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An Introduction to Statistical Concepts: Third Edition
by Richard G. Lomax Debbie L. Hahs-VaughnThe new edition of An Introduction to Statistical Concepts, is designed to help students really understand statistical concepts, the situations in which they can be used, and how to apply them to data. Hahs-Vaughn and Lomax discuss the most popular, along with many of the lesser-known, procedures and models, whilst also exploring nonparametric procedures used when standard assumptions are violated. They provide in-depth coverage of testing assumptions and highlight several online tools for computing statistics (e.g., effect sizes and their confidence intervals and power). This comprehensive, flexible and accessible text includes a new chapter on mediation and moderation; expanded coverage of effect sizes; discussions of sensitivity, specificity, false positive, and false negative, along with using the receiver operator characteristic (ROC) curve. This book, noted for its crystal-clear explanations, and its inclusion of only the most crucial equations, is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral disciplines—from education, business, communication, exercise science, psychology, sociology and more.
An Introduction to Statistical Data Science: Theory and Models
by Giorgio PicciThis graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data. Aimed at young scientists, and motivated by their scientific prospects, it provides first principle derivations of various algorithms and procedures, thereby supplying a solid background for their future specialization to diverse fields and applications. The beginning of the book presents the basics of statistical science, with an exposition on linear models. This is followed by an analysis of some numerical aspects and various regularization techniques, including LASSO, which are particularly important for large scale problems. Decision problems are studied both from the classical hypothesis testing perspective and, particularly, from a modern support-vector perspective, in the linear and non-linear context alike. Underlying the book is the Bayesian approach and the Bayesian interpretation of various algorithms and procedures. This is the key to principal components analysis and canonical correlation analysis, which are explained in detail. Following a chapter on nonlinear inference, including material on neural networks, the book concludes with a discussion on time series analysis and estimating their dynamic models. Featuring examples and exercises partially motivated by engineering applications, this book is intended for graduate students in applied mathematics and engineering with a general background in probability and linear algebra.
An Introduction to Statistical Inference and Its Applications with R (Chapman & Hall/CRC Texts in Statistical Science)
by Michael W. TrossetEmphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics #103)
by Trevor Hastie Gareth James Robert Tibshirani Daniela WittenAn 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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report
by Lisa Daniels Nicholas W. MinotAn Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.
An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report
by Lisa Daniels Nicholas W. MinotAn Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.
An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report
by Lisa Daniels Nicholas W. MinotAn Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report, Second Edition provides an integrated approach to research methods, statistics and data analysis, and interpretation of results in Stata. Drawing on their combined 25 years of experience teaching statistics and research methods, authors Lisa Daniels and Nicholas Minot frame data analysis within the research process—identifying gaps in the literature, examining the theory, developing research questions, designing a questionnaire or using secondary data, analyzing the data, and writing a research paper—so readers better understand the context of data analysis. Throughout, the text focuses on documenting and communicating results so students can produce a finished report or article by the end of their courses. The Second Edition has been thoroughly updated with all new articles and data—including coverage of ChatGPT, COVID-19 policies, and SAT scores—to demonstrate the relevance of data analysis for students. A new chapter on advanced methods in regression analysis allows instructors to better feature these important techniques. Stata code has been updated to the latest version, and new exercises throughout offer more chances for practice.
An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report
by Lisa Daniels Nicholas W. MinotAn Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report, Second Edition provides an integrated approach to research methods, statistics and data analysis, and interpretation of results in Stata. Drawing on their combined 25 years of experience teaching statistics and research methods, authors Lisa Daniels and Nicholas Minot frame data analysis within the research process—identifying gaps in the literature, examining the theory, developing research questions, designing a questionnaire or using secondary data, analyzing the data, and writing a research paper—so readers better understand the context of data analysis. Throughout, the text focuses on documenting and communicating results so students can produce a finished report or article by the end of their courses. The Second Edition has been thoroughly updated with all new articles and data—including coverage of ChatGPT, COVID-19 policies, and SAT scores—to demonstrate the relevance of data analysis for students. A new chapter on advanced methods in regression analysis allows instructors to better feature these important techniques. Stata code has been updated to the latest version, and new exercises throughout offer more chances for practice.
An Introduction to Statistics with Python: With Applications in the Life Sciences (Statistics and Computing)
by Thomas HaslwanterNow in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.
An Introduction to Statistics: An Active Learning Approach
by Kieth A. Carlson Jennifer R. WinquistAn Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.
An Introduction to Statistics: An Active Learning Approach
by Kieth A. Carlson Jennifer R. WinquistAn Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.
An Introduction to Statistics: An Active Learning Approach
by Jennifer R. Winquist Kieth Alton CarlsonThis updated and reorganized Third Edition of this textbook takes a workbook-style approach that encourages an active approach to learning statistics. Carefully placed reading questions throughout each chapter allow students to apply their knowledge right away, while in-depth activities based on current behavioral science scenarios, each with problem sets and quiz questions, give students the opportunity to assess their understanding of concepts while reading detailed explanations of more complex statistical concepts. Additional practice problems further solidify student learning. Most activities are self-correcting, so if a concept is misunderstood, this misunderstanding is corrected early in the learning process. After working through each chapter, students are far more likely to understand the material than when they only read the material.
An Introduction to Statistics: An Active Learning Approach
by Jennifer R. Winquist Kieth Alton CarlsonThis updated and reorganized Third Edition of this textbook takes a workbook-style approach that encourages an active approach to learning statistics. Carefully placed reading questions throughout each chapter allow students to apply their knowledge right away, while in-depth activities based on current behavioral science scenarios, each with problem sets and quiz questions, give students the opportunity to assess their understanding of concepts while reading detailed explanations of more complex statistical concepts. Additional practice problems further solidify student learning. Most activities are self-correcting, so if a concept is misunderstood, this misunderstanding is corrected early in the learning process. After working through each chapter, students are far more likely to understand the material than when they only read the material.
An Introduction to Stochastic Processes in Physics
by Don S. LemonsThis book provides an accessible introduction to stochastic processes in physics and describes the basic mathematical tools of the trade: probability, random walks, and Wiener and Ornstein-Uhlenbeck processes. It includes end-of-chapter problems and emphasizes applications.An Introduction to Stochastic Processes in Physics builds directly upon early-twentieth-century explanations of the "peculiar character in the motions of the particles of pollen in water" as described, in the early nineteenth century, by the biologist Robert Brown. Lemons has adopted Paul Langevin's 1908 approach of applying Newton's second law to a "Brownian particle on which the total force included a random component" to explain Brownian motion. This method builds on Newtonian dynamics and provides an accessible explanation to anyone approaching the subject for the first time. Students will find this book a useful aid to learning the unfamiliar mathematical aspects of stochastic processes while applying them to physical processes that he or she has already encountered.
An Introduction to Stochastic Processes in Physics: Containing On The Theory Of Brownian Motion By Paul Langevin, Translated By Anthony Gythiel
by Don S. LemonsThis “lucid, masterfully written introduction to an often difficult subject . . . belongs on the bookshelf of every student of statistical physics” (Dr. Brian J. Albright, Applied Physics Division, Los Alamos National Laboratory).This book provides an accessible introduction to stochastic processes in physics and describes the basic mathematical tools of the trade: probability, random walks, and Wiener and Ornstein-Uhlenbeck processes. With an emphasis on applications, it includes end-of-chapter problems. Physicist and author Don S. Lemons builds on Paul Langevin’s seminal 1908 paper “On the Theory of Brownian Motion” and its explanations of classical uncertainty in natural phenomena. Following Langevin’s example, Lemons applies Newton’s second law to a “Brownian particle on which the total force included a random component.” This method builds on Newtonian dynamics and provides an accessible explanation to anyone approaching the subject for the first time. This volume contains the complete text of Paul Langevin’s “On the Theory of Brownian Motion,” translated by Anthony Gythiel.
An Introduction to Stochastic Processes with Applications to Biology
by Linda J. AllenAn Introduction to Stochastic Processes with Applications to Biology, Second Edition presents the basic theory of stochastic processes necessary in understanding and applying stochastic methods to biological problems in areas such as population growth and extinction, drug kinetics, two-species competition and predation, the spread of epidemics, and
An Introduction to Support Vector Machines: and other kernel-based learning methods
by John Shawe-Taylor Nello CristianiniThis is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
An Introduction to Survey Research, Polling, and Data Analysis (3rd edition)
by Herbert F. Weisberg Jon A. Krosnick Bruce D. BowenExplains how surveys are conducted, how to read statistical reports, and how to analyze data, and provides guidelines for evaluating polls. Coverage includes understanding tables, interval statistics, survey design, sampling and question-writing, interviewing and coding strategies, analysis of different types of surveys, and reading and writing reports. Includes chapter exercises and answers. Paper edition (unseen), $25. 95. Annotation c. by Book News, Inc. , Portland, Or.
An Introduction to Systems Biology: Design Principles of Biological Circuits, Second Edition (Chapman & Hall/CRC Mathematical and Computational Biology)
by Uri AlonPraise for the first edition: … superb, beautifully written and organized work that takes an engineering approach to systems biology. Alon provides nicely written appendices to explain the basic mathematical and biological concepts clearly and succinctly without interfering with the main text. He starts with a mathematical description of transcriptional activation and then describes some basic transcription-network motifs (patterns) that can be combined to form larger networks. – Nature [This text deserves] serious attention from any quantitative scientist who hopes to learn about modern biology … It assumes no prior knowledge of or even interest in biology … One final aspect that must be mentioned is the wonderful set of exercises that accompany each chapter. … Alon’s book should become a standard part of the training of graduate students. – Physics Today Written for students and researchers, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological systems. It highlights simple, recurring circuit elements that make up the regulation of cells and tissues. Rigorously classroom-tested, this edition includes new chapters on exciting advances made in the last decade. Features: Includes seven new chapters The new edition has 189 exercises, the previous edition had 66 Offers new examples relevant to human physiology and disease
An Introduction to Tensor Analysis
by Bipin Singh Koranga Sanjay Kumar PadaliyaThe subject of Tensor Analysis deals with the problem of the formulation of the relation between various entities in forms which remain invariant when we pass from one system of coordinates to another. The invariant form of equation is necessarily related to the possible system of coordinates with reference to which the equation remains invariant. The primary purpose of this book is the study of the invariance form of equation relative to the totally of the rectangular co-ordinate system in the three-dimensional Euclidean space. We start with the consideration of the way the sets representing various entities are transformed when we pass from one system of rectangular co-ordinates to another. A Tensor may be a physical entity that can be described as a Tensor only with respect to the manner of its representation by means of multi-sux sets associated with different system of axes such that the sets associated with different system of co-ordinate obey the transformation law for Tensor. We have employed sux notation for tensors of any order, we could also employ single letter such A,B to denote Tensors.
An Introduction to Tensors and Group Theory for Physicists
by Nadir JeevanjeeAn Introduction to Tensors and Group Theory for Physicists provides both an intuitive and rigorous approach to tensors and groups and their role in theoretical physics and applied mathematics. A particular aim is to demystify tensors and provide a unified framework for understanding them in the context of classical and quantum physics. Connecting the component formalism prevalent in physics calculations with the abstract but more conceptual formulation found in many mathematical texts, the work will be a welcome addition to the literature on tensors and group theory. Advanced undergraduate and graduate students in physics and applied mathematics will find clarity and insight into the subject in this textbook.
An Introduction to Theory and Applications of Stationary Variational-Hemivariational Inequalities (Applied Mathematical Sciences #220)
by Weimin HanThis book offers a comprehensive and accessible introduction to the mathematical theory of stationary Variational-Hemivariational Inequalities (VHIs), a rapidly growing area of research with significant applications in science and engineering. Unlike traditional approaches that rely heavily on abstract inclusion results for pseudomonotone operators, this work presents a more user-friendly method grounded in basic Functional Analysis. VHIs include variational inequalities and hemivariational inequalities as special cases. The book systematically categorizes and names different VHIs, making it easier for readers to understand the specific problems being addressed. Designed for graduate students and researchers in mathematics, physical sciences, and engineering, this monograph not only provides a concise review of essential materials in Sobolev spaces, convex analysis, and nonsmooth analysis but also delves into applications in contact and fluid mechanics. Through detailed explanations and practical examples, the book bridges the gap between theory and practice, making the complex subject of VHIs more approachable. By focusing on the well-posedness of various forms of VHIs and extending the analysis to include mixed VHIs for the Stokes and Navier-Stokes equations, this book serves as an essential resource for anyone interested in the modeling, analysis, numerical solutions, and real-world applications of VHIs.
An Introduction to Thermal Physics
by Daniel SchroederThermal physics deals with collections of large numbers of particles - typically 10 to the 23rd power or so. Examples include the air in a balloon, the water in a lake, the electrons in a chunk of metal, and the photons given off by the sun. We can't possibly follow every detail of the motions of so many particles. So in thermal physics we assume that these motions are random, and we use the laws of probability to predict how the material as a whole ought to behave. Alternatively, we can measure the bulk properties of a material, and from these infer something about the particles it is made of. This book will give you a working understanding of thermal physics, assuming that you have already studied introductory physics and calculus. You will learn to apply the general laws of energy and entropy to engines, refrigerators, chemical reactions, phase transformations, and mixtures. You will also learn to use basic quantum physics and powerful statistical methods to predict in detail how temperature affects molecular speeds, vibrations of solids, electrical and magnetic behaviors, emission of light, and exotic low-temperature phenomena. The problems and worked examples explore applications not just within physics but also to engineering, chemistry, biology, geology, atmospheric science, astrophysics, cosmology, and everyday life.
An Introduction to Thermodynamics and Statistical Physics (UNITEXT for Physics)
by Piero OllaThis textbook offers an advanced undergraduate or initial graduate level introduction to topics such as kinetic theory, equilibrium statistical mechanics and the theory of fluctuations from a modern perspective. The aim is to provide the reader with the necessary tools of probability theory and thermodynamics (especially the thermodynamic potentials) to enable subsequent study at advanced graduate level. At the same time, the book offers a bird's eye view on arguments that are often disregarded in the main curriculum courses. Further features include a focus on the interdisciplinary nature of the subject and in-depth discussion of alternative interpretations of the concept of entropy. While some familiarity with basic concepts of thermodynamics and probability theory is assumed, this does not extend beyond what is commonly obtained in basic undergraduate curriculum courses.
An Introduction to Traffic Flow Theory (Springer Optimization and Its Applications #84)
by Lily ElefteriadouThis text provides a comprehensive and concise treatment of the topic of traffic flow theory and includes several topics relevant to today's highway transportation system. It provides the fundamental principles of traffic flow theory as well as applications of those principles for evaluating specific types of facilities (freeways, intersections, etc. ). Newer concepts of Intelligent transportation systems (ITS) and their potential impact on traffic flow are discussed. State-of-the-art in traffic flow research and microscopic traffic analysis and traffic simulation have significantly advanced and are also discussed in this text. Real world examples and useful problem sets complement each chapter. This textbook is meant for use in advanced undergraduate/graduate level courses in traffic flow theory with prerequisites including two semesters of calculus, statistics, and an introductory course in transportation. The text would also be of interest to transportation professionals as a refresher in traffic flow theory, or as a reference. Students and engineers of diverse backgrounds will find this text accessible and applicable to today's traffic issues.