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Introduction to Financial Option Valuation
This is a lively textbook providing a solid introduction to financial option valuation for undergraduate students armed with a working knowledge of a first year calculus. Written in a series of short chapters, its self-contained treatment gives equal weight to applied mathematics, stochastics and computational algorithms. No prior background in probability, statistics or numerical analysis is required. Detailed derivations of both the basic asset price model and the Black–Scholes equation are provided along with a presentation of appropriate computational techniques including binomial, finite differences and in particular, variance reduction techniques for the Monte Carlo method. Each chapter comes complete with accompanying stand-alone MATLAB code listing to illustrate a key idea. Furthermore, the author has made heavy use of figures and examples, and has included computations based on real stock market data.
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Simulation for Data Science with R
Harness actionable insights from your data with computational statistics and simulations using RAbout This Book• Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies• A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.What You Will Learn• The book aims to explore advanced R features to simulate data to extract insights from your data.• Get to know the advanced features of R including high-performance computing and advanced data manipulation• See random number simulation used to simulate distributions, data sets, and populations• Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations• Applications to design statistical solutions with R for solving scientific and real world problems• Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.In DetailData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these
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Introduction to Statistical Modelling
Statisticians rely heavily on making models of 'causal situations' in order to fully explain and predict events. Modelling therefore plays a vital part in all applications of statistics and is a component of most undergraduate programmes. 'An Introduction to Statistical Modelling' provides a single reference with an applied slant that caters for all three years of a degree course. The book concentrates on core issues and only the most essential mathematical justifications are given in detail. Attention is firmly focused on the statistical aspects of the techniques, in this lively, practical approach.
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Logical Introduction to Probability and Induction
A Logical Introduction to Probability and Induction is a textbook on the mathematics of the probability calculus and its applications in philosophy.On the mathematical side, the textbook introduces these parts of logic and set theory that are needed for a precise formulation of the probability calculus. On the philosophical side, the main focus is on the problem of induction and its reception in epistemology and the philosophy of science. Particular emphasis is placed on the means-end approach to the justification of inductive inference rules.In addition, the book discusses the major interpretations of probability. These are philosophical accounts of the nature of probability that interpret the mathematical structure of the probability calculus. Besides the classical and logical interpretation, they include the interpretation of probability as chance, degree of belief, and relative frequency. The Bayesian interpretation of probability as degree of belief locates probability in a subject's mind. It raises the question why her degreesof belief ought to obey the probability calculus. In contrast to this, chance and relative frequency belong to the external world. While chance is postulated by theory, relative frequencies can be observed empirically.A Logical Introduction to Probability and Induction aims to equip students with the ability to successfully carry out arguments. It begins with elementary deductive logic and uses it as basis for the material on probability and induction. Throughout the textbook results are carefully proved using the inference rules introduced at the beginning, and students are asked to solve problems in the form of 50 exercises. An instructor's manual contains the solutions to these exercises as wellas suggested exam questions.The book does not presuppose any background in mathematics, although sections 10.3-10.9 on statistics are technically sophisticated and optional. The textbook is suitable for lower level undergraduate courses in philosophy and logic.
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Introduction to Stochastic Processes Using R
This textbook presents some basic stochastic processes, mainly Markov processes. It begins with a brief introduction to the framework of stochastic processes followed by the thorough discussion on Markov chains, which is the simplest and the most important class of stochastic processes. The book then elaborates the theory of Markov chains in detail including classification of states, the first passage distribution, the concept of periodicity and the limiting behaviour of a Markov chain in terms of associated stationary and long run distributions. The book first illustrates the theory for some typical Markov chains, such as random walk, gambler's ruin problem, Ehrenfest model and Bienayme-Galton-Watson branching process; and then extends the discussion when time parameter is continuous. It presents some important examples of a continuous time Markov chain, which include Poisson process, birth process, death process, birth and death processes and their variations. These processes play a fundamental role in the theory and applications in queuing and inventory models, population growth, epidemiology and engineering systems. The book studies in detail the Poisson process, which is the most frequently applied stochastic process in a variety of fields, with its extension to a renewal process.The book also presents important basic concepts on Brownian motion process, a stochastic process of historic importance. It covers its few extensions and variations, such as Brownian bridge, geometric Brownian motion process, which have applications in finance, stock markets, inventory etc. The book is designed primarily to serve as a textbook for a one semester introductory course in stochastic processes, in a post-graduate program, such as Statistics, Mathematics, Data Science and Finance. It can also be used for relevant courses in other disciplines. Additionally, it provides sufficient background material for studying inference in stochastic processes. The book thus fulfils the need of a concise but clear and student-friendly introduction to various types of stochastic processes.
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Introduction To Quantum Statistical Mechanics (2nd Edition)
Introduction to Quantum Statistical Mechanics (Second Edition) may be used as an advanced textbook by graduate students, even ambitious undergraduates in physics. It is also suitable for non experts in physics who wish to have an overview of some of the classic and fundamental quantum models in the subject. The explanation in the book is detailed enough to capture the interest of the reader, and complete enough to provide the necessary background material needed to dwell further into the subject and explore the research literature.
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Introduction to Computational Risk Management of Equity-Linked Insurance
The quantitative modeling of complex systems of interacting risks is a fairly recent development in the financial and insurance industries. Over the past decades, there has been tremendous innovation and development in the actuarial field. In addition to undertaking mortality and longevity risks in traditional life and annuity products, insurers face unprecedented financial risks since the introduction of equity-linking insurance in 1960s. As the industry moves into the new territory of managing many intertwined financial and insurance risks, non-traditional problems and challenges arise, presenting great opportunities for technology development.Today's computational power and technology make it possible for the life insurance industry to develop highly sophisticated models, which were impossible just a decade ago. Nonetheless, as more industrial practices and regulations move towards dependence on stochastic models, the demand for computational power continues to grow. While the industry continues to rely heavily on hardware innovations, trying to make brute force methods faster and more palatable, we are approaching a crossroads about how to proceed. An Introduction to Computational Risk Management of Equity-Linked Insurance provides a resource for students and entry-level professionals to understand the fundamentals of industrial modeling practice, but also to give a glimpse of software methodologies for modeling and computational efficiency. Features Provides a comprehensive and self-contained introduction to quantitative risk management of equity-linked insurance with exercises and programming samples Includes a collection of mathematical formulations of risk management problems presenting opportunities and challenges to applied mathematicians Summarizes state-of-arts computational techniques for risk management professionals Bridges the gap between the latest developments in finance and actuarial literature and the practice of risk management for investment-combined life insurance Gives a comprehensive review of both Monte Carlo simulation methods and non-simulation numerical methods Runhuan Feng is an Associate Professor of Mathematics and the Director of Actuarial Science at the University of Illinois at Urbana-Champaign. He is a Fellow of the Society of Actuaries and a Chartered Enterprise Risk Analyst. He is a Helen Corley Petit Professorial Scholar and the State Farm Companies Foundation Scholar in Actuarial Science. Runhuan received a Ph.D. degree in Actuarial Science from the University of Waterloo, Canada. Prior to joining Illinois, he held a tenure-track position at the University of Wisconsin-Milwaukee, where he was named a Research Fellow.Runhuan received numerous grants and research contracts from the Actuarial Foundation and the Society of Actuaries in the past. He has published a series of papers on top-tier actuarial and applied probability journals on stochastic analytic approaches in risk theory and quantitative risk ma
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Introduction to Secondary Data Analysis with IBM SPSS Statistics
Many professional, high-quality surveys collect data on people′s behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to:Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and ′clean′ secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you′ll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book′s companion website give you an opportunity to practice, check your understanding and work hands on with real data as you′re learning.
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Introduction to Data Analysis
Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.
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Introduction to Parallel Algorithms
Parallel algorithms Made Easy The complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms-appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book: * Presents basic concepts in clear and simple terms * Incorporates numerous examples to enhance students' understanding * Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering * Employs extensive illustrations of new design techniques * Discusses parallel algorithms in the context of PRAM model * Includes end-of-chapter exercises and detailed references on parallel computing. This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.
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Introduction to Probability Models, ISE
Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions. A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states. Simplified Approach for Analyzing Nonhomogeneous Poisson processes Additional results on queues relating to the (a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system; (b) inspection paradox for M/M/1 queues (c) M/G/1 queue with server breakdown Many new examples and exercises.
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Introduction to Quantum Computing
This textbook introduces quantum computing to readers who do not have much background in linear algebra based on the self-study experience of the author as an engineer. The author targets undergraduate and master students who are willing to spend about 60 -90 hours seriously learning quantum computing. This book is also suitable for self-study and teaching videos for each chapter and more than 200 exercises with answers are provided. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike books that only give superficial, “hand-waving” explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book
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Introduction to Classifier Performance Analysis with R
Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features:An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learning.Commonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPA.Limitations of some commonly used performance measures are highlighted.Coverage includes performance parameters and inferential techniques for them.Also covered are techniques for comparative analysis of competing classifiers.A key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick package.This is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.
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Introduction to Bayesian Inference, Methods and Computation
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
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Introduction to a Social Worker
Originally published in 1964, this was an introduction to social casework, that is social work with individuals and families. It was written for students at the beginning of their training and, while intended for the social worker, it would also prove useful to other students of the social sciences and interesting to the ordinary citizen who wanted to know what social work could offer either to people in trouble or to those who adopted it as a career.The book was both authoritative and up-to-date at the time, in a field in which methods of training had evolved quite rapidly. To this its origin bears witness: the preparatory work was done by Miss Florence Mitchell, an experienced social worker and teacher of social casework. The book was shaped in consultation with other practitioners and teachers, including Dr Younghusband and Miss K. M. Lewis of the National Institute whose work had done so much to shape modern methods of training.The first chapter gives a picture of people who need the social worker’s help. It is followed by a brief survey of methods, by three chapters of case studies and by a final chapter on social work in the social services. The book thus combines a philosophy with practical guidance.
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Invitation to Law and Social Science
This innovative work treats law as the set of rules governing how people should act in society, and it demonstrates how the legal system attempts to deter antisocial behavior. Comprised of three sections. the book explores different ways in which law decides issues of responsibility, how cases are adjudicated, and theories of distributive justice and social change. Distinguished by its problem-oriented, topical perspective, An Invitation to Law and Social Science serves as an invaluable book for course in law and society, legal process, and the sociology of law.
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Introduction to Computational Physics for Undergraduates
This is an introductory textbook on computational methods and techniques intended for undergraduates at the sophomore or junior level in the fields of science, mathematics, and engineering. It provides an introduction to programming languages such as FORTRAN 90/95/2000 and covers numerical techniques such as differentiation, integration, root finding, and data fitting. The textbook also entails the use of the Linux/Unix operating system and other relevant software such as plotting programs, text editors, and mark up languages such as LaTeX. It includes multiple homework assignments.
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Introduction to Scientific Research Methods in Geography and Environmental Studies
"Covers a broad range of subjects that undergraduates in the discipline should be familiar and comfortable with upon graduation. From chapters on the scientific method and fundamental research concepts, to experimental design, sampling and statistical analysis, the text offers an excellent introduction to the key concepts of geographical research. The content is applicable for students at the beginning of their studies right through to planning and conducting dissertations. The book has also been of particular support in designing my level 1 and 2 tutorials which cover similar ground to several of the chapters."- Joseph Mallalieu, School of Geography, Leeds University"Montello and Sutton is one of the best texts I′ve used in seminars on research methodology. The text offers a clear balance of quantitative vs. qualitative and physical vs. human which I′ve found particularly valuable. The chapters on research ethics, scientific communication, information technologies and data visualization are excellent." - Kenneth E. Foote, Department of Geography, University of Colorado at BoulderThis is a broad and integrative introduction to the conduct and interpretation of scientific research, covering both geography and environmental studies. Written for undergraduate and postgraduate students, it:Explains both the conceptual and the technical aspects of research, as well as all phases of the research processCombines approaches in physical geography and environmental science, human geography and human-environment relations, and geographic and environmental information techniques (such as GIS, cartography, and remote sensing)Combines natural and social scientific approaches common to subjects in geography and environmental studiesIncludes case studies of actual research projects to demonstrate the breadth of approaches takenIt will be core reading for students studying scientific research methods in geography, environmental studies and related disciplines such as planning and earth science.
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Introduction to Symbolic Dynamics and Coding
Symbolic dynamics is a mature yet rapidly developing area of dynamical systems. It has established strong connections with many areas, including linear algebra, graph theory, probability, group theory, and the theory of computation, as well as data storage, statistical mechanics, and $C^*$-algebras. This Second Edition maintains the introductory character of the original 1995 edition as a general textbook on symbolic dynamics and its applications to coding. It is written at an elementary level and aimed at students, well-established researchers, and experts in mathematics, electrical engineering, and computer science. Topics are carefully developed and motivated with many illustrative examples. There are more than 500 exercises to test the reader's understanding. In addition to a chapter in the First Edition on advanced topics and a comprehensive bibliography, the Second Edition includes a detailed Addendum, with companion bibliography, describing major developments and new research directions since publication of the First Edition.
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Practical Introduction to Econometric Methods
The text is aimed at final-year undergraduate students or those at the graduate level doing econometrics for the first time. It is an introductory course in the theory and practice of classical and modern econometric methods. A proper study of the material will allow the reader to - Understand the scope and limitations of classical and modern econometric techniques - Read, write and properly interpret articles and reports of an applied econometric nature - Build upon the elements of econometric theory and practice introduced in the book Although some basic knowledge of matrix algebra and elementary statistical theory will be assumed, much of it is covered in the body of the text. All the main theoretical concepts are illustrated with the use of econometric software, mainly EViews.
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Modern Introduction to Probability and Statistics
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
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Introduction to Empirical Processes and Semiparametric Inference
Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
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Introduction to Psychometrics and Psychological Assessment
An Introduction to Psychometrics and Psychological Assessment is the successor to Cooper’s prize-winning book Psychological Testing: Theory and Practice. This expanded and updated volume shows how psychological questionnaires and tests can be chosen, administered, scored, interpreted and developed. In providing students, researchers, test users, test developers and practitioners in the social sciences, education and health with an evaluative guide to choosing, using, interpreting and developing tests, it provides readers a thorough grasp of the principles (and limitations) of testing, together with the necessary methodological detail.This book has three distinctive features. First, it stresses the basic logic of psychological assessment without getting bogged down with mathematics; the spreadsheet simulations and utilities which are integrated into the text allow users to explore how numbers behave, rather than reading equations. Readers will "learn by doing". Second, it covers both the theory behind psychological assessment and the practicalities of locating, designing and using tests and interpreting their scores. Finally, it is evaluative. Rather than just describing concepts such as test reliability or adaptive testing, it stresses the underlying principles, merits and drawbacks of each approach to assessment, and methods of developing and evaluating questionnaires and tests. Unusually for an introductory text, it includes coverage of several cutting-edge techniques, and this new edition expands the discussion on measurement invariance, methods of detecting/quantifying bias and hierarchical factor models, and features added sections on:Best practices for translation of tests into other languages and problems of cultural biasAutomatic item generationThe advantages, drawbacks and practicalities of internet-based testingGeneralizability theoryNetwork analysisDangerous assumptions made when scoring testsThe accuracy of tests used for assessing individualsThe two-way relationship between psychometrics and psychological theoryAimed at non-mathematicians, this friendly and engaging text will help you to understand the fundamental principles of psychometrics that underpin the measurement of any human characteristic using any psychological test. Written by a leading figure in the field and accompanied by additional resources, including a set of spreadsheets which use simulated data and other techniques to illustrate important issues, this is an essential introduction for all students of psychology and related disciplines. It assumes very little statistical background and is written for students studying psychological assessment or psychometrics, and for researchers and practitioners who use questionnaires and tests to measure personality, cognitive abilities, educational attainment, mood or motivation.
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Concise Introduction to Econometrics
In this short and very practical 2002 introduction to econometrics Philip Hans Franses guides the reader through the essential concepts of econometrics. Central to the book are practical questions in various economic disciplines, which can be answered using econometric methods and models. The book focuses on a limited number of the essential, most widely used methods, before going on to review the basics of econometrics. The book ends with a number of case studies drawn from recent empirical work to provide an intuitive illustration of what econometricians do when faced with practical questions. Throughout the book Franses emphasises the importance of specification, evaluation and implementation of models appropriate to the data. Assuming basic familiarity only with matrix algebra and calculus the book is designed to appeal as either a short stand-alone introduction for students embarking on an empirical research project or as a supplement to any standard introductory textbook.
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Introduction to Using Mapping Sentences
This book acts as an introductory guide to understanding and using the mapping sentence as a tool in social science and humanities research. The book fills the need for a concise text that simply instructs how and when to use a mapping sentence and provides practical examples. Mapping sentences are a major research component and tool of facet theory. The book begins by covering the background to mapping sentence, including the philosophy and theory underpinning it. The following chapter discuss what mapping sentence is, what different kinds of mapping sentences exist, and knowing when and which to use it in a given situation. The book then moves into describing how to write a mapping sentence and how to analyse the information gained from mapping sentence research. It ends with a consideration of the future developments of mapping sentences and their applications across the social sciences and humanities, including in particular psychology, marketing, behavioural biology, art and health.
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Introduction to Acceptance Sampling and SPC with R
An Introduction to Acceptance Sampling and SPC with R is an introduction to statistical methods used in monitoring, controlling and improving quality. Topics covered include acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; and the design and analysis of experiments for process troubleshooting and discovering ways to improve process output. Origins of statistical quality control and the technical topics presented in the remainder of the book are those recommended in the ANSI/ASQ/ISO guidelines and standards for industry. The final chapter ties everything together by discussing modern management philosophies that encourage the use of the technical methods presented earlier.In the modern world sampling plans and the statistical calculations used in statistical quality control are done with the help of computers. As an open source high-level programming language with flexible graphical output options, R runs on Windows, Mac and Linux operating systems, and has add-on packages that equal or exceed the capability of commercial software for statistical methods used in quality control. In this book, we will focus on several R packages. In addition to demonstrating how to use R for acceptance sampling and control charts, this book will concentrate on how the use of these specific tools can lead to quality improvements both within a company and within their supplier companies.This would be a suitable book for a one-semester undergraduate course emphasizing statistical quality control for engineering majors (such as manufacturing engineering or industrial engineering), or a supplemental text for a graduate engineering course that included quality control topics.
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Contemporary Introduction to Sociology
The influential authors significantly update their popular introductory text that invites students to reflect on their lives in the context of the combustible leap from modern to postmodern life. The authors show how culture is central to understanding many world problems as they challenge readers to confront the problems and possibilities of an era in which the futures of the physical and social environments seem uncertain. As culture rapidly changes in the 21st century, the authors have successfully incorporated these nuances with many important updates on race and racism, Black Lives Matter, the rise of populist politics, ISIS, new social media, feminist perspectives on sex work, trans and non-gender conforming identities, and more.New to this edition:New data, text box examples, photos, exercises, study questions, and glossary terms appear throughout.New discussions added of arts-based and participatory approaches to research, historical changes in the perception of deviance, legalization of marijuana; Islam vs. secularism in France, new forms of socialization, heteronormative and essentialist language related to sex and gender, intersections of social class and other identities, the prison industrial complex, informal sharing economies, atheism, and more.New text boxes include: Young Saudis Find Freedom in their PhonesHow One Stupid Tweet Blew Up Justine Sacco’s LifeSchool-to-Prison PipelineIndia’s Reproductive Assembly LineWorkers Feel Pain of LayoffsLike Prohibition, the fight over guns is about something elseMicro-aggression and Changing Moral CulturesPraise for A Contemporary Introduction to Sociology"Treats sociology as a living, vibrant discipline. The book is a masterful synthesis written in a style that is at once sophisticated, engaging, and accessible."—Peter Kivisto, Augustana College "Alexander and Thompson have produced the modern textbook we have all been waiting for—comprehensive and coherent, but above all intelligent. Designed to make teaching sociology unproblematic, the book is the ideal combination of theory, evidence, and accessibility."—Bryan S. Turner, editor of The Cambridge Dictionary of Sociology"Sets new standards in speaking directly to students of the most significant recent developments in sociology and social changes they are living. It shows how inspiring the sociological imagination can be in areas like media, sexuality, gender relations, inequality, and globalization.—Lyn Spillman, University of Notre Dame"A truly contemporary sociology, one that mines the classics of sociology for insights into a profoundly changed, postmodern world. Most important, the book reminds us of sociology’s capacity to surprise."—Francesca Polletta, University of California–Irvine"An extraordinary textbook that synthesizes a wealth of sociological studies. The book is engaging and readable, key concepts are clearly defined, and important theories are succinctly explicated. I highly recommend it to students and faculty alike."—Will
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