase transitions. This volume is the proceedings of a workshop at which leading international researchers in this discipline discussed their most recent results and examined the validity of the empirical laws of econophysics. Topics include stock market prices and foreign exchange rates, income distribution, market anomalies, and risk management. The papers herein relate econophysics to other models, present new models, and illustrate the mechanisms by which financial fluctuations occur using actual financial data. Containing the most recent econophysics results, this volume will serve as an indispensable reference for economic theorists and practitioners alike.

This volume is dedicated to two recent intensive areas of research in the econometrics of panel data, namely nonstationary panels and dynamic panels. It includes a comprehensive survey of the nonstationary panel literature including panel unit root tests, spurious panel regressions and panel cointegration tests. In addition, it provides recent developments in the estimation of dynamic panel data models using generalized method of moments. The volume includes eleven chapters written by twenty authors. These chapters: investigate better methods of estimating dynamic panels; develop methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels; extend the concept of serial correlation common features analysis to nonstationary panel data models; study the local power of panel unit root test statistics; derive the asymptotic distributions of various estimators for the panel cointegrated regression model; propose a unit root test in the presence of structural change; develop a new limit theory for panel data that may be cross-sectionally heterogeneous; propose stationarity tests for a heterogeneous panel data model; derive instrumental variable estimators for a semiparametric partially linear dynamic panel data model; and conduct Monte Carlo experiments to study the small sample properties of a growth convergence equation. This collection of papers should prove useful for practitioners and researchers working with panel data.

This volume contains papers presented at a conference in May 1988 in Washington, D.C., commemorating the fiftieth anniversary of the founding of the Conference on Research in Income and Wealth (CRIW). The call for papers emphasized assessments of broad topics in economic measurement, both conceptual and pragmatic. The organizers desired (and succeeded in obtaining) a mix of papers that, first, illustrate the range of measurement issues that economics as a science must confront and, second, mark major milestones of CRIW accomplishment. The papers concern prices and output (Griliches, Pieper, Triplett) and also the major productive inputs, capital (Hulten) and labor (Hamermesh). Measures of saving, the source of capital accumulation, are covered in one paper (Boskin); measuring productivity, the source of much of the growth in per capita income, is reviewed in another (Jorgenson). The use of economic data in economic policy analysis and in regulation are illustrated in a review of measures of tax burden (Atrostic and Nunns) and in an analysis of the data needed for environmental regulation (Russell and Smith); the adequacy of data for policy analysis is evaluated in a roundtable discussion (chapter 12) involving four distinguished policy analysts with extensive government experience in Washington and Ottawa.

This edited volume celebrates the profound legacy of Peter Schmidt, an eminent figure in econometric research. Originally featured as a Special Issue in Empirical Economics in 2023, this book gathers esteemed econometricians to honor Schmidt's influential work. His distinguished career encompassed pioneering contributions to various realms of econometrics, including time series and panel data econometrics, as well as stochastic frontier analysis. This Festschrift beautifully captures his synergy of theoretical innovation and empirical significance.Written by distinguished econometricians, the volume presents the state-of-the-art in econometrics, traversing Schmidt's diverse interests. It spotlights his impact on applied econometrics and features 25 contributions on topics such as panel data econometrics, stochastic frontier analysis and efficiency/productivity measurement, time series methods, general applied econometrics, copulas, nonparametric methods, andlimited dependent variable models. Readers will gain an overview of the state of econometrics through the lens of Schmidt's multifaceted expertise, exemplifying the enduring resonance of Schmidt's scholarly journey and his indelible impact on the field.

Bernan Press proudly presents the nineteenth edition of Employment, Hours, and Earnings: States and Areas 2024. A special edition to the Handbook of U.S. Labor Statistics: Employment, Earnings, Prices, Productivity, and Other Labor Data, this reference isa consolidated wealth of employment information, providing monthly and annual data on hours worked and earnings made by industry, including figures and summary information spanning several years. These data are presented for states and the 75 largest metropolitan statistical areas (MSAs).This edition features:Nearly 300 tables with data on employment for each state, the District of Columbia, and the nation's seventy-five largest MSAsDetailed, nonseasonally adjusted industry data organized by month and yearHours and earnings data for each state by industryAn introduction for each state and the District of Columbia that denotes salient data and noteworthy trends, including changes in population and the civilian labor force, industry increases and declines, employment and unemployment statistics, and a chart detailing employment percentages by industryRanking of the seventy-five largest MSAs, including census population estimates, unemployment rates, and the percent change in total nonfarm employmentConcise technical notes that explain pertinent facts about the data, including sources, definitions, and significant changes, with references for further guidanceA comprehensive appendix that details the geographical components of the seventy-five largest MSAsThe employment, hours, and earnings data in this publication provide a detailed and timely picture of the fifty states, the District of Columbia, and the nation's seventy-five largest MSAs. These data can be used to analyze key factors affecting state and local economies and to compare national cyclical trends to local-level economic activity.This reference is an excellent source of information for analysts in both the public and private sectors. Readers who are involved in public policy can use these data to determine the health of the economy, to clearly identify which sectors are growing and which are declining, and to determine the need for federal assistance. State and local jurisdictions can use the data to determine the need for services, including training and unemployment assistance, and for planning and budgetary purposes. In addition, the data can be used to forecast tax revenue. In private industry, the data can be used by business owners to compare their business to the economy as a whole and to identify suitable areas when making decisions about plant locations, wholesale and retail trade outlets, and locating a particular sector base.

The book is a collection of different aspects of outliers and related topics written by experts. Topics covered include definition of outliers, their sources, consequences, identification, computational and robustness issues, handling of outliers in diversified areas of statistics such as univariate and multivariate data, linear and generalized linear models, time series, linear functional and structural models, circular data, spatial data, big data, high dimensional data, multi-view data. The book emphasizes the importance of outliers, and will appeal to workers in Data Mining; which is one of the fastest-growing business applications of statistics. The book makes outlier detection methods widely usable by practitioners. Examples are drawn from various fields.

Statistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.

The GII 2024 reveals who is leading globally in innovation, ranking the innovation performance of 133 economies and highlighting their strengths and weaknesses. The thematic focus of the 2024 edition is social entrepreneurship. It looks at how a flurry of new ventures are finding innovative solutions directly addressing critical societal issues. Examples drawn from around the world showcase successful examples of social entrepreneurship, helping guide innovation policymakers and support schemes to better scale social entrepreneurship ventures for maximum systemic impact.

This book addresses a wide range of recent methodological aspects, applications and best practices of statistics production. It comprises a selection of peer-reviewed contributions of methodological and applied interest presented at the 4th Conference of European Statistics Stakeholders, CESS 2022, held in Rome, Italy, on October 20-21, 2022. The first part discusses statistical methods with applications to environmental risk assessment, sentinels data, surveillance systems during the Covid-19 pandemic, healthcare risk management, the analysis of regional or structural changes of scale, household distributional accounts, regional rental prices on municipalities, the network topology of the Euro area interbank market, tourism statistics and big data, statistical literacy, and Sustainable Development Goal composite indicators for EU countries. The second part focuses on statistical methodologies for complex data analysis, namely the optimal number of clusters to rank a model-based index, clustering methods for asymmetric data using spectral approaches, a family of parsimonious matrix-variate mixture models for heavy-tailed data, the importance of robust second-stage regressions for financial data, and on perturbation methods. In view of the overarching theme “The European Data Ecosystem for the Statistical Information of the Digital Age” and the importance of statistical data for monitoring the progress of the United Nations'Sustainable Development Goals, the CESS 2022 meeting provided a forum for discussion on methodologies, results, challenges and best practices among methodologists, producers, and users of European Statistics from academia, the national statistical offices and the institutions of the European Union. Chapter Estimating regional rental prices on LAU 2 municipalities in North Rhine-Westphalia is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book explores the application of complex variables to econometric modeling. Providing a thorough introduction to the theory of complex numbers, it extends these concepts to develop complex-valued models that enhance the accuracy and depth of economic forecasting and data analysis. From simple to multiple complex linear regression, the monograph discusses model formulation, estimation techniques, and correlation analysis, supported by examples in R. This comprehensive guide is a useful resource for students, researchers, and practitioners aiming to apply advanced mathematical techniques to tackle complex real-life problems, making it a useful tool for enhancing predictive analytics in business, economics, and finance.

ベクトル自己回帰（VAR)に特化した実用書 本書はRを使ってベクトル自己回帰（VAR：vector autoregression analysis）分析を行うものです。理論に関する疑問、モデル構築に関する疑問、分析ツールをRで書くことの疑問など、VARに関する疑問に答え、実証分析を行ううえで役に立つ情報を提供します。 改訂2版では、「ヒストリカル分解」を章として拡充するとともに、構造安定性に関する仮説検定を追加し、単位根仮説検定のトピックを更に充実しました。また、本書で使用するRパッケージやR関数に関して再検討を行い、「CADFtest」を本書で使用するRパッケージに含めました。

Using a reader-friendly and straightforward approach, Langley/Novack/Gibson/Coyle's SUPPLY CHAIN MANAGEMENT: A LOGISTICS PERSPECTIVE, 12th Edition, blends logistics theory with practical applications. The latest content highlights emerging issues, technology developments and global changes in the constantly evolving and critically-important field of supply chain management. This edition examines today's real companies and how public and private organizations are responding to the continuing pressure to modernize and transform their supply chains. Dive into real-world businesses and see how organizations respond to the ongoing need for modernization in their supply chains. Experience hands-on learning with updated features, brief cases and global content -- providing a glimpse into the decisions supply chain managers make daily. Each chapter begins with profiles that spotlight real organizations, people or events -- underlining the significance of current supply chain matters.

Evans'MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 12th EDITION, presents the latest developments in the field as students examine fundamental principles, historical underpinnings of total quality, and quantitative methods and applications, all with a strong focus on Six Sigma and the Malcolm Baldrige framework for performance excellence. Using numerous examples and illustrations of quality management approaches in real organizations, review and discussion questions, cases and experiential projects, it provides a comprehensive foundation for understanding quality management.

Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.Key Features: Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis. Includes comprehensive quantitative methodologies for quantitative social science studies. Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience. Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.

This book, using R and RStudio, demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more. Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm's data—particularly in the case of finance, software, insurance and biotech firms—comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002). Audit effectiveness has declined over the past two decades, as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. This 2nd edition includes updated code and test. Machine learning, AI, and SEC's EDGAR data are also, improved and updated. The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits. Many examples reflect the focus of the 2024 CPA exam, and the data analytics-machine learning approach will be central to the AICPA's programs, in the near future.

Ante la necesidad frecuente de conocer técnicas para medir el capital intelectual a consecuencia de la importancia que este ha alcanzado en la era de la información como elemento generador de rentabilidad y productividad, el presente libro presenta una metodología sencilla y práctica para el cálculo de un coeficiente cuyo resultado permite medir cómo el capital intelectual crea valor en las organizaciones a partir de las eficiencias del capital humano, estructural y empleado que lo conforman. Asimismo, presenta los resultados de una investigación adelantada por la autora en torno a la creación de valor por parte de las grandes empresas colombianas a partir del capital intelectual, que buscó identificar las eficiencias que más explican los cambios en cuatro indicadores financieros que se escogieron para el estudio, como fueron: rentabilidad sobre la inversión, rentabilidad sobre el patrimonio, rotación de los activos totales y la utilidad operacional. Es así como Medición de la creación de valor a partir del capital intelectual en grandes empresas colombianas se constituye en una guía para estudiantes de contaduría y afines en lo que respecta al análisis del activo intangible con un enfoque más económico y gerencial; también puede ser utilizado por gerentes y consultores interesados en conocer métodos para medir el impacto del capital intelectual sobre aspectos de rentabilidad y productividad en las organizaciones; de igual modo, para estudiantes de doctorado del campo contable y afines, se puede constituir en una guía para abordar el estudio de lo intangible en las organizaciones con un enfoque empírico y cuantitativo.

In the modern symphony of business, each section-from the technical to the managerial-must play in harmony. Authors Ron Itelman and Juan Cruz Viotti introduce a bold methodology to synchronize your business and technical teams, transforming them into a single, high-performing unit. Misalignment between business and technical teams halts innovation. You'll learn how to transcend the root causes of project failure-the ambiguity, knowledge gaps, and blind spots that lead to wasted efforts. The unifying methodology in this book will teach you these alignment tools and more: The four facets of data products: A simple blueprint that encapsulates data and business logic helps eliminate the most common causes of wasted time and misunderstandingThe concept compass: An easy way to identify the biggest sources of misalignmentSuccess spectrums: Define the required knowledge and road map your team needs to achieve successJSON Schema: Leverage JSON and JSON Schema to technically implement the strategy at scale, including extending JSON Schema with custom keywords, understanding JSON Schema annotations, and hosting your own schema registryData hygiene: Learn how to design high-quality datasets aligned with creating real business value, and protect your organization from the most common sources of pain

Taking an amusing and digestible look at the usually dry world of probability and statistics, this is the ultimate guide to how you can incorporate them into everyday life, from one of the world's most sought-after experts in game theory. This is the only book you need to become a statistics whizz! Numbers are everywhere – food packaging, weather forecasts, social media, adverts, and more. You can't escape them. But you can learn to understand them – and avoid being fooled! This book breaks down the key fundamentals in statistics in a fun and accessible way so that you can understand the numbers that occupy your life. • Make sense of sports stats – discover who is the greatest scorer of all time • Learn to interpret scientific studies and how they're reported in the media so you're never misled again • Discover tips and tricks to make you a more successful gambler • Explore what role stats has to play in flat-earth conspiracy arguments • Read about misunderstood probabilities in the Sally Clarke and OJ Simpson trials With easy-to-follow explanations, tables, graphs, and real-life examples, this book helps you evaluate your options, calculate your chances of success, and make better decisions.

This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments.This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.

How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example.With this book, you will:Learn how to use basic concepts of causal inferenceFrame a business problem as a causal inference problemUnderstand how bias gets in the way of causal inferenceLearn how causal effects can differ from person to personUse repeated observations of the same customers across time to adjust for biasesUnderstand how causal effects differ across geographic locationsExamine noncompliance bias and effect dilution

Summarizing the Asia and the Pacific results of the 2021 International Comparison Program, this report estimates purchasing power parities (PPPs) and PPP-based measures of total and per capita real gross domestic product (GDP) for 21 economies. It also provides revised results for the 2017 ICP cycle. The PPPs enable comparison in real terms across economies by removing price level differences among them. The results provide policy makers and other users with useful information on price levels and PPP-based GDP expenditure values for comparative analysis and policy applications. The ICP is a global statistical initiative carried out under the auspices of the United Nations Statistical Commission.

Key Indicators for Asia and the Pacific 2024, the 55th edition of this series, includes the most recently available economic, financial, social, and environmental indicators for the 49 regional members of the Asian Development Bank. It presents the latest key statistics on development issues concerning the economies of Asia and the Pacific to a broad audience, including policymakers, development practitioners, government officials, researchers, students, and the general public. Section I of this issue provides an overview of the Asia and Pacific region's progress on climate action under Sustainable Development Goal 13, emphasizing the urgent need for strong policy interventions. Section II reviews the availability of appropriate climate change data in the context of the Global Set of Climate Change Statistics and Indicators, highlighting significant statistical gaps that must be addressed. Section III considers the value of geographically granular data for tailored policies—from identifying emissions hot spots to pinpointing vulnerable communities. Section IV discusses the need to strengthen statistical capacity and address key challenges, promising initiatives, and essential collaborations to improve the availability, granularity, timeliness, and accuracy of climate change data. This publication is available online at adb.org/publications/key-indicators-asia-and-pacific-2024, where additional tables containing greater detail on each of the 49 economies can also be accessed. Data relating to this 55th edition, including individual economy tables, are also available at kidb.adb.org.

In a VUCA world, which is becoming increasingly volatile, uncertain, and complex, companies, organizations, and states must respond promptly and adequately to the respective situations. Making decisions based on past experiences is less successful in these times than having an accurate understanding of current conditions. The importance of empirical sciences, continuous environmental observation, timely analysis of causal relationships, and deriving new insights from them is increasing. From this, it can be deduced which measures are likely to achieve one's goals with predictable probability, such as which price for an offer generates the desired demand or which marketing measure reaches the desired target group. Where classical statistics were once used for calculations and predictions, today free (open source) tools like R allow data in various formats and from any number of sources to be read in, processed, and analyzed using methods of Artificial Intelligence and Machine Learning. The results can then be perfectly visualized so that decision-makers can benefit quickly and effectively. The age of Data Science has arrived. Digitalization is more than a buzzword or a promise; it is actionable and usable for everyone. This book teaches you, based on the latest version of R at the time of publication, how to use Artificial Intelligence and Machine Learning in Industry 4.0.

This book focuses on four key issues that are crucial to achieving productivity and sustainability in the circular supply chain management industry. These key topics are the elements of circular supply chain management, statistical techniques such as the design of experiments and statistical process control, well-known fuzzy techniques in the performance measurement analysis of circular supply chains, and the indicators of managing productivity and sustainability before and after implementing the model. Presenting a creative combination of fuzzy and statistical techniques on performance measurement in circular supply chain management, this book would be of interest to experts and technical managers who are responsible for implementing productivity and sustainability models within the organization as well as academics.

This book explores multiple types of innovation within the modern wine industry and how it has developed historically. The book provides and extensive examination of the existing knowledge in this subject. This highlights the evolution of historical and contemporary trends and signposts the authoritative literature published and most important researchers active in the field. With this comprehensive approach, the book is a versatile resource for both scholars and industry professionals, akin to a'Swiss Army Knife'for all aspects of green innovation in the wine sector.

This 3rd edition revised and extended compendium contains and explains essential mathematical formulas within an economic context. Newly added content introduces the mathematical practical application of optimization by using the Lagrange function, presenting the issue of linear optimization cases where the relative extremes (minima or maxima) of a linear (target) function can be determined under restrictive linear constraint and its relevance for business management practice. A broad range of aids and supportive examples will help readers to understand the formulas and their practical applications. This mathematical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice. The topics presented include but are not limited to mathematical signs and symbols, logic, arithmetic, algebra, linear algebra, combinatorics, and financial mathematics, including an international comparison between different national methods used in the calculation of interest, optimization of linear models, functions, differential calculus, integral calculus, elasticities, annuity calculation, economic functions, and the Peren Theorem.Given its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.

Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics. The book argues that a solid'programming'skill level is not only well within reach for many but also worth pursuing for researchers and business analysts. The ability to write a program leverages field-specific expertise and fosters interdisciplinary collaboration as source code continues to become an important communication channel. Given the pace of the development in data science, many senior researchers and mentors, alongside non-computer science curricula lack a basic software engineering component. This book fills the gap by providing a dedicated programming-with-data resource to both academic scholars and practitioners.Key Features overview: breakdown of complex data science software stacks into core components applied: source code of figures, tables and examples available and reproducible solely with license cost-free, open source software reader guidance: different entry points and rich references to deepen the understanding of selected aspects

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field. It is structured thematically into parts focusing on statistical process monitoring, financial statistics, and spatial statistics with environmetrics, each featuring chapters from leading experts. The opening articles on statistical process monitoring present novel methodologies for the detection of anomalies and control charting techniques, which are crucial for maintaining quality in manufacturing processes. Detailed discussions are included on integrating multivariate statistical methods and real-time monitoring to enhance process reliability and efficiency. The part on financial statistics explores rigorous approaches in financial econometrics, with an emphasis on dynamic modelling of market volatility and risk assessment. Contributions cover advanced asset allocation strategies, leveraging high-dimensional data analysis, and the application of machine learning techniques. Spatial statistics and environmetrics are addressed through innovative research on the statistical analysis of environmental data. This includes the use of geostatistical models and hybrid models that combine traditional statistical techniques with machine learning to improve the prediction of environmental phenomena. Key topics here involve the modelling of extremes and airborne pollutants, the prediction of earthquakes using a smartphone-based sensor network, and reviews of selected topics essential in modern spatial statistics. Each part not only reflects Wolfgang Schmid's interests and impact in these areas but also provides detailed theoretical and applied studies, demonstrating how these sophisticated statistical methods can be effectively employed in practical scenarios. This makes the book an indispensable resource for researchers and practitioners looking to apply cutting-edge statistical techniques in these complex fields.

In the field of modern marketing, a pivotal challenge emerges as traditional strategies grapple with the complexities of an increasingly data-centric world. Marketers, researchers, and business consultants find themselves at a crossroads, navigating the intricate intersection of data science and strategic marketing practices. This challenge serves as the catalyst for Data-Driven Marketing for Strategic Success, a guide designed to address the pressing issues faced by academic scholars and professionals alike. This comprehensive exploration unveils the transformative power of data in reshaping marketing strategies, offering a beacon of strategic success in a sea of uncertainty. This book transcends the realm of traditional marketing literature. It stands as a useful resource, not merely adding elements to ongoing research but shaping the very future of how researchers, practitioners, and students engage with the dynamic world of data-driven marketing. It is strategically tailored to reach a diverse audience, offering valuable insights to academics and researchers exploring advanced topics, practitioners in the marketing industry seeking practical applications, and graduate students studying data science, marketing, and business analytics. Policymakers, ethicists, and industry regulators will find the dedicated section on ethical considerations particularly relevant, emphasizing the importance of responsible practices in the data-driven marketing landscape. Covering foundational concepts and advanced applications, the book delves into customer segmentation, predictive analytics, sentiment analysis, marketing attribution modeling, dynamic pricing optimization, ethical considerations, and future trends. Real-world case studies provide practical insights for marketers and data scientists, ensuring a comprehensive understanding of the transformative potential of data science in marketing. Data-Driven Marketing for Strategic Success is an indispensable compass, guiding both scholarly discussions and practical success in the ever-evolving domain of modern marketing through the strategic integration of data.

A reader-friendly introduction to the exciting world of statistics for the analysts, business leaders and policymakers of tomorrow.'Barrow and Shabab's Statistics for Economics, Accounting and Business Studies textbook has an excellent mix of application, intuition and statistical theory that is suitable for students with and without prior knowledge in statistics.'Dr Robert Riegler, Senior Lecturer in Economics, Aston University Statistics for Economics, Accounting, and Business Studies,8th edition by Michael Barrow and C. Rashaad Shabab, is a user-friendly introduction to the subject, aimed at students studying economics, accountancy, finance, and business. This comprehensive text applies a wide variety of statistical tools and techniques to real-world situations, with numerous examples and exercises integrated throughout to help you build up your knowledge and skills. Easy to follow and without requiring any prior knowledge of Statistics, this book will guide you through the essential principles of the field and provide a thorough analysis of statistical concepts and methods. Key features in this new edition include: A new chapter on Data Visualisation, reflecting the growing importance of this topic Application of statistics to issues of contemporary interest, such as the impact of global events like Covid-19, development economics, tourism, education and working hours A greater focus on data science and big data, with supporting online material including data sets, software and code (Python and R) to analyse the data, as well as Excel versions, included for those who prefer Plenty of exercises and problems to test your understanding of the subject and measure your progress'An accessible, real-world and practical book. What really struck me about is that the book goes beyond a'cook-book'approach (first, we do this, then we do that...). There is depth of analysis of the key methodological concepts.'Dr Nektarios Tzempelikos, Associate Professor of Marketing, Anglia Ruskin University'This has long been my recommended text for business school undergraduates studying economics, finance or accounting. The focus on applications over theory makes it suitable for students with and without A level maths. It is rightly popular with my students.'Professor David Paton, Nottingham University Business School

Business Statistics ISE

This informative book reveals the pervasive nature of large inaccuracies in economic statistics. Drawing on numerous real-world examples including case studies from the COVID-19 pandemic, Peter van Bergeijk presents profound insights into how downplaying these errors undermines the scientific rigour of economic analysis, and outlines how to manage uncertainty in economic analysis moving forward.Global in scope, the book reflects on the current problematic practices within economics, including measurement errors in the analysis of GDP, inflation, trade and employment. Chapters examine key case studies from advanced and developing countries, detailing how the persistence of measurement error impacted the assessment of these events at the time and in retrospect. The author further identifies the best practices for the assessment of error in statistics and equips the reader with the tools to self-assess inaccuracies in economic data in a way that improves evidence-based analysis and decision-making processes.Championing a fresh perspective on econometrics, this book is vital for scholars and students of economics, data science and research methods. Additionally, policy analysts and data producers will value this book's perspective on the reliability of their data and its provision of problem-solving strategies.

In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies.This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data.• Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making.• Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context.• Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions.• Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.

Now in its second edition, this important book examines test validity in the behavioral, social, and educational sciences by exploring three fundamental problems: measurement, causation, and meaning. Psychometric and philosophical perspectives and unresolved issues receive attention, as the authors explore how measurement is conceived from both the classical and modern perspectives.Split into three accessible sections, the first contrasts theories of measurement as applied to the validity of behavioral science measures, and the second considers causal theories of measurement as well as alternative theories of causation. The final section explores the meaning and interpretation of test scores as they apply to test validity, offering a conceptual overview of the field and its current state. Each carefully revised chapter begins with an overview of key theories and literature, concludes with a list of suggested readings, and features boxes with real-life situations that connect theory to practice. Examples of specific issues include: How tests can assess an attribute without measuring it. The role of values in test validity. Interpreting responses to the same question in different languages. Researchers, practitioners, and policy makers interested in test validity or developing tests will appreciate the book's cutting-edge review of test validity. Focusing on both the underlying concepts, as well as practical challenges of test construction and use, it also serves as a supplement in graduate or advanced undergraduate courses on test validity, psychometrics, testing, or measurement taught in psychology, education, sociology, social work, political science, business, criminal justice, and other fields. The book does not assume a background in measurement.

Data graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book presents a practical approach to data visualisation with real applications front and centre.The first part of the book is a series of case studies, each describing a graphical analysis of a real dataset. The second part pulls together ideas from the case studies and provides an overview of the main factors affecting understanding graphics.Key Features: Explains how to get insights from graphics. Emphasises the value of drawing many graphics. Underlines the importance for analysis of background knowledge and context. Readers may be data scientists, statisticians or people who want to become more visually literate. A knowledge of Statistics is not required, just an interest in data graphics and some experience of working with data. It will help if the reader knows something of basic graphic forms such as barcharts, histograms, and scatterplots.

This book provides professionals and practitioners with a comprehensive guide on credit risk modeling, capital modeling, and validation for Current Expected Credit Loss (CECL), International Financial Reporting Standard 9 (IFRS9), Basel Capital and Comprehensive Capital Analysis and Review (CCAR) procedures. It describes how credit risk modeling, capital modeling, and validation are done in big banks with code and examples. The book features innovative concepts such as Binary Logit Approximation (BLA) for Competing Risk Framework; Adaptive and Exhaustive Variable Selection (AEVS) for automatic modeling; Full Observation Stratified Sampling (FOSS) for unbiased sampling; and Prohibited Correlation Index (PCI) for Fair Lending Texts. It also features a chapter on credit underwriting and scoring, addressing the credit underwriting risk with some innovations. It is a valuable guide for professionals, practitioners and graduate students in risk management.

This publication is the seventh in a series of pocketbook compilations on energy statistics designed to highlight the availability of data on various aspects of energy production, transformation and use and its linkages to other key statistics. Energy is central to the achievement of the 2030 Agenda for Sustainable Development and the Paris Agreement on climate change, and sound energy statistics are the basis for the reliable measurement of progress, thereby assisting the formulation of policy measures to achieve international and national sustainable development goals.

The Energy Statistics Yearbook 2021 is the sixty-fifth issue in a series of annual compilations of internationally comparable statistics summarizing world energy trends. Annual data for 225 countries and areas for the period 2018 to 2021 are presented on production, trade and consumption of energy: solids, liquids, gaseous fuels, electricity and heat, covering both renewable and non-renewable sources of energy. In addition, per capita consumption series are also provided for all energy products, and for the first time a special table on CO2 emissions is published in the Yearbook. Graphs are included to illustrate historic trends and/or changes in composition of production and/or consumption of major energy products. Special tables of interest include: CO2 emissions from fossil fuels, international trade tables for coal, crude petroleum and natural gas by partner countries – providing information on direction of trade; selected series of statistics on renewables and wastes; refinery distillation capacity; and a table on selected energy resources.

The 2021 Electricity Profiles contains electricity profiles for about 225 countries and territories, providing detailed information on production, trade and consumption of electricity, net installed capacity and thermal power plant input and efficiency for all countries. Electricity production and electric installed capacity are disaggregated by source, as coming from combustible fuels, hydro, nuclear and other sources; and by type of producer, whether from main activity producers or auto producers.

The Energy Balances 2021 contains energy balances for over 220 countries and territories, showing production, trade, transformation and consumption (by sector) in energy units for all energy products. It presents energy data for countries and areas in a format which shows the overall picture of the production, trade, transformation and consumption of energy products utilized in the region. Such a format is useful in assessing and analyzing supply and consumption patterns across both products and countries in detail on an internationally comparable basis.

Master the fundamental concepts and applications of operations (OM) and supply chain management (SCM) with OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 3E by award-winning authors Collier/Evans. This edition balances coverage of both manufacturing and service businesses with the latest updates, an additional new SCM chapter and new discussions that highlight the latest changes in OM and SCM. Clear explanations are supported with contemporary examples and new and updated case studies that demonstrate how concepts apply. Discussions highlight new techniques and principles as well as the most recent Excel techniques and digital tools. Solved problems further guide you through key formulas and computations. MindTap online learning platform is available for both manual calculations and the use of Excel spreadsheet templates and models. MindTap's algorithmic homework and interactive learning tools also show you how to apply qualitative and quantitative reasoning to today's OM and SCM concepts.

This thoroughly revised edition of the California Environmental Law and Natural Resources Handbook is an invaluable reference manual for lawyers, students, conservationists, and developers. It will take you step-by-step through statutes, programs, and stringent environmental standards in the State of California.Various agencies, departments, and boards are charged with the authority to protect the environment and natural resources. Governmental responsibility for implementing and enforcing the law is divided among many governmental bodies, including statewide agencies, special districts, regional boards, and city and county governments. The California Environmental Law and Natural Resource Handbook seeks to simplify California's complex laws by providing a clearly written comprehensive overview of key environmental topics impacting the State of California. It includes the latest information on topics such as water quality, hazardous waste control, oil spill prevention, and wetland protections.

The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the statistical and data science programming language R. These solutions include examples of complex real-world data, adding greater context and exposing the reader to greater technical challenges. This book focuses on the Import to Tidy to Transform steps. It demonstrates how “Visualise” is an important part of Exploratory Data Analysis, a strategy for identifying potential problems with the data prior to cleaning.This book is designed for readers with a working knowledge of data manipulation functions in R or other programming languages. It is suitable for academics for whom analyzing data is crucial, businesses who make decisions based on the insights gleaned from collecting data from customer interactions, and public servants who use data to inform policy and program decisions. The principles and practices described within The Data Preparation Journey apply regardless of the context.Key Features: Includes R package containing the code and data sets used in the book Comprehensive examples of data preparation from a variety of disciplines Defines the key principles of data preparation, from access to publication