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Statistical Literacy: A Beginner′s Guide
by Rhys Christopher JonesIn an increasingly data-centric world, we all need to know how to read and interpret statistics. But where do we begin? This book breaks statistical terms and concepts down in a clear, straightforward way. From understanding what data are telling you to exploring the value of good storytelling with numbers, it equips you with the information and skills you need to become statistically literate. It also: Dispels misconceptions about the nature of statistics to help you avoid common traps. Helps you put your learning into practice with over 60 Tasks and Develop Your Skills activities. Draws on real-world research to demonstrate the messiness of data – and show you a path through it. Approachable and down to earth, this guide is aimed at undergraduates across the social sciences, psychology, business and beyond who want to engage confidently with quantitative methods or statistics. It forms a reassuring aid for anyone looking to understand the foundations of statistics before their course advances, or as a refresher on key content.
Statistical Literacy: A Beginner′s Guide
by Rhys Christopher JonesIn an increasingly data-centric world, we all need to know how to read and interpret statistics. But where do we begin? This book breaks statistical terms and concepts down in a clear, straightforward way. From understanding what data are telling you to exploring the value of good storytelling with numbers, it equips you with the information and skills you need to become statistically literate. It also: Dispels misconceptions about the nature of statistics to help you avoid common traps. Helps you put your learning into practice with over 60 Tasks and Develop Your Skills activities. Draws on real-world research to demonstrate the messiness of data – and show you a path through it. Approachable and down to earth, this guide is aimed at undergraduates across the social sciences, psychology, business and beyond who want to engage confidently with quantitative methods or statistics. It forms a reassuring aid for anyone looking to understand the foundations of statistics before their course advances, or as a refresher on key content.
Statistical Literacy at School: Growth and Goals (Studies in Mathematical Thinking and Learning Series)
by Jane M. WatsonThis book reveals the development of students' understanding of statistical literacy. It provides a way to "see" student thinking and gives readers a deeper sense of how students think about important statistical topics. Intended as a complement to curriculum documents and textbook series, it is consistent with the current principles and standards of the National Council of Teachers of Mathematics. The term "statistical literacy" is used to emphasize that the purpose of the school curriculum should not be to turn out statisticians but to prepare statistically literate school graduates who are prepared to participate in social decision making. Based on ten years of research--with reference to other significant research as appropriate--the book looks at students' thinking in relation to tasks based on sampling, graphical representations, averages, chance, beginning inference, and variation, which are essential to later work in formal statistics. For those students who do not proceed to formal study, as well as those who do, these concepts provide a basis for decision making or questioning when presented with claims based on data in societal settings. Statistical Literacy at School: Growth and Goals:*establishes an overall framework for statistical literacy in terms of both the links to specific school curricula and the wider appreciation of contexts within which chance and data-handling ideas are applied;*demonstrates, within this framework, that there are many connections among specific ideas and constructs;*provides tasks, adaptable for classroom or assessment use, that are appropriate for the goals of statistical literacy; *presents extensive examples of student performance on the tasks, illustrating hierarchies of achievement, to assist in monitoring gains and meeting the goals of statistical literacy; and*includes a summary of analysis of survey data that suggests a developmental hierarchy for students over the years of schooling with respect to the goal of statistical literacy.Statistical Literacy at School: Growth and Goals is directed to researchers, curriculum developers, professionals, and students in mathematics education as well those across the curriculum who are interested in students' cognitive development within the field; to teachers who want to focus on the concepts involved in statistical literacy without the use of formal statistical techniques; and to statisticians who are interested in the development of student understanding before students are exposed to the formal study of statistics.
Statistical Literacy for Clinical Practitioners
by William H. Holmes William C. RinamanThis textbook on statistics is written for students in medicine, epidemiology, and public health. It builds on the important role evidence-based medicine now plays in the clinical practice of physicians, physician assistants and allied health practitioners. By bringing research design and statistics to the fore, this book can integrate these skills into the curricula of professional programs. Students, particularly practitioners-in-training, will learn statistical skills that are required of today's clinicians. Practice problems at the end of each chapter and downloadable data sets provided by the authors ensure readers get practical experience that they can then apply to their own work.
Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)
by Richard GoldenThe recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
Statistical Machine Learning for Engineering with Applications (Lecture Notes in Statistics #227)
by Jürgen Franke Anita SchöbelThis book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.
Statistical Machine Translation
by Philipp KoehnThe filed of machine translation has recently been energized by the emergence of statistical techniques, which have brought the dream of automatic language translation closer to reality. This class-tested textbook, authored by an active researcher in the field, provides a gentle and accessible introduction to the latest methods and enables the reader to build machine translation systems for any language pair.
A Statistical Mechanical Interpretation of Algorithmic Information Theory (SpringerBriefs in Mathematical Physics #36)
by Kohtaro TadakiThis book is the first one that provides a solid bridge between algorithmic information theory and statistical mechanics. Algorithmic information theory (AIT) is a theory of program size and recently is also known as algorithmic randomness. AIT provides a framework for characterizing the notion of randomness for an individual object and for studying it closely and comprehensively. In this book, a statistical mechanical interpretation of AIT is introduced while explaining the basic notions and results of AIT to the reader who has an acquaintance with an elementary theory of computation.A simplification of the setting of AIT is the noiseless source coding in information theory. First, in the book, a statistical mechanical interpretation of the noiseless source coding scheme is introduced. It can be seen that the notions in statistical mechanics such as entropy, temperature, and thermal equilibrium are translated into the context of noiseless source coding in a natural manner. Then, the framework of AIT is introduced. On this basis, the introduction of a statistical mechanical interpretation of AIT is begun. Namely, the notion of thermodynamic quantities, such as free energy, energy, and entropy, is introduced into AIT. In the interpretation, the temperature is shown to be equal to the partial randomness of the values of all these thermodynamic quantities, where the notion of partial randomness is a stronger representation of the compression rate measured by means of program-size complexity. Additionally, it is demonstrated that this situation holds for the temperature itself as a thermodynamic quantity. That is, for each of all the thermodynamic quantities above, the computability of its value at temperature T gives a sufficient condition for T to be a fixed point on partial randomness.In this groundbreaking book, the current status of the interpretation from both mathematical and physical points of view is reported. For example, a total statistical mechanical interpretation of AIT that actualizes a perfect correspondence to normal statistical mechanics can be developed by identifying a microcanonical ensemble in the framework of AIT. As a result, the statistical mechanical meaning of the thermodynamic quantities of AIT is clarified. In the book, the close relationship of the interpretation to Landauer's principle is pointed out.
Statistical Mechanics: An Introductory Graduate Course (Graduate Texts in Physics)
by A. J. Berlinsky A. B. HarrisIn a comprehensive treatment of Statistical Mechanics from thermodynamics through the renormalization group, this book serves as the core text for a full-year graduate course in statistical mechanics at either the Masters or Ph.D. level. Each chapter contains numerous exercises, and several chapters treat special topics which can be used as the basis for student projects. The concept of scaling is introduced early and used extensively throughout the text. At the heart of the book is an extensive treatment of mean field theory, from the simplest decoupling approach, through the density matrix formalism, to self-consistent classical and quantum field theory as well as exact solutions on the Cayley tree. Proceeding beyond mean field theory, the book discusses exact mappings involving Potts models, percolation, self-avoiding walks and quenched randomness, connecting various athermal and thermal models. Computational methods such as series expansions and Monte Carlo simulations are discussed, along with exact solutions to the 1D quantum and 2D classical Ising models. The renormalization group formalism is developed, starting from real-space RG and proceeding through a detailed treatment of Wilson’s epsilon expansion. Finally the subject of Kosterlitz-Thouless systems is introduced from a historical perspective and then treated by methods due to Anderson, Kosterlitz, Thouless and Young. Altogether, this comprehensive, up-to-date, and engaging text offers an ideal package for advanced undergraduate or graduate courses or for use in self study.
Statistical Mechanics: A Concise Advanced Textbook (UNITEXT for Physics)
by Sergio CecottiThis textbook is based on lecture notes that the author delivered at Qiuzhen College (Tsinghua University), a Chinese institution known for its exceptionally talented mathematics students. The book's intended audience shapes its character. It introduces Statistical Mechanics from the ground up, offering a fully self-contained presentation that aims for mathematical precision. It distinguishes rigorous results from controlled approximations and provides physical insights into phenomena. Despite its concise nature (suited for a one-semester basic course), this book covers several topics typically not found in introductory texts. These include Shannon's information-theoretic interpretation of entropy, the gauge approach to order-disorder duality in the Ising model, the Yang-Lee theory, and the quantum dissipation-fluctuation theorem. Additionally, it explores frustrated and quenched systems, including an introduction to the celebrated Parisi solution of the Sherrington-Kirkpatrick model of spin glasses. The path integral formalism is extensively discussed from various perspectives to suit different applications. Chapter 2 approaches path integrals through the Feynman-Kac formula and second quantization. In Chapter 5, they are examined within the context of effective field theories like Landau-Ginzburg theory, while Chapter 6 delves into their connection with Brownian motion, Langevin stochastic differential equations, and Fokker-Planck diffusion PDEs. The book also explores the relationship between stochastic processes and supersymmetry. Various techniques for computing path integrals, especially functional determinants, are introduced throughout the relevant chapters, offering the most suitable computational tools for each application.
Statistical Mechanics: Fundamentals and Model Solutions
by Teunis C DorlasStatistical Mechanics: Fundamentals and Model Solutions, Second Edition Fully updated throughout and with new chapters on the Mayer expansion for classical gases and on cluster expansion for lattice models, this new edition of Statistical Mechanics: Fundamentals and Model Solutions provides a comprehensive introduction to equilibrium statistical mechanics for advanced undergraduate and graduate students of mathematics and physics. The author presents a fresh approach to the subject, setting out the basic assumptions clearly and emphasizing the importance of the thermodynamic limit and the role of convexity. With problems and solutions, the book clearly explains the role of models for physical systems, and discusses and solves various models. An understanding of these models is of increasing importance as they have proved to have applications in many areas of mathematics and physics. Features Updated throughout with new content from the field An established and well-loved textbook Contains new problems and solutions for further learning opportunity Author Professor Teunis C. Dorlas is at the Dublin Institute for Advanced Studies, Ireland.
Statistical Mechanics
by Richard P. FeynmanPhysics, rather than mathematics, is the focus in this classic graduate lecture note volume on statistical mechanics and the physics of condensed matter. This book provides a concise introduction to basic concepts and a clear presentation of difficult topics, while challenging the student to reflect upon as yet unanswered questions.
Statistical Mechanics of Classical and Disordered Systems: Luminy, France, August 2018 (Springer Proceedings in Mathematics & Statistics #293)
by Véronique Gayrard Louis-Pierre Arguin Nicola Kistler Irina KourkovaThese proceedings of the conference Advances in Statistical Mechanics, held in Marseille, France, August 2018, focus on fundamental issues of equilibrium and non-equilibrium dynamics for classical mechanical systems, as well as on open problems in statistical mechanics related to probability, mathematical physics, computer science, and biology. Statistical mechanics, as envisioned more than a century ago by Boltzmann, Maxwell and Gibbs, has recently undergone stunning twists and developments which have turned this old discipline into one of the most active areas of truly interdisciplinary and cutting-edge research. The contributions to this volume, with their rather unique blend of rigorous mathematics and applications, outline the state-of-the-art of this success story in key subject areas of equilibrium and non-equilibrium classical and quantum statistical mechanics of both disordered and non-disordered systems. Aimed at researchers in the broad field of applied modern probability theory, this book, and in particular the review articles, will also be of interest to graduate students looking for a gentle introduction to active topics of current research.
Statistical Mechanics of Hamiltonian Systems with Bounded Kinetic Terms: An Insight into Negative Temperature (Springer Theses)
by Marco BaldovinRecent experimental evidence about the possibility of "absolute negative temperature" states in physical systems has triggered a stimulating debate about the consistency of such a concept from the point of view of Statistical Mechanics. It is not clear whether the usual results of this field can be safely extended to negative-temperature states; some authors even propose fundamental modifications to the Statistical Mechanics formalism, starting with the very definition of entropy, in order to avoid the occurrence of negative values of the temperature tout-court.The research presented in this thesis aims to shed some light on this controversial topic. To this end, a particular class of Hamiltonian systems with bounded kinetic terms, which can assume negative temperature, is extensively studied, both analytically and numerically. Equilibrium and out-of-equilibrium properties of this kind of system are investigated, reinforcing the overall picture that the introduction of negative temperature does not lead to any contradiction or paradox.
The Statistical Mechanics of Irreversible Phenomena
by Pierre GaspardThis book provides a comprehensive and self-contained overview of recent progress in nonequilibrium statistical mechanics, in particular, the discovery of fluctuation relations and other time-reversal symmetry relations. The significance of these advances is that nonequilibrium statistical physics is no longer restricted to the linear regimes close to equilibrium, but extends to fully nonlinear regimes. These important new results have inspired the development of a unifying framework for describing both the microscopic dynamics of collections of particles, and the macroscopic hydrodynamics and thermodynamics of matter itself. The book discusses the significance of this theoretical framework in relation to a broad range of nonequilibrium processes, from the nanoscale to the macroscale, and is essential reading for researchers and graduate students in statistical physics, theoretical chemistry and biological physics.
Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction
by Sacha Friedli Yvan VelenikThis motivating textbook gives a friendly, rigorous introduction to fundamental concepts in equilibrium statistical mechanics, covering a selection of specific models, including the Curie–Weiss and Ising models, the Gaussian free field, O(n) models, and models with Kać interactions. Using classical concepts such as Gibbs measures, pressure, free energy, and entropy, the book exposes the main features of the classical description of large systems in equilibrium, in particular the central problem of phase transitions. It treats such important topics as the Peierls argument, the Dobrushin uniqueness, Mermin–Wagner and Lee–Yang theorems, and develops from scratch such workhorses as correlation inequalities, the cluster expansion, Pirogov–Sinai Theory, and reflection positivity. Written as a self-contained course for advanced undergraduate or beginning graduate students, the detailed explanations, large collection of exercises (with solutions), and appendix of mathematical results and concepts also make it a handy reference for researchers in related areas. Builds a narrative around the driving concepts, focusing on specific examples and models. Self-contained and accessible. Features numerous exercises and solutions, as well as a comprehensive appendix.
Statistical Mechanics of Neural Networks
by Haiping HuangThis book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.
Statistical Meta-Analysis with Applications
by Bimal K. Sinha Guido Knapp Joachim HartungAn accessible introduction to performing meta-analysis across various areas of researchThe practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies.Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include:Meta-analysis regression proceduresMultiple-endpoint and multiple-treatment studiesThe Bayesian approach to meta-analysisPublication biasVote counting proceduresMethods for combining individual tests and combining individual estimatesUsing meta-analysis to analyze binary and ordinal categorical dataNumerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study.Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.
Statistical Method from the Viewpoint of Quality Control
by Walter A. ShewhartImportant text offers lucid explanation of how to regulate variables and maintain control over statistics in order to achieve quality control over manufactured products, crops and data. Topics include statistical control, establishing limits of variability, measurements of physical properties and constants, and specification of accuracy and precision. First inexpensive paperback edition.
Statistical Methodologies with Medical Applications
by Poduri Srs RaoThis book presents the methodology and applications of a range of important topics in statistics, and is designed for graduate students in Statistics and Biostatistics and for medical researchers. Illustrations and more than ninety exercises with solutions are presented. They are constructed from the research findings of the medical journals, summary reports of the Centre for Disease Control (CDC) and the World Health Organization (WHO), and practical situations. The illustrations and exercises are related to topics such as immunization, obesity, hypertension, lipid levels, diet and exercise, harmful effects of smoking and air pollution, and the benefits of gluten free diet. This book can be recommended for a one or two semester graduate level course for students studying Statistics, Biostatistics, Epidemiology and Health Sciences. It will also be useful as a companion for medical researchers and research oriented physicians.
Statistical Methods and Analyses for Medical Devices
by Scott A. PardoThis book provides a reference for people working in the design, development, and manufacturing of medical devices. While there are no statistical methods specifically intended for medical devices, there are methods that are commonly applied to various problems in the design, manufacturing, and quality control of medical devices. The aim of this book is not to turn everyone working in the medical device industries into mathematical statisticians; rather, the goal is to provide some help in thinking statistically, and knowing where to go to answer some fundamental questions, such as justifying a method used to qualify/validate equipment, or what information is necessary to support the choice of sample sizes.While, there are no statistical methods specifically designed for analysis of medical device data, there are some methods that seem to appear regularly in relation to medical devices. For example, the assessment of receiver operating characteristic curves is fundamental to development of diagnostic tests, and accelerated life testing is often critical for assessing the shelf life of medical device products. Another example is sensitivity/specificity computations are necessary for in-vitro diagnostics, and Taguchi methods can be very useful for designing devices. Even notions of equivalence and noninferiority have different interpretations in the medical device field compared to pharmacokinetics. It contains topics such as dynamic modeling, machine learning methods, equivalence testing, and experimental design, for example.This book is for those with no statistical experience, as well as those with statistical knowledgeable—with the hope to provide some insight into what methods are likely to help provide rationale for choices relating to data gathering and analysis activities for medical devices.
Statistical Methods and Applications in Forestry and Environmental Sciences (Forum for Interdisciplinary Mathematics)
by Girish Chandra Raman Nautiyal Hukum ChandraThis book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling, small area estimation, calibration approach-based estimators, design of experiments, multivariate techniques, Internet of Things, and ridge regression methods. It also covers the history of the implementation of statistical techniques in Indian forestry and the National Forest Inventory of India.The book is a valuable resource for applied statisticians, students, researchers, and practitioners in the forestry and environment sector. It includes real-world examples and case studies to help readers apply the techniques discussed. It also motivates academicians and researchers to use new technologies in the areas of forestry and environmental sciences with the help of software like R, MATLAB, Statistica, and Mathematica.
Statistical Methods and Modeling of Seismogenesis
by Nikolaos Limnios Eleftheria Papadimitriou George TsaklidisThe study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.
Statistical Methods: Connections, Equivalencies, and Relationships
by Kenneth J. Berry Janis E. JohnstonThe primary purpose of this book is to introduce the reader to a wide variety of interesting and useful connections, relationships, and equivalencies between and among conventional and permutation statistical methods. There are approximately 320 statistical connections and relationships described in this book. For each connection or connections the tests are described, the connection is explained, and an example analysis illustrates both the tests and the connection(s). The emphasis is more on demonstrations than on proofs, so little mathematical expertise is assumed. While the book is intended as a stand-alone monograph, it can also be used as a supplement to a standard textbook such as might be used in a second- or third-term course in conventional statistical methods. Students, faculty, and researchers in the social, natural, or hard sciences will find an interesting collection of statistical connections and relationships - some well-known, some more obscure, and some presented here for the first time.
Statistical Methods for Cancer Studies
by Richard G. CornellThis book focuses on public health and epidemiologic aspects of cancer, and explores the sources of information concerning the frequency of occurrence of human cancer. It describes statistical methods useful in studying problems arising in the field of cancer and its concurrent development.