Starting from simple generalizations of factorials and binomial coefficients, this book gives a friendly and accessible introduction to q q-analysis, a subject consisting primarily of identities between certain kinds of series and products. Many applications of these identities to combinatorics and number theory are developed in detail. There are numerous exercises to help students appreciate the beauty and power of the ideas, and the history of the subject is kept consistently in view. The book has few prerequisites beyond calculus. It is well suited to a capstone course, or for self-study in combinatorics or classical analysis. Ph.D. students and research mathematicians will also find it useful as a reference.

"This book offers a hands-on introduction and teaching resource for students, users, and teachers of Qualitative Comparative Analysis (QCA; Ragin, 1987, 2000, 2008b). Given its superior ability to model certain aspects of complexity, QCA has made inroads into virtually every social science discipline and beyond. Software solutions for QCA have also been developing at a fast pace. This book seeks to reduce the time and effort required when we first encounter the logic of not just a new method but also new software. It offers a genuinely simple, intuitive, and hands-on resource for implementing the state-of-the-art protocol of QCA using R, the most advanced software environment for QCA. Our book has an applied and practical focus"--

In May 1984 the Swedish Council for Scientific Research convened a small group of investigators at the scientific research station at Abisko, Sweden, for the purpose of examining various conceptual and mathematical views of the evolution of complex systems. The stated theme of the meeting was deliberately kept vague, with only the purpose of discussing alternative mathematically based approaches to the modeling of evolving processes being given as a guideline to the participants. In order to limit the scope to some degree, it was decided to emphasize living rather than nonliving processes and to invite participants from a range of disciplinary specialities spanning the spectrum from pure and applied mathematics to geography and analytic philosophy. The results of the meeting were quite extraordinary; while there was no intent to focus the papers and discussion into predefined channels, an immediate self-organizing effect took place and the deliberations quickly oriented themselves into three main streams: conceptual and formal structures for characterizing sys tem complexity; evolutionary processes in biology and ecology; the emergence of complexity through evolution in natural lan guages. The chapters presented in this volume are not the proceed ings of the meeting. Following the meeting, the organizers felt that the ideas and spirit of the gathering should be preserved in some written form, so the participants were each requested to produce a chapter, explicating the views they presented at Abisko, written specifically for this volume. The results of this exercise form the volume you hold in your hand.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

An Introduction to Mathematical Analysis provides detailed explanations and exhaustive proofs, and follows an axiomatic approach to presenting the material. The text assumes that the student has little background in mathematical analysis; therefore, the initial pace is slowed down. The proofs are formal, complete, and augmented by an informal and heuristic explanation. The author presents the subject in clear and evocative language, and includes treatment of the Lebesgue integral, a topic not usually found in texts of this level. Mathematical problems are included throughout the text and are designed to get the student involved at every stage. Key Features: * All the information introduced is proved by axioms * Extensive proofs are formal and complete * Includes a novel treatment of the Lebesgue Integral * Emphasis on developing proofs helps students acquire skills essential to subsequent courses