Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. "Try This!" exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.

Applications of Group Theory to Combinatorics contains 11 survey papers from international experts in combinatorics, group theory and combinatorial topology. The contributions cover topics from quite a diverse spectrum, such as design theory, Belyi functions, group theory, transitive graphs, regular maps, and Hurwitz problems, and present the state-of-the-art in these areas. Applications of Group Theory to Combinatorics will be useful in the study of graphs, maps and polytopes having maximal symmetry, and is aimed at researchers in the areas of group theory and combinatorics, graduate students in mathematics, and other specialists who use group theory and combinatorics. Jack Koolen teaches at the Department of Mathematics at Pohang University of Science and Technology, Korea. His main research interests include the interaction of geometry, linear algebra and combinatorics, on which he published 60 papers. Jin Ho Kwak is Professor at the Department of Mathematics at Pohang University of Science and Technology, Korea, where he is director of the Combinatorial and Computational Mathematics Center (Com2MaC). He works on combinatorial topology, mainly on covering enumeration related to Hurwitz problems and regular maps on surfaces, and published more than 100 papers in these areas. Ming-Yao Xu is Professor in Department of Mathematics at Peking University, China. The focus in his research is in finite group theory and algebraic graph theory. Ming-Yao Xu published over 80 papers on these topics.

Despite the obvious importance of measurement in any scientific endeavor, few students of the social sciences receive adequate training in the principles and problems of assigning numerical values to the subjects, objects, events, groups and operations they study, and still less in the process of translating theoretical ideas and concepts into variables. This kind of casualness with respect to measurement is often in marked contrast to their methodically designed research, which has grown out of subtle and sophisticated theoretical consideration.

Scaling is intended to remedy this deficiency by providing a broad and detailed description of the major processes for developing measurement scales. The chapters, which include both classics in the field and the best of modern work, require no great mathematical sophistication, and go well beyond the conventional study of attitudes to the more general uses of scaling. They enable the student and researcher to examine the development of measures of scalability and the problems and weaknesses they present, to become familiar with the development of tests of significance for reproducibility and scalability and the need for them, and to examine the lively history of the subject and experience the excitement that can be secured from sharing with a creative author the first report of his insight.

Part One presents a series of general articles that deal in philosophic terms with the problem of measurement, with what is meant by measurement and scaling as well as the notions underlying the process of measuring. Part Two deals with the scaling methods developed by L. L. Thurstone, including paired comparison scaling, equal-appearing interval scaling, and successive interval scaling. The third part focuses upon scalogram analysis, presenting the background, rationale and procedures for Guttman scaling. The fourth part is concerned with summated rating, or Likert scaling. Part Five is a consideration of unfolding theory and methods. Part Six is made up of articles that focus on various special cases and problems relevant to scaling. The book also contains an unusually full reference bibliography and a set of convenient reference tables associated with the development and use of measurement scales.

This alternative textbook for courses on teaching mathematics asks teachers and prospective teachers to reflect on their relationships with mathematics and how these relationships influence their teaching and the experiences of their students. Applicable to all levels of schooling, the book covers basic topics such as planning and assessment, classroom management, and organization of classroom experiences; it also introduces some novel approaches to teaching mathematics, such as psychoanalytic perspectives and post-modern conceptions of curriculum. Traditional methods-of-teaching issues are recast in a new discourse, provoking new ideas for making mathematics education meaningful to teachers as well as their students. Co-authored by a professor and coordinator of mathematics education programs, with several practicing elementary, middle and high school mathematics, a unique aspect of this book is that it is a collaboration of teachers across all pre-college grade levels, making it ideal for discussion groups that include teachers at any level. Embracing Mathematics: integrates pedagogy and content exploration in ways that are unique in mathematics education features textboxes with reflection questions and suggested explorations that can be easily utilized as homework for a course or as discussion opportunities for teacher reading groups offers examples of teachers' action research projects that grew out of their interactions with the main chapters in the book is not narrowly limited to mathematics education but incorporates curriculum studies - an invaluable asset that allows instructors to find more ways to engage students in self-reflexive acts of teaching Embracing Mathematics bookis intended as a method text for undergraduate and master's-level mathematics education courses and more specialized graduate courses on mathematics education, and as a resource for teacher discussion groups.

* Contains two introductory chapters on how to set up an R environment and do basic imports/manipulation of meta-analysis data, including exercises. * Describes statistical concepts clearly and concisely before applying them in R. * Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book.

Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explores Bayesian principles and illustrates their application to healthcare research. Building on the basics of classic biostatistics and algebra, this easy-to-read book provides a clear overview of the subject. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles. The author also elaborates on the current controversies between Bayesian and frequentist biostatisticians. The book concludes with recommendations for Bayesians to improve their standing in the clinical trials community. Calculus derivations are relegated to the appendices so as not to overly complicate the main text. As Bayesian methods gain more acceptance in healthcare, it is necessary for clinical scientists to understand Bayesian principles. Applying Bayesian analyses to modern healthcare research issues, this lucid introduction helps readers make the correct choices in the development of clinical research programs.

Candlestick charts are often used in speculative markets to describe and forecast asset price movements. This book is the first of its kind to investigate candlestick charts and their statistical properties. It provides an empirical evaluation of candlestick forecasting. The book proposes a novel technique to obtain the statistical properties of candlestick charts. The technique, which is known as the range decomposition technique, shows how security price is approximately logged into two ranges, i.e. technical range and Parkinson range. Through decomposition-based modeling techniques and empirical datasets, the book investigates the power of, and establishes the statistical foundation of, candlestick forecasting.

Features: Includes over 104 codes in OOPs python, all of which can be used either as a standalone program or integrated with any other main program without any issues. Every parameter in the input, output and execution has been provided while keeping both beginner and advanced users in mind. The output of every program is explained thoroughly with detailed examples. A detailed mathematical commenting is done along side the code which enhances clarity about the flow and working of the code

Recent developments in multi-parametric optimization and control Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material. Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control--from the linear quadratic regulator over hybrid systems to periodic systems and robust control. The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems. Uses case studies to illustrate real-world applications for a better understanding of the concepts presented Covers the fundamentals of optimization and model predictive control Provides information on key topics, such as the basic sensitivity theorem, linear programming, quadratic programming, mixed-integer linear programming, optimal control of continuous systems, and multi-parametric optimal control An appendix summarizes the history of multi-parametric optimization algorithms. It also covers the use of the parametric optimization toolbox (POP), which is comprehensive software for efficiently solving multi-parametric programming problems.

This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations.

Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology.

The book opens with a review of the types of research questions the mediation model addresses. Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions.

Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate level research methods or statistics course is assumed. The overview of mediationanalysis and the guidelines for conducting a mediation analysis will be appreciated by all readers.

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.

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Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without sacrificing mathematical rigour, bridging the gap between the many excellent introductory books and the more advanced, graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while being respectful to the history of statistical inference. It seeks to provide a thorough grounding in both the theory and application of statistics, with even the more abstract parts placed in the context of a practical setting. Features: *Complete introduction to mathematical probability, random variables, and distribution theory. *Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes. *Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference. *Detailed introduction to Bayesian statistics and associated topics. *Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC). This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.

Although the spatial dimension of ecosystem dynamics is now widely recognized, the specific mechanisms behind species patterning in space are still poorly understood and the corresponding theoretical framework is underdeveloped. Going beyond the classical Turing scenario of pattern formation, Spatiotemporal Patterns in Ecology and Epidemiology: Theory, Models, and Simulation illustrates how mathematical modeling and numerical simulations can lead to greater understanding of these issues. It takes a unified approach to population dynamics and epidemiology by presenting several ecoepidemiological models where both the basic interspecies interactions of population dynamics and the impact of an infectious disease are explicitly considered. The book first describes relevant phenomena in ecology and epidemiology, provides examples of pattern formation in natural systems, and summarizes existing modeling approaches. The authors then explore nonspatial models of population dynamics and epidemiology. They present the main scenarios of spatial and spatiotemporal pattern formation in deterministic models of population dynamics. The book also addresses the interaction between deterministic and stochastic processes in ecosystem and epidemic dynamics, discusses the corresponding modeling approaches, and examines how noise and stochasticity affect pattern formation. Reviewing the significant progress made in understanding spatiotemporal patterning in ecological and epidemiological systems, this resource shows that mathematical modeling and numerical simulations are effective tools in the study of population ecology and epidemiology.

Edexcel and A Level Modular Mathematics C1 features: Student-friendly worked examples and solutions, leading up to a wealth of practice questions. Sample exam papers for thorough exam preparation. Regular review sections consolidate learning. Opportunities for stretch and challenge presented throughout the course. 'Escalator section' to step up from GCSE. PLUS Free LiveText CD-ROM, containing Solutionbank and Exam Cafe to support, motivate and inspire students to reach their potential for exam success. Solutionbank contains fully worked solutions with hints and tips for every question in the Student Books. Exam Cafe includes a revision planner and checklist as well as a fully worked examination-style paper with examiner commentary.

Spatializing Social Media charts the theoretical and methodological challenges in analyzing and visualizing social media data mapped to geographic areas. It introduces the reader to concepts, theories, and methods that sit at the crossroads between spatial and social network analysis to unpack the conceptual differences between online and face-to-face social networks and the nonlinear effects triggered by social activity that overlaps online and offline. The book is divided into four sections, with the first accounting for the differences between space (the geometrical arrangements that structure and enable forms of interaction) and place (the mechanisms through which social meanings are attached to physical locations). The second section covers the rationale of social network analysis and the ontological differences, stating that relationships, more than individual and independent attributes, are key to understanding of social behavior. The third section covers a range of case studies that successfully mapped social media activity to geographically situated areas and considers the inflection of homophilous dependencies across online and offline social networks. The fourth and last section of the book explores a range of networks and discusses methods for and approaches to plotting a social network graph onto a map, including the purpose-built R package Spatial Social Media. The book takes a non-mathematical approach to social networks and spatial statistics suitable for postgraduate students in sociology, psychology and the social sciences.

Who doesn't love a mystery? Students will have a blast reading the adventures of kid detectives Zara and Mendel and their dog, Digit, and helping them solve intriguing math puzzlers along the way. In this collection of comical mysteries, students meld reading strategies, such as text marking, with essential math skills to tackle real-world problems. A delightful way to practice whole-number computation, fractions, measurement, geometry, algebraic reasoning, and so much more!

The methodological needs of environmental studies are unique in the breadth of research questions that can be posed, calling for a textbook that covers a broad swath of approaches to conducting research with potentially many different kinds of evidence. Fully updated to address new developments such as the effects of the internet, recent trends in the use of computers, remote sensing, and large data sets, this new edition of Research Methods for Environmental Studies is written specifically for social science-based research into the environment. This revised edition contains new chapters on coding, focus groups, and an extended treatment of hypothesis testing. The textbook covers the best-practice research methods most used to study the environment and its connections to societal and economic activities and objectives. Over five key parts, Kanazawa introduces quantitative and qualitative approaches, mixed methods, and the special requirements of interdisciplinary research, emphasizing that methodological practice should be tailored to the specific needs of the project. Within these parts, detailed coverage is provided on key topics including the identification of a research project, hypothesis testing, spatial analysis, the case study method, ethnographic approaches, discourse analysis, mixed methods, survey and interview techniques, focus groups, and ethical issues in environmental research. Drawing on a variety of extended and updated examples to encourage problem-based learning and fully addressing the challenges associated with interdisciplinary investigation, this book will be an essential resource for students embarking on courses exploring research methods in environmental studies.

Keep your mind sharp all year long with Mathematics 2023: Your Daily Epsilon of Math, a 12" x 12" wall calendar featuring a new math problem every day and 12 beautiful math images! Let mathematicians Rebecca Rapoport and Dean Chung tickle the left side of your brain by providing you with a math challenge for every day of the year. The solution is always the date, but the fun lies in figuring out how to arrive at the answer, and possibly discovering more than one method of arriving there. Problems run the gamut from arithmetic through graduate level math. Some of the most tricky problems require only middle school math applied cleverly. With word problems, math puns, and interesting math definitions added into the mix, this calendar will intrigue you for the whole year. End the year with more brains than you had when it began with Mathematics 2023: Your Daily Epsilon of Math.

Routledge Library Editions: The City reprints some of the most important works in urban studies published in the last century. For further information on this collection please email [email protected].

City, Region and Regionalism was first published in 1947.

Do large cities grow more or less rapidly than small ones? Why should the relationship between city size and population growth vary so much from one period to another? This book studies the process of population growth in a national set of cities, relating its findings to the theoretical concepts of urban geography. To test his ideas, the author studies the growth of cities in England and Wales between 1801 and 1911. His explanations draw strongly on the connection between growth and the adoption of innovations. He develops a model of innovation diffusions in a set of cities and, in support of this model, looks at the way in which three particular innovations - the telephone, building societies and gaslighting - spread amongst English towns in the nineteenth century. This book was first published in 1973.

Hall argues that 'London was the chief manufacturing centre of the country in 1861, and without doubt for centuries before that'. This book looks at industries in London over time from 1861. This book was first published in 1962.

Larson IS student success. INTERMEDIATE ALGEBRA: ALGEBRA WITHIN REACH, 6E, International Edition owes its success to the hallmark features for which the Larson team is known: learning by example, a straightforward and accessible writing style, emphasis on visualization through the use of graphs to reinforce algebraic and numeric solutions and to interpret data, and comprehensive exercise sets. These pedagogical features are carefully coordinated to ensure that students are better able to make connections between mathematical concepts and understand the content. With a bright, appealing design, the new Sixth Edition builds on the Larson tradition of guided learning by incorporating a comprehensive range of student success materials to help develop students' proficiency and conceptual understanding of algebra. The text also continues coverage and integration of geometry in examples and exercises.