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Starnes D: The Practice of Statistics
for the AP® Course

The Practice of Statistics for the AP® Course

De Gruyter Studium

Die Wahrscheinlichkeitstheorie gehört zu den Kerndisziplinen der modernen Mathematikausbildung. Sie ist die Grundlage für alle Modelle, die "Risiko" und "Unsicherheit" einbeziehen. Dieses Lehrbuch gibt einen direkten, verlässlichen und modernen Zugang zu den wichtigsten Ergebnissen der mathematischen Wahrscheinlichkeitstheorie. Aufbauend auf dem Band "Maß &amp; Integral" werden zunächst elementare Fragen Wahrscheinlichkeitsverteilungen, Zufallsvariable, Unabhängigkeit, bedingte Wahrscheinlichkeiten und charakteristische Funktionen - bis hin zu einfachen Grenzwertsätzen behandelt. Diese Themen werden dann um das Studium von Summen unabhängiger Zufallsvariablen - Gesetze der Großen Zahlen, Null-Eins-Gesetze, random walks, zentraler Grenzwertsatz von Lindeberg-Feller - ergänzt. Allgemeine bedingte Erwartungen, Anwendungen von charakteristischen Funktionen und eine Einführung in die Theorie unendlich teilbarer Verteilungen und der großen Abweichungen runden die Darstellung ab. In gleicher Ausstattung erscheint der Folgeband "Martingale &amp; Prozesse". Lösungen zu den im Buch befindlichen Übungsaufgaben unter: http://www.motapa.de/stoch/index.shtml

Stochastik: von Abweichungen bis Zufall

Wahrscheinlichkeit

Air Transportation Industry considers the influence of political, legal, economic, social, and technological factors on the developments in the industry. It provides a brief historical background of the air transport industry, the determinants of the changes in the airline business, and adaptation processes that resulted in the evolution of business models and structural changes in the industry.Utilizing a unique database containing the characteristics of more than 16,000 air carriers worldwide, the book discusses key findings related to changes in the transport capacity of airlines operating at different points in time, including the length of the lifecycle and reasons for termination of activity, types, and geographical scope of operations. It also explores the impacts of global and regional-scale legal regulations.The book will interest air transportation and airport operations researchers. It can also serve as a reference for management and operations transportation students in logistics, air transportation, and economics courses.

History and Developments

Air Transportation Industry

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

Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author's knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.

A GUIded Toolkit using R

Introduction to Bayesian Econometrics

Wiley Series in Probability and Statistics

The definitive resource for ensuring diagnostic tests meet the highest standards of statistical rigor and clinical effectiveness Statistical Methods in Diagnostic Medicine, 3rd Edition by Xiao-Hua Zhou, Jiarui Sun, Gene A. Pennello, Nancy A. Obuchowski and Donna K. McClish delivers the most comprehensive treatment of statistical methodologies for diagnostic test evaluation available today. The authors of the 2nd Edition - Peking University PKU Distinguished Chair Professor Zhou, Cleveland Clinic Professor Obuchowski, and Virginia Commonwealth University Professor Donna McClish - team with U.S. Food and Drug Administration senior mathematical statistician Pennello and doctoral researcher Sun to address a critical challenge facing medical professionals: ensuring that diagnostic tests used in clinical practice are accurate, methodologically sound, free from bias, and effective. This edition provides practitioners and researchers with the statistical foundation necessary to design, analyze, and validate diagnostic studies that can withstand regulatory scrutiny and clinical demands. The book has been thoroughly revised to incorporate the latest advances in diagnostic test methodology, featuring significant expansions in biomarker evaluation and benefit-risk assessment. The authors have restructured content to improve cohesion through integrated case studies that span multiple chapters, while updating each section with contemporary methods and streamlining discussions of older techniques to focus on the most relevant approaches for today's diagnostic challenges. Readers will also find: <ul> <li>Three entirely new chapters covering statistical methods for risk prediction, quantitative imaging biomarkers, and efficacy and effectiveness of biomarkers and other tests.</li> <li>Enhanced coverage of sample size calculations, accuracy estimation methods, and comparative analysis techniques for competing diagnostic tests</li> <li>Advanced analytical approaches including methods for comparing correlated ROC curves in multi-reader studies and techniques for correcting verification bias</li> <li>Comprehensive treatment of regression analysis applications in diagnostic accuracy research with updated methodological guidance</li> <li>Integrated case studies that demonstrate real-world application of statistical methods across different diagnostic scenarios and study designs</li> </ul> Perfect for biostatisticians, applied statisticians, clinical researchers, and regulatory professionals working in diagnostic medicine, Statistical Methods in Diagnostic Medicine will also benefit graduate students and researchers interested in gaining the statistical expertise needed to design robust diagnostic studies.

Statistical Methods in Diagnostic Medicine

Unlock the full potential of quantitative trading with Prescriptions for Quant Traders Using R-a practical, hands-on guide for turning data into trading decisions.This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. Organized into ten structured parts, it delivers step-by-step "prescriptions" using R-script-driven tasks that solve real-world trading problems. Each prescription is designed to be immediately actionable, so readers can move from concept to implementation without guesswork.What makes this book different is its clarity, utility, and its link to online video walk-throughs of R scripts. The text flows seamlessly without interruptions, yet a comprehensive bibliography ensures depth and rigor. Every chapter blends explanation with executable code, enabling traders to:<ul> <li> Design systematic trading strategies in R - and understand why they work.</li> <li> Apply robust statistical and econometric methods to financial data for better predictions.</li> <li> Evaluate risk and performance metrics to refine strategies with confidence.</li> <li> Automate workflows so insights move faster from model to market.</li> </ul>Whether you are a retail trader aiming to compete with professionals, a financial analyst seeking sharper models, or a data scientist expanding into trading, this book bridges the gap between theory and practice.Readers will find special value in the prescription-based approach. Just as a doctor prescribes remedies, these scripts deliver targeted solutions to trading challenges-from portfolio optimization and volatility forecasting to Bayesian inference and machine learning integration.In a marketplace where retail participation continues to grow, Prescriptions for Quant Traders Using R equips you with the knowledge, tools, and confidence to approach trading systematically. The result: deeper insights, stronger discipline, and strategies that stand the test of real-world markets.Whether you're coding your first model or refining a sophisticated strategy, this book provides the prescriptions you need to succeed.

Videos & Scripts

Prescriptions for Quant Traders Using R

Operations Research Series

This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out. The textbook targets engineering students and emphasizes the use of operations research models and solution methods important in the design, control, operation, and management of global supply chains.Completely updated, Supply Chain Engineering: Models and Applications, Second Edition stresses quantitative models and methods, highlights global supplier selection and vendor risk management techniques, and discusses the use of multiple criteria decision-making models in supply chain management. The new edition includes chapters on health and humanitarian supply chains, including disaster management and logistics modeling, and on warehousing and distribution. Disruptions to global supply chains due to the COVID-19 pandemic are discussed throughout the book. Industry and government strategies to make the global supply chains resilient are also presented. Thirty four case studies have been included to illustrate various supply chain models and methods. Exercises are included at the end of each chapter, and a solutions manual and PowerPoint slides are available for qualified textbook adoptions.The new edition continues to target upper-level undergraduate and graduate students in engineering, as well as MBA students in operations management, logistics, and supply chain management programs that emphasize quantitative analysis. It is also useful as a reference for technical professionals and researchers in industrial engineering, supply chain management, procurement, logistics and health administration.

Models and Applications

Supply Chain Engineering

Routledge Revivals

Distribution Theory

Probability and Statistics for Engineering and the Sciences, Loose-Leaf Version

New Machine Learning Methods for the Sustainable Development Goals

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

Pelican Books

'Probably the UK's greatest living statistician' TelegraphFrom the UK's 'statistical national treasure', a clever and data-driven guide to how we can live with risk and uncertaintyWe live in a world where uncertainty is inevitable. How should we deal with what we don't know? And what role do chance, luck and coincidence play in our lives?David Spiegelhalter has spent his career dissecting data in order to understand risks and assess the chances of what might happen in the future. In The Art of Uncertainty, he gives readers a window onto how we can all do this better.In engaging, crystal-clear prose, he takes us through the principles of probability, showing how it can help us think more analytically about everything from medical advice to pandemics and climate change forecasts, and explores how we can update our beliefs about the future in the face of constantly changing experience. Along the way, he explains why roughly 40% of football results come down to luck rather than talent, how the National Risk Register assesses near-term risks to the United Kingdom, and why we can be so confident that two properly shuffled packs of cards have never, ever been in the exact same order.Drawing on a wide range of captivating real-world examples, this is an essential guide to navigating uncertainty while also having the humility to admit what we do not know

How to Navigate Chance, Ignorance, Risk and Luck

The Art of Uncertainty

The Practice of Statistics for the AP® Course

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