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Schumacher E: Angewandte Statistik

Angewandte Statistik

Eine Einführung für Nichtmathematiker

Comprehensive Textbook Resource for Understanding Transport Modelling Modelling Transport provides unrivalled depth and breadth of coverage on the topic of transport modelling. Each topic is approached as a modelling exercise with discussion of the roles of theory, data, model specification, estimation, validation, and application. The authors present the state of the art and its practical application in a pedagogic manner, easily understandable to both students and practitioners. An accompanying website hosts a solutions manual. Sample topics and learning resources included in the work are as follows: <ul><li>State-of-the-art developments in the field of transport modelling, including new research and examples</li><li>Factors to consider for better modelling and forecasting</li><li>Information and analysis on dynamic assignment and micro-simulation and model design and specification</li><li>Agent and Activity Based Modelling</li><li>Modelling new modes and services</li></ul> Graduate students in transportation engineering and planning, transport economics, urban studies, and geography programs along with researchers and practitioners in the transportation and urban planning industry can use Modelling Transport as a comprehensive reference work for a wide array of topics pertaining to this field.

Comprehensive Textbook Resource for Understanding Transport Modelling

Modelling Transport provides unrivalled depth and breadth of coverage on the topic of transport modelling. Each topic is approached as a modelling exercise with discussion of the roles of theory, data, model specification, estimation, validation, and application. The authors present the state of the art and its practical application in a pedagogic manner, easily understandable to both students and practitioners. An accompanying website hosts a solutions manual.

Sample topics and learning resources included in the work are as follows:
* State-of-the-art developments in the field of transport modelling, including new research and examples
* Factors to consider for better modelling and forecasting
* Information and analysis on dynamic assignment and micro-simulation and model design and specification
* Agent and Activity Based Modelling
* Modelling new modes and services

Graduate students in transportation engineering and planning, transport economics, urban studies, and geography programs along with researchers and practitioners in the transportation and urban planning industry can use Modelling Transport as a comprehensive reference work for a wide array of topics pertaining to this field.

Modelling Transport

Aufgaben

Mathematik in der Wirtschaftsschule - Band 1 (Print inkl. E-Book Edubase, Neuauflage 2024)

In diesen Buch wird die Technik der "Artificial Intelligence" von ihren Grundlagen her, das heißt Datensammlungen, statistischen Werkzeugen und Schätzung für neue Ereignisse, betrachtet und daran die Frage diskutiert, ob denn "Technik ethisch sein kann". Dabei zeigt sich ein Spannungsfeld zwischen im öffentlichen Diskurs oft behandelten Narrativen, wie "Artificial Intelligence" wahrgenommen wird, und den technischen Grundlagen, welche ein Verständnis von angewandter Statistik erfordern. Während diese Narrative oft von "self-learning", "autonomous" oder sogar "thinking" ausgehen, führt die Technik aber nur das aus, was Menschen programmiert haben oder Nutzer durch ihre Eingaben abrufen. Wenn Ethik die Frage des "richtigen" Handelns ist, dann betrifft dies immer die Entwickler, Betreiber und Nutzer solcher technischen Werkzeuge, denn diese sind immer nur Objekte, aber nie Subjekte von ethischen Fragen. Dazu bedarf es immer eines Verständnis der Statistik als Grundlage und eines verantwortlichen Umgangs mit den dabei genutzten Daten.Der Inhalt<ul><li>Einleitung: Kann denn Technik ethisch sein?</li><li>Narrative über AI, ChatBots und Roboter</li><li>Richtiges Handeln versus statistische Maschinen</li><li>Technikfolgen und Gesellschaft</li><li>Der Versuch einer Leitlinie für die Menschen</li><li>Anhang: Entwicklung der AI</li></ul>Der AutorDr. Udo Milkau ist ein "digitaler Dinosaurier", der sich seit 1974 mit - nach damaliger Terminologie - Digitaltechnik beschäftigt und seit Beginn der 1990er-Jahre mit "Artificial Intelligence". Nach über 35 Jahren Berufserfahrung insbesondere in der Finanzindustrie und Tätigkeit als Lehrbeauftragter an der Goethe Universität Frankfurt und der Frankfurt School of Finance and Management lehrt er heute an der Dualen Hochschule Baden-Würtemberg (DHBW) in Mosbach. Er hat bis heute über 130 Veröffentlichungen und vier Bücher zu Themen der Digitalisierung in Banken und zum Risikomanagement publiziert.

Kann denn Technik ethisch sein?

Künstliche Intelligenz zwischen Ethik und Statistik

Wiley Finance

PRAISE FOR FINANCIAL DATA ANALYTICS "Really interesting, and an impressive masterpiece! Financial Data Analytics contains a rich amount of material, with original research findings in almost every chapter; many parts of the book will even be directly helpful for my own teaching in business school. In view of its dedication towards data-driven analytical tools genuinely needed in financial problems, I believe that it is the very book that defines the scope of financial data analytics." -Alain Bensoussan, Fellow of AMS, IEEE, and SIAM; President of INRIA (1984-1996); President of CNES (Centre National d'Etudes Spatiales) (1996-2003); Chairman of ESA Council (European Space Agency) (1999-2002); Former Member of Advisory Board, Mathematical Finance; Lars Magnus Ericsson Chair Professor of Management, Naveen Jindal School of Management, University of Texas at Dallas "Financial Data Analytics is an exceptional book that integrates mathematics, practical examples, and real-life scenarios. With its focus on real datasets and practical programming codes in Python and R, the book offers a comprehensive exploration of various topics. It presents novel research findings and provides valuable insights for researchers, practitioners, and actuarial students. The book strikes a balance between foundational concepts and advanced techniques, making it an invaluable reference for professionals in the field ... By redefining the landscape of financial data analytics in FinTech and InsurTech, this book establishes itself as a trusted guide in the industry." - Simon Lam, Fellow of SOA, CFA, FRM; President of The Actuarial Society of Hong Kong (2018, 2023); Deputy CEO &amp; General Manager, Munich Re (Hong Kong) "The book will certainly play an impactful role in the advancement of financial analytics and should be on the bookshelf of every serious student of the topic." - Wai Keung Li, Fellow of Am. Stat. Assoc. and Inst. Math. Stat.; Emeritus Professor, The University of Hong Kong; Dean, Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong "... The dual focus on theory and applications, together with the discussion on recent advancements of the fields, makes the book one of a kind, even field-defining, among books on similar topics, and an ideal resource for anyone interested in understanding and implementing statistical models in this era of big data, as well as for students preparing for professional examinations on data analytics, such as the SRM, PA and ATPA exams of the Society of Actuaries." - Ambrose Lo, Fellow of SOA, Chartered Enterprise Risk Analyst; Author of ACTEX Study Manual for SOA Exam SRM, ACTEX Study Manual for SOA Exam PA, and ACTEX Study Manual for SOA Exam ATPA "... Financial Data Analytics is one comprehensive biblical handbook for academic researchers, financial practitioners, and graduate students for both methodologies and applications. The book also lays a systematic framework for future extension and enrichment for financial data analytics." - Nai-pan Tang, Former Chief Risk Officer and Member of Executive Committee, Hang Seng Bank; Former Deputy CEO and Chief Risk Officer, Shanghai Commercial Bank Ltd.; Former Director of the Board, Deputy CEO, Alternative CEO, Chief Risk Officer, and Vice Chairman of Asset Management, China CITIC Bank International; Director, The Hong Kong Institute of Bankers (2019-2021); Professor of Practice, Department of Finance, Chinese University of Hong Kong

An essential introduction to data analytics and Machine Learning techniques in the business sector

In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs--especially of key results--and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves.

The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) &amp; Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems.

The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech.

After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction.

This book can help readers become well-equipped with the following skills:
* To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions
* To apply effective data dimension reduction tools to enhance supervised learning
* To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose

The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam.

Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Financial Data Analytics with Machine Learning, Optimization and Statistics

A practice-oriented and accessible introduction to geographical statistics In the newly revised Second Edition of Practical Statistics for Geographers and Earth Scientists, distinguished researcher Nigel Walford delivers an authoritative and easy-to-follow introduction to the principles and applications of statistical analysis in a geographical context. The book assists students in the development of competence in the statistical procedures necessary to conduct independent investigations, field-work, and related geographical research projects. The book explains statistical techniques relevant to geographical, geospatial, earth, and environmental data. It employs graphics and mathematical notation for maximum clarity. Guidance is provided on how to formulate research questions to ensure that the correct data is collected for the chosen analysis method. This new edition incorporates a new section on exploratory spatial analysis and spatial statistics. It also offers: <ul><li>A thorough introduction to first principles in the statistical analysis of geographical data, including discussions of the quality, content, collection, and acquisition of geographical data</li> <li>In-depth treatments of geographical data exploration, including the taking of statistical measures or quantities, as well as frequency distributions, probability, and hypotheses</li> <li>Comprehensive explorations of testing times, including parametric and nonparametric tests</li> <li>Insightful discussions of relationship investigation, including correlation and regression, as well as the spatial aspects of geographical data</li></ul> Perfect for undergraduates pursuing a degree in geography, Practical Statistics for Geographers and Earth Scientists will also be a valuable tool for students in other earth and environmental sciences.

A practice-oriented and accessible introduction to geographical statistics

In the newly revised Second Edition of Practical Statistics for Geographers and Earth Scientists, distinguished researcher Nigel Walford delivers an authoritative and easy-to-follow introduction to the principles and applications of statistical analysis in a geographical context. The book assists students in the development of competence in the statistical procedures necessary to conduct independent investigations, field-work, and related geographical research projects.

The book explains statistical techniques relevant to geographical, geospatial, earth, and environmental data. It employs graphics and mathematical notation for maximum clarity. Guidance is provided on how to formulate research questions to ensure that the correct data is collected for the chosen analysis method.

This new edition incorporates a new section on exploratory spatial analysis and spatial statistics. It also offers:
* A thorough introduction to first principles in the statistical analysis of geographical data, including discussions of the quality, content, collection, and acquisition of geographical data
* In-depth treatments of geographical data exploration, including the taking of statistical measures or quantities, as well as frequency distributions, probability, and hypotheses
* Comprehensive explorations of testing times, including parametric and nonparametric tests
* Insightful discussions of relationship investigation, including correlation and regression, as well as the spatial aspects of geographical data

Perfect for undergraduates pursuing a degree in geography, Practical Statistics for Geographers and Earth Scientists will also be a valuable tool for students in other earth and environmental sciences.

Practical Statistics for Geographers and Earth Scientists

Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

Unlocking the Power of Data, International Adaptation

Statistics

Eine Kultur des Genug aus ökonomischer und buddhistischer Sicht - Das Plädoyer für die Überwindung der "Gier-Wirtschaft"

All you need is less

Ein neuer Blick auf unsere Gesellschaft und die Welt!
 
 Zahlen, Fakten und Statistiken sind im Normalfall langweilig und manchmal schwer zu verstehen. Dies gilt besonders für Wirtschaftsstatistiken, die oft mehr verschleiern als erhellen. Oder in sich geschlossene Systeme darstellen, also lediglich Äpfel mit Äpfeln vergleichen - am Besten noch von der gleichen Sorte und vom gleichen Produzenten.
 Tritt man aber einen Schritt zurück und schaut über den Tellerrand, dann entdeckt man wahnwitzige Zahlen, die mehr über unsere Gesellschaft und die Welt in der wir leben aussagen, als langwierige Analysen es vermögen.
 Die Zahlenvergleiche, die Anabelle Körbel recherchiert und zusammengestellt hat, sind oft überraschend, manchmal verblüffend und absurd. In ihrer Gesamtheit zeichnen sie das Bild einer Welt, in der sich finanzieller Größenwahn und seine skurrilen Auswirkungen einen festen Platz gesichert haben.
 Größenwahn in Zahlen liefert einerseits unterhaltsame Fakten zum Weitererzählen, regt aber auch zum Nachdenken über die Unverhältnismässigkeiten dieser Welt an.

Ein neuer Blick auf unsere Gesellschaft und die Welt!Zahlen, Fakten und Statistiken sind im Normalfall langweilig und manchmal schwer zu verstehen. Dies gilt besonders für Wirtschaftsstatistiken, die oft mehr verschleiern als erhellen. Oder in sich geschlossene Systeme darstellen, also lediglich Äpfel mit Äpfeln vergleichen – am Besten noch von der gleichen Sorte und vom gleichen Produzenten.Tritt man aber einen Schritt zurück und schaut über den Tellerrand, dann entdeckt man wahnwitzige Zahlen, die mehr über unsere Gesellschaft und die Welt in der wir leben aussagen, als langwierige Analysen es vermögen.Die Zahlenvergleiche, die Anabelle Körbel recherchiert und zusammengestellt hat, sind oft überraschend, manchmal verblüffend und absurd. In ihrer Gesamtheit zeichnen sie das Bild einer Welt, in der sich finanzieller Größenwahn und seine skurrilen Auswirkungen einen festen Platz gesichert haben.Größenwahn in Zahlen liefert einerseits unterhaltsame Fakten zum Weitererzählen, regt aber auch zum Nachdenken über die Unverhältnismässigkeiten dieser Welt an.

199 Fakten, die deinen Blick auf die Welt verändern

Größenwahn in Zahlen

Wiley Series in Probability and Statistics

This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R

This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R

Concepts and Applications

Statistical Planning and Inference

Séparée

selbstbewusst und sinnlich

Séparée No. 45

Wiley and SAS Business Series

Unveil the future of marketing with data science as your guide, transforming insights into action and theory into practice

In an era where data reigns supreme, Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers emerges as the quintessential guide for navigating the complex terrain of digital marketing analytics. Authored by Dr. Iain Brown, a leader in the field of data science, this comprehensive tome is meticulously crafted for students, professionals, and data scientists eager to unlock the transformative power of data in marketing. Through a unique blend of theoretical insights and practical applications, Brown demystifies the science behind data-driven marketing strategies that resonate in today's digital-first world.

With chapters that traverse from the fundamentals of marketing data science to the cutting edge of generative AI, this guide is replete with real-world examples, hands-on exercises, and actionable insights. Readers are equipped to master the art of data collection, analysis, and application, paving the way for innovative marketing solutions that captivate and convert. Whether it's through predictive analytics, NLP, or customer journey analytics, Brown's expertise illuminates the path to leveraging data for strategic advantage.

Mastering Marketing Data Science is more than a book; it's a roadmap to the future of marketing. Dr. Brown invites readers to embark on a journey of discovery and transformation, where data science becomes the cornerstone of marketing excellence. Prepare to be empowered with knowledge that not only enlightens but also enables the application of data science principles to real-world marketing challenges, driving unparalleled business value and innovation.

Praise for MASTERING MARKETING DATA SCIENCE

"This book is an indispensable resource for marketers seeking to harness the power of data science to drive innovation, enhance customer engagement, and achieve competitive advantage in today's digital landscape. A must-read for both seasoned professionals and those aspiring to transform their marketing strategies through data science."
-- Bernard Marr, Bestselling Author and International Keynote Speaker on Business and Technology

"This is an outstanding and timely book on Mastering Marketing Data Science as it provides a unique blend of foundational as well as emerging topics."
-- Prof. dr. Bart Baesens, Professor KU Leuven, Lecturer Southampton Business School

"Mastering Marketing Data Science redefines the landscape of modern marketing, offering a compelling roadmap for harnessing the power of data science."
-- Professor Ganna Pogrebna, Lead for Behavioural Data Science at the Alan Turing Institute (UK); Executive Director at AI and Cyber Futures Institute and Honorary Professor at the University of Sydney Business School (Australia)

Unlock the Power of Data: Transform Your Marketing Strategies with Data Science

In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively.
* Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science.
* Practical Applications: Engage with real-world examples, hands-on exercises in both Python &amp; SAS, and actionable insights to apply in your marketing campaigns.
* Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science.
* Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape.
* Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative.

Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.

A Comprehensive Guide for Today's Marketers

Mastering Marketing Data Science

Wir leben in einer Welt der Daten. Daten allein aber sind wertlos, wenn wir nicht in der Lage sind, aus ihnen Informationen zu gewinnen. Um Informationen aus Daten zu extrahieren, sind Methoden der multivariaten Datenanalyse unerlässlich. Dieses Buch bietet eine leicht verständliche Einführung in die wichtigsten Methoden der multivariaten Datenanalyse. Es ist anwendungsorientiert, erfordert nur wenige Kenntnisse in Mathematik und Statistik, demonstriert die Verfahren mit numerischen Beispielen und veranschaulicht jede Methode anhand eines ausführlichen Fallbeispiels. Für Interessierte werden im Einführungskapitel für alle Verfahren relevante statistische Grundlagen aufgefrischt. Für die 18. Auflage wurden alle Kapitel überprüft und mit der aktuellen Version von IBM SPSS gerechnet.&nbsp;Der Inhalt Einführung in die empirische Datenanalyse – Regressionsanalyse – Varianzanalyse&nbsp;–&nbsp;Diskriminanzanalyse – Logistische Regression – Kontingenzanalyse – Faktorenanalyse&nbsp;–&nbsp;Clusteranalyse – Conjoint-AnalyseDas Buch wurde 2015 vom Berufsverband Deutscher Markt- und Sozialforscher (BVM) als das Lehrbuch ausgezeichnet, das die deutsche Marktforschungspraxis in den letzten Jahrzehnten nachhaltig geprägt hat. Es ist auch in Englisch und Chinesisch erschienen. Auf der Webseite www.multivariate.de werden weitere Materialien (bspw. Excel-Beispiele, R Code) angeboten, durch die sich die Verfahren noch besser erschließen und vertiefen lassen. Interaktive Flashcards helfen den eigenen Lernfortschritt zu kontrollieren. Mit der Springer Nature Flashcards-App können Sie exklusive Inhalte nutzen und Ihr Wissen testen.&nbsp;Die Autoren((author fotos))

Eine anwendungsorientierte Einführung

Multivariate Analysemethoden

Angewandte Statistik

Bibliografische Angaben

Schlagworte

Bibliografische Angaben

Schlagworte

Weitere Titel zum Thema