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Peren F: Statistics for Business and
Economics

Statistics for Business and Economics

Compendium of Essential Formulas

Essentials

Predictive Intelligence bringt Klarheit in die KI-Debatte – und vor allem Wirkung in die Praxis. Dieses e
 ssential 
 zeigt, wie Unternehmen über klassische KI hinausgehen und mithilfe von Understanding Models sowie Consequence-Aware AI Prognosen in fundierte Entscheidungen überführen. Anschauliche Use Cases aus Marketing, Vertrieb, HR, Kommunikation und Nachhaltigkeit verdeutlichen, wie Predictive Intelligence zum zentralen Steuerungsinstrument des intelligenten Unternehmens wird.
 Ein kompakter Leitfaden für Fach- und Führungskräfte, die KI nicht nur verstehen, sondern deren Konsequenzen bewerten und in wirksame Entscheidungen übersetzen wollen.Aus dem Inhalt<ul><li>Historischer Abriss: Von Statistik und KI zu Predictive Intelligence</li><li>Abgrenzung: BI, Data Science, Predictive Analytics, Understanding Models und Predictive Intelligence</li><li>Methodik: Von Heuristiken über Best Strategies zu Next Best Decisions</li><li>Die technologische Einzigartigkeit von Predictive Intelligence und Consequence-Aware AI</li><li>Ausblick: The Intelligent Enterprise</li></ul>Die AutorenProf. Dr. Uwe G. Seebacher
 zählt zu den führenden europäischen Vordenkern für Predictive Intelligence sowie Methoden- und Strukturwissenschaften an der Schnittstelle von Wissenschaft und Praxis. Er ist Vorstand von FYNEST Agency und predictores.ai und mit mehreren renommierten internationalen Universitäten und Business Schools affiliiert.
 Prof. Dr.-Ing. Christoph Legat
 ist ein Unternehmer, Berater, Wissenschaftler und Experte für Normung und Technologie im Bereich Künstliche Intelligenz und Digitale Zwillinge. Er lehrt und forscht an der Technischen Hochschule Augsburg (THA) in Deutschland zu neuen Methoden und Konzepten für den erfolgreichen Einsatz moderner Informationstechnologien in der industriellen Digitalisierung.

Predictive Intelligence
 bringt Klarheit in die KI-Debatte – und vor allem Wirkung in die Praxis. Dieses Essential zeigt, wie Unternehmen Prognosen konsequent in fundierte, operative Entscheidungen überführen und sich vom reaktiven zum antizipativen Management entwickeln. Anschauliche Use Cases aus Marketing, Vertrieb, HR, Kommunikation und Nachhaltigkeit machen deutlich, wie Predictive Intelligence zum zentralen Steuerungsinstrument des Intelligent Enterprise wird.
 Ein kompakter Leitfaden für Fach- und Führungskräfte, die KI nicht nur verstehen, sondern strategisch nutzen und in wirksame Entscheidungen übersetzen wollen.&nbsp;

Von Daten und Prognosen zu besseren Entscheidungen

Predictive Intelligence

Chapman and Hall/CRC Financial Mathematics Series

Introduction to Financial Derivatives with Python, Second Editon continues to provide an accessible introduction to derivatives and quantitative finance. Starting from first principles, the book develops the foundations of derivative pricing before progressing to numerical methods and advanced volatility models. Mathematical concepts are introduced progressively, allowing the reader to develop the necessary tools alongside their financial applications. Financial intuition, mathematical foundations, and Python implementation are integrated throughout the book.The book covers the essential topics in derivative pricing and introduces numerical methods widely used in quantitative finance. It also develops advanced volatility models, including CEV, local volatility, Heston, and SABR.Features<ul> <li>Suitable for undergraduate and graduate students, as well as practitioners and anyone seeking an accessible introduction to quantitative finance</li> <li>Covers derivative pricing from fundamental principles to advanced volatility models</li> <li>Introduces numerical pricing techniques, including binomial trees and Monte Carlo simulation</li> <li>Provides chapter summaries, exercises, and examination material</li> <li>Accompanied by a GitHub repository containing the Python code used throughout the book</li> <li>No prior programming experience is required; introductions to Python and coding are provided.</li> </ul>New to the Second Edition<ul> <li>Fresh material on the Bachelier model and normal implied volatility</li> <li>A new chapter on local volatility covers the motivation for local volatility modelling, the CEV model, and Dupire's formula</li> <li>A new chapter on stochastic volatility develops the Heston and SABR models</li> <li>Python implementations to help the reader understand the concepts presented</li> <li>A new appendix including sample exams. This allows readers to practice the concepts learned throughout the book.</li> </ul>

Introduction to Financial Derivatives with Python

Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications

This book explores the integration of sustainable AI into industrial automation and its impact on workflows, human roles, and cost-benefit dynamics. It covers practical applications—such as cyber-physical systems, human-machine interfaces, and industrial robotics—while addressing green practices, economic viability, and key challenges like situational awareness and information retrieval. Designed for policymakers, researchers, corporate leaders, and students, it offers a comprehensive guide to building resilient, environmentally responsible, and automated industries.Prateek is an Assistant Professor at the Department of Electronics &amp; Communication Engineering at Madan Mohan Malaviya University of Technology, Gorakhpur, India. Previously, he was with IIIT, Pune, and IIIT Kota, India. His research interests include physical layer issues in wireless communication.Rajeev Arya is an Assistant Professor in the Department of Electronics Engineering at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. He was with NIT Patna for over seven years—he specializes in AI/ML applications for Wireless Sensor Networks, 5G/6G and UAV networks. Several projects funded by DST-SERB, MeitY, and DRDO-DMSRDE are to his credit, and he has organized key international conferences including SoCTA-2019, ICCET-2022, and MAI-2026.Ajit Kumar Verma is a distinguished academic, researcher, and thought leader with an exceptional career spanning engineering sciences, safety and reliability engineering. He is currently Professor of Technical Safety at the Western Norway University of Applied Sciences, Haugesund, and has been a professor in Norway since 2012. Earlier, he served for several years at IIT Bombay, including around 15 years as Professor/Senior (HAG scale) Professor in Reliability Engineering and Electrical Engineering. He was an adjunct faculty at the University of Stavanger (Norway), and held guest professorships at Luleå University of Technology (Sweden), and several institutions in India. He had supervised/ co-supervised 41 PhD students and over 100 Master's thesis in leading universities. He has been Editor-in-Chief of three International Journals published by Springer, in the past. He is globally ranked (2025) in the top three and in the top .05 percent of scholars in Safety Engineering worldwide for his lifetime contributions by US based Scholar GPS. He has received Lifetime Achievement Awards from various institutions and his recent book jointly authored received the highest citation award by Springer. Springer (Switzerland) has also published a commemorative volume in 2020 to honour his contributions.

This book combines sustainable industrial innovation with artificial intelligence to achieve the automation of industries. It explores how AI is impacting the human element in industries, the cost benefit analysis of AI implementation,&nbsp; streamlining of the workflow when&nbsp;AI is sustainably incorporated into industrial sector for automation; and the challenges and limitations faced by the researchers, corporate and policy-makers while planning, designing and operating industries automated by a sustainable AI. It contains a chapter dedicated to discussing development of a situationally aware industry using AI.&nbsp;The book presents a fresh flow of content related to sustainable computing aspects for industrial automation— importance of sustainable AI at various steps of the industrial lifecycle; chapter-wise focus on specific domains of industrial automation such as cyber physical systems, man-machine interface, industrial robotics etc., for a diverse coverage while remaining relevant to industrial automation; explores sustainability aspect of AI in terms of green practices, environmental impact, social impact, economic viability, resource-frugality etc.;&nbsp;dedicates separate chapters to critical topics such as survivability, information retrieval and processing and situational-awareness.Sustainability interests the policymakers in incorporating the judicious usage of AI tools into the industry compliance regulations. Implementation of AI in industries is useful for researchers as well as practitioners. Automation of industries using AI would interest the corporate readers. Various technologies discussed in the book shall interest graduates and pre-final undergraduates for staying up-to-date with the latest terminologies.

Sustainable Artificial Intelligence for Industrial Automation

Future of Business and Finance

Venture Project Design with AI-Supported ToolBoard

AI-Enhanced Entrepreneurship

Handbook of Labor, Human Resources and Population Economics

The First Principle in Empirical Research

Significance by Sight

This important new book explores how AI drives sustainable digital transformation, offering expert insights on strategy, innovation, governance, and value creation. With its integrated and sustainability-centered framework, the book creates a holistic perspective rarely addressed in existing AI and digital transformation literature. The focus is on how intelligent technologies are driving fundamental changes in the way organizations operate, compete, and create value. Its 13 chapters, from leading academics and industry specialists, offer a comprehensive and timely perspective on the sustainable impact of artificial intelligence on digital transformation. The information provided here goes beyond technological hype to explain how AI is reshaping responsible strategies, innovation processes, organizational structures, and managerial decision making. Through a combination of conceptual insights and applied perspectives, the chapters highlight practical approaches to implementing AI while addressing critical challenges such as governance, ethics, and workforce transformation. Readers gain a deeper understanding of how organizations can harness intelligent technologies to improve performance, build competitive advantage, and support sustainable growth. Designed for researchers, graduate students, executives, and policy professionals, this book serves both as a strategic resource and a scholarly reference. By bridging theory and practice, the text provides essential insights for those seeking to navigate complexity and lead effectively in an era of rapid technological change.

Artificial Intelligence-Driven Digital Transformation

Chapman and Hall/CRC Series on Statistics in Business and Economics

Bayesian methods are now central to empirical macroeconomics, with routine use in academic research and at central banks for forecasting and policy analysis. As the models applied in practice have become increasingly high-dimensional and computationally intensive, a working understanding of the estimation algorithms-not only the models themselves-has become essential. While many existing texts treat Bayesian econometrics broadly, Bayesian Macroeconometrics focuses specifically on the models and methods used in modern Bayesian macroeconomic analysis, for readers who want both to understand these methods and to implement them.Bringing together modeling, posterior derivations, algorithmic design, and computation, the book derives each method step by step and integrates MATLAB code directly into the text. It pays particular attention to how posterior samplers are constructed in high-dimensional settings, where implementation choices can determine whether a method is practical for empirical work.A single idea ties the material together: linear regression as a recurring building block, with state space models and vector autoregressions treated as collections of linear regressions linked by latent states or dynamic dependence. From this foundation, the book moves to the models at the center of modern practice, including stochastic volatility, dynamic factor models, vector autoregressions, time-varying parameter VARs, and large VARs, alongside global-local shrinkage priors and Bayesian nonparametrics.Each chapter is anchored by worked examples using real macroeconomic data, closes with exercises, and is supported by a companion website providing datasets and code in MATLAB, Python, and R. The book is suitable as a primary text for graduate courses in Bayesian macroeconometrics and Bayesian time-series analysis, and as a reference for researchers and central-bank practitioners.Key Features:<ul> <li>Comprehensive coverage of Bayesian macroeconometric models - from basic Bayesian regressions and mixture models to linear Gaussian state space models, stochastic volatility models, and vector autoregressions.</li> <li>Emphasis on practical implementation - each model is illustrated with empirical applications using macroeconomic data and supported by reproducible code in MATLAB, Python, and R.</li> <li>Discussion of modern computation techniques - covering a range of advanced Markov chain Monte Carlo methods, including Hamiltonian Monte Carlo.</li> <li>Accessible style - technical results are derived in a step-by-step manner and are complemented by intuitive explanations and worked examples.</li> <li>Coverage of cutting-edge models and methods - including Dirichlet process mixtures, Gaussian process regressions, large VARs, and global-local priors.</li> </ul>

Methods and Applications

Bayesian Macroeconometrics

This book provides a well-founded and practice-oriented analysis of blockchain technology as a digital disruptive business innovation in the context of small and medium-sized enterprises (SMEs). By combining theoretical foundations with an empirical case study in the field of document management, it demonstrates how technological concepts can be translated into practical business applications. The focus is on key strategic success factors such as user acceptance, resource availability, and organizational integration. The analysis is based on an international research framework and systematically incorporates current global studies and developments. In doing so, the author offers valuable insights for academia, business practice, and policymakers operating at the intersection of digitalization and the SME sector.&nbsp;The AuthorDr. Evgeniya Lange
 serves as a lecturer at various institutions. Her research focuses on digital disruptive innovations, digital transformation, and blockchain technologies. She earned her doctorate from Middlesex University London, where she specialized in blockchain innovation in the context of small and medium-sized enterprises (SMEs). She has also studied digital technologies and business at several renowned institutions, including Harvard University, the Massachusetts Institute of Technology (MIT), Stanford University, the University of Oxford, and Cornell University. Since 2008, she has been active as an entrepreneur, combining academic expertise with extensive practical experience in the SME sector and in government-funded digital transformation projects. Through her work, she bridges the gap between research and practice, with a particular focus on the opportunities and challenges of digital innovation for businesses.
 The translation was done with the help of artificial intelligence. A&nbsp;subsequent&nbsp;human revision was done primarily in terms of content.&nbsp; 
 This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.

This book provides a well-founded and practice-oriented analysis of blockchain technology as a digital disruptive business innovation in the context of small and medium-sized enterprises (SMEs). By combining theoretical foundations with an empirical case study in the field of document management, it demonstrates how technological concepts can be translated into practical business applications. The focus is on key strategic success factors such as user acceptance, resource availability, and organizational integration. The analysis is based on an international research framework and systematically incorporates current global studies and developments. In doing so, the author offers valuable insights for academia, business practice, and policymakers operating at the intersection of digitalization and the SME sector.

Advantages and Challenges of Blockchain Technology: A Case Study

Digital Disruptive Innovations: Blockchain for SMEs

The sixth edition of this leading reference book on insurance medicine provides a comprehensive guide to life expectancy for underwriters and clinicians involved in the life insurance industry. Extensively revised and expanded, the new edition reflects developments in life and healthcare insurance as well as medicine. Almost 20 years since it's last edition, each chapter&nbsp;is&nbsp;significantly updated to include the latest advancements in each disease, risk selection, and classification. Additionally, new content&nbsp;has been&nbsp;added to include rare disorders, the impact of climate and environmental changes on insurance medicine, screening for diseases and risk of disease, and critical illness. The impact of AI and other advanced technologies&nbsp;is&nbsp;also be discussed.&nbsp;

Brackenridge's Medical Selection of Life Risks

Lecture Notes in Economics and Mathematical Systems

This volume would prove to be a beneficial textbook option for economics faculties wishing to offer a PhD level course on quantitative developmental economics or applied economics. This reference volume offers a gentle introduction to various state-of-the-art mathematical and econometric modelling techniques with detailed applications. Each chapter, written by experienced scholars, describe a particular quantitative method for economic modelling and policy analysis. The chapters not only cover the theoretical foundations in a student-friendly language but also provide step-by-step guide to computational aspects. Real-world applications, given in each chapter, focus on the socio-economic conditions and well-being of people in the developing economies and middle-income countries.

Quantitative Methods for Applied Development Economics and Policy Analysis

Mit dem Buch „Digital Business Generator“ von Tobias Kollmann und Simon Hensellek bekommt der Leser ein praxisorientiertes Tool für den Aufbau digitaler Geschäftsmodelle in der Digitalen Wirtschaft in die Hand, mit dem er Schritt für Schritt entweder einen vollkommen neuen digitalen Geschäftsprozess entwickeln oder einen bereits vorhandenen realen Geschäftsprozess digital transformieren kann. Es geht also nicht nur um den Neuaufbau von digitalen Geschäftsmodellen, sondern auch um die Digitale Transformation bestehender realer Geschäftsprozesse. Neue Unternehmen (Startups) und etablierte Unternehmen (Mittelstand/Industrie) aus allen Branchen müssen und können sich entsprechend mit dem resultierenden „Digital Business Generator“ dieser Herausforderung einer Digitalen Innovation bzw. einer Digitalen Transformation stellen. Der Digital Business Generator (DBG) vermittelt als Tool ein umfassendes Rahmenwerk und zeigt auf, wie ein digitales Geschäftsmodell basierend auf Wertschöpfungsprozessen durch innovative Informationstechnologie (IT) verstanden, entworfen, implementiert und kontinuierlich (re-)evaluiert werden kann.
 Neu in der 2. Auflage
 
 In der 2. Auflage wurden alle Kapitel überarbeitet und die Terminologie von „E-Business“ auf „Digital Business“ umgestellt. Aktuelle Themen wie z. B. Digital Intrapreneurship, Digital Interpreneurship, Digitalisierungsdruck u.v.m. wurden einbezogen bzw. an neuere Entwicklungen angepasst. Ebenso wurden alle Praxisbeispiele und Medienhinweise auf einen neuen Stand gebracht.
 
 Die Zielgruppen
 
 Die Zielgruppe sind zum einen Praktiker, die sich mit dem Gedanken einer Unternehmensgründung in der Digitalen Wirtschaft befassen bzw. Gründer, die bereits in solchen Unternehmen arbeiten. Dazu gehören auch Vertreter von Mittelstand und Industrie, die über Inkubatoren oder Acceleratoren den Anschluss an die Startup-Szene suchen bzw. in den eigenen Unternehmen über Innovations- bzw. Fachabteilungen oder sogar schon eigenen Digital Units neue digitale Geschäftsmodelle aufbauen wollen. Eine weitere Zielgruppe sind zum anderen Studierende und Lehrkräfte, die sich mit den Themen (Digital) Entrepreneurship, Innovationsmanagement, Digital Business bzw. Digital Commerce beschäftigen.
 
 Die Autoren
 Prof. Dr. Tobias Kollmann
 &nbsp;ist Inhaber des Lehrstuhls für BWL und Wirtschaftsinformatik, insbesondere Digital Business und Digital Entrepreneurship, an der Universität Duisburg-Essen, Campus Essen.
 Dr. Simon Hensellek
 &nbsp;ist Juniorprofessor für Entrepreneurship und Digitalisierung an der Technischen Universität Dortmund.

Mit dem Buch „Digital Business Generator“ von Tobias Kollmann und Simon Hensellek bekommt der Leser ein praxisorientiertes Tool für den Aufbau digitaler Geschäftsmodelle in der Digitalen Wirtschaft in die Hand, mit dem er Schritt für Schritt entweder einen vollkommen neuen digitalen Geschäftsprozess entwickeln oder einen bereits vorhandenen realen Geschäftsprozess digital transformieren kann. Es geht also nicht nur um den Neuaufbau von digitalen Geschäftsmodellen, sondern auch um die Digitale Transformation bestehender realer Geschäftsprozesse. Neue Unternehmen (Startups) und etablierte Unternehmen (Mittelstand/Industrie) aus allen Branchen müssen und können sich entsprechend mit dem resultierenden „Digital Business Generator“ dieser Herausforderung einer Digitalen Innovation bzw. einer Digitalen Transformation stellen. Der Digital Business Generator (DBG) vermittelt als Tool ein umfassendes Rahmenwerk und zeigt auf, wie ein digitales Geschäftsmodell basierend auf Wertschöpfungsprozessen durch innovative Informationstechnologie (IT) verstanden, entworfen, implementiert und kontinuierlich (re-)evaluiert werden kann.
 Neu in der 2. Auflage
 
 In der 2. Auflage wurden alle Kapitel überarbeitet und die Terminologie von „E-Business“ auf „Digital Business“ umgestellt. Aktuelle Themen wie z.B. Digital Intrapreneurship, Digital Interpreneurship, Digitalisierungsdruck u.v.m. wurden einbezogen bzw. an neuere Entwicklungen angepasst. Ebenso wurden alle Praxisbeispiele und Medienhinweise auf einen neuen Stand gebracht.
 
 Die Zielgruppen
 
 Die Zielgruppe sind zum einen Praktiker, die sich mit dem Gedanken einer Unternehmensgründung in der Digitalen Wirtschaft befassen bzw. Gründer, die bereits in solchen Unternehmen arbeiten. Dazu gehören auch Vertreter von Mittelstand und Industrie, die über Inkubatoren oder Acceleratoren den Anschluss an die Startup-Szene suchen bzw. in den eigenen Unternehmen über Innovations- bzw. Fachabteilungen oder sogar schon eigenen Digital Units neue digitale Geschäftsmodelle aufbauen wollen. Eine weitere Zielgruppe sind zum anderen Studierende und Lehrkräfte, die sich mit den Themen (Digital) Entrepreneurship, Innovationsmanagement, Digital Business bzw. Digital Commerce beschäftigen.

Aufbau von Geschäftsmodellen und -prozessen in der Digitalen Wirtschaft

Digital Business Generator

Statistics for Business and Economics

Weitere Titel zum Thema