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

The Practice of Statistics for the AP® Course

Routledge Advanced Texts in Economics and Finance

Big Data analytics have emerged as a powerful tool, transforming the global financial market by enabling more informed decision-making, enhancing predictive capabilities and improving risk management. By leveraging vast amounts of data, advanced analytics can provide real-time insights into market trends, forecast potential downturns and identify investment opportunities. This innovative book is one of the first advanced textbooks to address the rapidly evolving field of Big Data in finance.Drawing on the author's extensive research, consulting and teaching experience, it applies advanced theoretical and empirical methods to massive high-frequency databases to explore important areas of finance, including market efficiency, equities, fixed income securities, options and market microstructure. The book begins with an introduction to risk and uncertainty before exploring applications of advanced analytics to daily data, as well as applications to big data with intraday data. It concludes with suggestions for future work. Raw databases, SAS software code and intermediate and output data files are made available to enable readers to work with real data and perform their own analyses.Advanced Analytics in Financial Markets is the ideal textbook for advanced undergraduate and graduate courses in finance, big data and advanced analytics, as well as a reference book for scholars and practitioners.

Advanced Analytics in Financial Markets

From bar bets to Nobel Prizes, viral outbreaks to lottery wins, how statistics shapes our choices, reveals hidden truths, and guides us toward certainty in uncertain times. We live in an uncertain world. Morning commutes, natural disasters, global pandemics-our lives are riddled with events whose outcomes we can never know with certainty. In What Are the Odds? Mark Prell reveals how statistical thinking empowers us to navigate this uncertainty with clarity and confidence. Whether weighing the benefits of different medications or deciding if home insurance is worth the steep cost, Prell shows that just as important as asking what the data says is asking how reliable it is. Describing the core concepts and methods of statistical thinking, he teaches us how to extract meaningful information from raw data, and crucially, to recognize data that's been cherry-picked, fabricated, or is simply wrong. As we ask if survey responses are truthful, estimate the number of deaths from the Covid pandemic, and plan for retirement, we develop a skill set to test and update our assumptions, to make more accurate forecasts, and above all, to assess the certainty of our conclusions. Through unforgettable stories of statistical ingenuity, Prell demonstrates that the underlying math can be intuitive and even fun. And as he turns us all into statisticians, he reminds us that statistics is about more than manipulating data-it's about joining a community of statisticians committed to truth and integrity.

A Statistical Guide to Certainty in an Uncertain World

What Are the Odds?

Artificial Intelligence in Smart Healthcare Systems

Offering a holistic view of the pioneering trends and innovations in smart healthcare management, this book focuses on the methodologies, frameworks, design issues, tools, architectures, and technologies necessary to develop and understand intelligent healthcare systems and emerging applications in the present era. Smart Technologies in Healthcare Management: Pioneering Trends and Applications provides an overview of various technical and innovative aspects, challenges, and issues in smart healthcare, along with recent and novel findings. It highlights the latest advancements and applications in the field of intelligent systems and explores the importance of cloud computing and the design of sensors in an IoT system. The book offers algorithms and a framework with models in machine learning and AI for smart healthcare management. A detailed flow chart and innovative and modified methodologies related to intelligent computing in healthcare are discussed, as well as real-world-based examples so that readers can compare technical concepts with daily life concepts. This book will be a useful reference for academicians and the healthcare industry, along with professionals interested in exploring innovations in varied applicational areas of AI, IoT, and machine learning. Researchers, startup companies, and entrepreneurs will also find this book of interest.

Pioneering Trends and Applications

Smart Technologies in Healthcare Management

Routledge-Giappichelli Studies in Business and Management

From Basic Growth Models to Computational Methods

Dynamic Programming for Economists

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

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

Derivatives: Mathematical Foundations for Finance Students is written for students of finance and actuarial science who want to understand derivatives not just as formulas but as ideas that make sense. The goal is to build the mathematical foundations up step by step - starting from the familiar territory of first-year undergraduate probability and statistics and moving all the way to stochastic calculus and the principles of risk-neutral pricing, which form the heart of modern financial mathematics.This text will enable the reader to:¿ understand models of asset price behavior and apply stochastic calculus ¿ use stochastic simulations in R to investigate processes and price options ¿ price options with the binomial model ¿ derive solutions to the Black-Scholes equation using risk-neutral pricing ¿ price interest-rate derivatives using Black's model, short-rate models and the Heath-Jarrow-Morton (HJM) framework ¿ tackle quantitative finance exam questions.It incorporates a range of learning features to aid student understanding, including boxed examples, end-of-chapter summaries, selected questions from Society of Actuaries (US) Quantitative Finance examinations and further reading suggestions. The book is also supported by a suite of digital learning resources, including PowerPoint slides, multiple choice questions, instructor manual/advice document for lecturers and a test bank.This book will appeal to both undergraduate and postgraduate students who wish to understand the principles of stochastic calculus and option pricing.

Mathematical Foundations for Finance Students

Derivatives

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

Routledge Revivals

Distribution Theory

Statistical Design and Inference for the Social Sciences&nbsp;goes beyond the math to teach students how to use data to support meaningful, causal arguments.

Statistical Design and Inference for the Social Sciences

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows. This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects. <ul><li>Conduct exploratory analysis of data to improve quality and model outcomes</li> <li>Apply sampling and experimental design to reduce bias and answer questions with clarity</li> <li>Use regression to understand data-generating processes and detect anomalies</li> <li>Build predictive models using classification, clustering, and unsupervised learning with unbalanced data</li> </ul>

50+ Essential Concepts Using R and Python

AI-Assisted Statistics for Data Scientists

The Practice of Statistics for the AP® Course

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