Mathematical and statistical software Books
£50.76
SAS Institute Visual Analytics with SAS Viya: Special Collection
£12.33
SAS Institute The Little SAS Book: A Primer, Sixth Edition
£51.73
SAS Institute SAS Programming for R Users
£22.75
12th Media Services Mathematics for Computer Science
£41.98
12th Media Services Mathematics for Computer Science
£35.95
£76.90
Packt Publishing Limited Practical Discrete Mathematics: Discover math
Book SynopsisA practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data scienceKey Features Apply the math of countable objects to practical problems in computer science Explore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematics Learn complex statistical and mathematical concepts with the help of hands-on examples and expert guidance Book DescriptionDiscrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.What you will learn Understand the terminology and methods in discrete math and their usage in algorithms and data problems Use Boolean algebra in formal logic and elementary control structures Implement combinatorics to measure computational complexity and manage memory allocation Use random variables, calculate descriptive statistics, and find average-case computational complexity Solve graph problems involved in routing, pathfinding, and graph searches, such as depth-first search Perform ML tasks such as data visualization, regression, and dimensionality reduction Who this book is forThis book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.Table of ContentsTable of Contents Key Concepts, Notation, Set Theory, Relations, and Functions Formal Logic and Constructing Mathematical Proofs Computing with Base-n Numbers Combinatorics Using SciPy Elements of Discrete Probability Computational Algorithms in Linear Algebra Computational Requirements for Algorithms Storage and Feature Extraction of Graphs, Trees, and Networks Searching Data Structures and Finding Shortest Paths Regression Analysis with NumPy and Scikit-Learn Web Searches with PageRank Principal Component Analysis with Scikit-Learn
£46.54
Springer London Ltd MATLAB® for Engineers Explained
Book SynopsisBased on the new 'guided-tour' concept that eliminates the start-up transient encountered in learning new programming languages, this beginner's introduction to MATLAB teaches a sufficient subset of the functionality and gives the reader practical experience on how to find more information. Recent developments in MATLAB to advance programming are described using realistic examples in order to prepare students for larger programming projects. In addition, a large number of exercises, tips, and solutions mean that the course can be followed with or without a computer. The development of MATLAB programming and its use in engineering courses makes this a valuable self-study guide for both engineering students and practicing engineers.Trade ReviewFrom the reviews: "The book consists of three parts: an initiation in matlab, more advanced programming in matlab, and some elaborated applications. … The text gives a bottom-up learning-by-example approach. … Thus the reader is forced to sit at the computer and do experiments, which is in my opinion the best and fastest way to learn matlab. The excellent help tool of matlab should do the rest. … The command summaries in the appendices make it … a substitute for the matlab manuals." (Adhemar Bultheel, Bulletin of the Belgian Mathematical Society, Vol. 12 (1), 2005)Table of Contents1 Learning MATLAB.- 1 Introduction.- 2 Interactive computation and elementary functions.- 3 Manipulating matrices.- 4 Strings and workspace administration.- 5 Graphical illustrations.- 6 Matrix algebra and polynomials.- 7 Advanced graphics.- 8 MATLAB Scripts.- 9 MATLAB Functions.- 10 Functions of functions.- 2 Advanced Programming.- 11 Data Structures.- 11.1 Sparse Matrices.- 11.2 Multidimensional Arrays and Cell Arrays.- 11.3 Structs.- 12 Object Orientation.- 13 Graphical Object Orientation and User Interfaces.- 13.1 Graphical objects.- 13.2 Default settings.- 13.3 Graphical User Interface (GUI).- 13.4 Constructing a GUI using guide.- 14 Optimizing MATLAB Code.- 15 Calling C-routines from MATLAB.- 3 Applications of MATLAB.- 16 Calculus.- 17 Data interpolation.- 18 Linear Algebra.- 19 Optimization.- 20 Numerical Accuracy and Number Representation.- 21 Statistics.- 22 Control Theory and the LTI Object.- 23 Dynamical Simulation with SIMULlNK.- 24 Ordinary Differential Equations.- 25 Signal processing.- 26 Communication Systems.- 27 Documentation, presentation and animation.- A Answers to the exercises.- B Command reference.- C Summary of mathematical functions.- D Toolbox Summaries.- E Graphics summary.
£44.99
Springer Nature Switzerland AG An Introduction to Data Analysis in R: Hands-on Coding, Data Mining, Visualization and Statistics from Scratch
Book SynopsisThis textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.Trade Review“It was very interesting to go through the pages of this book. The authors should be commended for writing a thorough book about complex concepts of data analysis in R that could, however, be read easily. I warmly recommend this book to students of statistics but also to professionals who would like to acquire advanced analytical skills or improve their competencies in R, especially nowadays with R very popular amongst data analysts.” (Georgios Nikolopoulos, ISCB News, iscb.info, Issue 71, June, 2021)Table of ContentsPreface.- 1 Introduction.- 2 Introduction to R.- 3 Databases in R.- 4 Visualization.- 5 Data Analysis with R.- R Packages and Funtions.
£59.99
Springer Nature Switzerland AG A Beginner’s Guide to Statistics for Criminology
Book SynopsisThis book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).Table of Contents1. Getting started.2. Managing your data.3. Data visualization.4. Spatiotemporal data visualization and basic crime analysis.5. Descriptive statistics: measures of central tendency.6. Descriptive statistics: measures of dispersion.7. Statistical inference in criminal justice research.8. Defining the observed significance level of a test.9. Hypothesis testing using the binomial distribution.10. Chi-square: a test commonly used for nominal-level measures.11. The normal distribution and its application to tests of statistical significance.12. Comparing means in two samples.13. Analysis of variance.14. Measures of association for nominal and ordinal variables.15. Measuring association for interval data.16. Introduction to regression analysis.
£66.49
Springer Nature Switzerland AG A Course on Small Area Estimation and Mixed
Book SynopsisThis advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians. Table of Contents1 Small Area Estimation.- 2 Design-based Direct Estimation.- 3 Design-based Indirect Estimation.- 4 Prediction Theory.- 5 Linear Models.- 6 Linear Mixed Models.- 7 Nested Error Regression Models.- 8 EBLUPs under Nested Error Regression Models.- 9 Mean Squared Error of EBLUPs.- 10 EBPs under Nested Error Regression Models.- 11 EBLUPs under Two-fold Nested Error Regression Models.- 12 EBPs under Two-fold Nested Error Regression Models.- 13 Random Regression Coefficient Models.- 14 EBPs under Unit-level Logit Mixed Models.- 15 EBPs under Unit-level Two-fold Logit Mixed Models.- 16 Fay-Herriot Models.- 17 Area-level Temporal Linear Mixed Models.- 18 Area-level Spatio-temporal Linear Mixed Models.- 19 Area-level Bivariate Linear Mixed Models.- 20 Area-level Poisson Mixed Models.- 21 Area-level Temporal Poisson Mixed Models.- A Some Useful Formulas.- Index.
£104.49
Springer Nature Switzerland AG Luminescence: Data Analysis and Modeling Using R
Book SynopsisThis book covers applications of R to the general discipline of radiation dosimetry and to the specific areas of luminescence dosimetry, luminescence dating, and radiation protection dosimetry. It features more than 90 detailed worked examples of R code fully integrated into the text, with extensive annotations. The book shows how researchers can use available R packages to analyze their experimental data, and how to extract the various parameters describing mathematically the luminescence signals. In each chapter, the theory behind the subject is summarized, and references are given from the literature, so that researchers can look up the details of the theory and the relevant experiments. Several chapters are dedicated to Monte Carlo methods, which are used to simulate the luminescence processes during the irradiation, heating, and optical stimulation of solids, for a wide variety of materials. This book will be useful to those who use the tools of luminescence dosimetry, including physicists, geologists, archaeologists, and for all researchers who use radiation in their research.Table of Contents1. Introduction.- 2. Analysis and Modeling of TL Data.- 3. Analysis of Experimental OSL Data.- 4. Dose Response of Dosimetric Materials.- 5. Monte Carlo Simulations With Fixed Time Interval.- 6. Luminescence as a Stochastic Life-and-Death Process.- 7. Delocalized Transitions: The R Package RLumCarlo.- 8. Localized Transitions: The R Package RLumCarlo.- 9. Quantum Tunneling and Luminescence Models.- 10. Quantum Tunneling: The R Package RLumCarlo.- 11. Comprehensive Quartz Models Using Program KMS.- 12. Quartz Models Using the R-Package RLumModel.
£66.49
Springer Nature Switzerland AG An Introduction to Bayesian Inference, Methods
Book SynopsisThese 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. Table of ContentsUncertainty and Decisions.- Prior and Likelihood Representation.- Graphical Modeling.- Parametric Models.- Computational Inference.- Bayesian Software Packages.- Model choice.- Linear Models.- Nonparametric Models.- Nonparametric Regression.- Clustering and Latent Factor Models.- Conjugate Parametric Models.
£54.99
De Gruyter Computational Technologies: Advanced Topics
Book SynopsisThis book discusses questions of numerical solutions of applied problems on parallel computing systems. Nowadays, engineering and scientific computations are carried out on parallel computing systems, which provide parallel data processing on a few computing nodes. In the development of up-to-date applied software, this feature of computers must be taken into account for the maximum efficient usage of their resources. In constructing computational algorithms, we should separate relatively independent subproblems in order to solve them on a single computing node.
£43.22
De Gruyter Differential Geometry, Differential Equations, and Special Functions
Book SynopsisThis volume, the third of a series, consists of applications of Mathematica® to a potpourri of more advanced topics. These include differential geometry of curves and surfaces, differential equations and special functions and complex analysis. Some of the newest features of Mathematica® are demonstrated and explained and some problems with the current implementation pointed out and possible future improvements suggested. Contains a large number of worked out examples. Explains some of the most recent mathematical features of Mathematica®. Considers topics discussed rarely or not at all in the context of Mathematica®. Can be used to supplement several different courses. Based on actual university courses.
£56.52
Springer International Publishing AG Practical LaTeX
Book SynopsisPractical LaTeX covers the material that is needed for everyday LaTeX documents. This accessible manual is friendly, easy to read, and is designed to be as portable as LaTeX itself.A short chapter, Mission Impossible, introduces LaTeX documents and presentations. Read these 30 pages; you then should be able to compose your own work in LaTeX. The remainder of the book delves deeper into the topics outlined in Mission Impossible while avoiding technical subjects. Chapters on presentations and illustrations are a highlight, as is the introduction of LaTeX on an iPad.Students, faculty, and professionals in the worlds of mathematics and technology will benefit greatly from this new, practical introduction to LaTeX. George Grätzer, author of More Math into LaTeX (now in its 4th edition) and First Steps in LaTeX, has been a LaTeX guru for over a quarter of century.From the reviews of More Math into LaTeX:``There are several LaTeX guides, but this one wins hands down for the elegance of its approach and breadth of coverage.''—Amazon.com, Best of 2000, Editors Choice``A very helpful and useful tool for all scientists and engineers.''—Review of Astronomical Tools``A novice reader will be able to learn the most essential features of LaTeX sufficient to begin typesetting papers within a few hours of time…An experienced TeX user, on the other hand, will find a systematic and detailed discussion of all LaTeX features, supporting software, and many other advanced technical issues.''—Reports on Mathematical PhysicsTrade ReviewFrom the book reviews:“I’ve been looking for a friendly and accessible manual that I could recommend to students as a way to get over the initial learning curve, and this book seemed like it would fit the bill. … The emphasis is on skills necessary for writing and presenting in an academic setting, and in particular it is geared toward students in math, physics, and numerate disciplines. … Overall, it is a well-presented volume and pleasant to read.” (Sara Kalvala, Computing Reviews, December, 2014)“The book starts with a quick survey, and then explores a bit deeper how to typeset the text, the use of environments, (mathematical) formulas and arrays, and finally the global structure of the document (top matter, body, back matter). … this book might be interesting to read, not only for the beginner, but also for the experienced LaTeX user.” (Adhemar Bultheel, euro-math-soc.eu, December, 2014)“If I really, really have to learn LaTeX, this is the book I’ll go to in a flash. … Even at a first glance or at first browse it’s abundantly clear that this is a very good book for a TeXtyro like me … . It’s eminently practical and therefore eminently worthwhile.” (Michael Berg, MAA Reviews, November, 2014)Table of ContentsIntroduction.- 1. Getting LaTex.- 2. Typing Text.- 3. Text environments.- 4. Typing Formulas.- 5. Displayed Formulas.- 6. Articles.- 7. Making Presentations.- 8. Customization.- 9. The Symbol Tables.- Index.
£27.99
Springer International Publishing AG Regression Modeling Strategies: With Applications
Book SynopsisThis highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques. Trade Review“The aim and scope of this edition to provide graduate students and professional and early career researchers with insights, understandings and working knowledge of regression modelling. … . The book is sequentially organized and well structured and many chapters are self-contained. It includes many useful topics and techniques for graduate .students and researchers alike. This book can be used as a textbook and equally as a reference book.” (Technometrics, Vol. 58 (2), February, 2016)Table of ContentsIntroduction.- General Aspects of Fitting Regression Models.- Missing Data.- Multivariable Modeling Strategies.- Describing, Resampling, Validating and Simplifying the Model.- R Software.- Modeling Longitudinal Responses using Generalized Least Squares.- Case Study in Data Reduction.- Overview of Maximum Likelihood Estimation.- Binary Logistic Regression.- Binary Logistic Regression Case Study 1.- Logistic Model Case Study 2: Survival of Titanic Passengers.- Ordinal Logistic Regression.- Case Study in Ordinal Regression, Data Reduction and Penalization.- Regression Models for Continuous Y and Case Study in Ordinal Regression.- Transform-Both-Sides Regression.- Introduction to Survival Analysis.- Parametric Survival Models.- Case Study in Parametric Survival Modeling and Model Approximation.- Cox Proportional Hazards Regression Model.- Case Study in Cox Regression.- Appendix.
£94.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Das Statistiklabor: R leicht gemacht
Book SynopsisDas Arbeitsbuch führt in die Nutzung der Software Statistiklabor ein. Die Funktionalität wird im ersten Teil detailliert beschrieben, der zweite Teil illustriert Standardauswertungen. Die Software kann kostenfrei unter www.statistiklabor.de heruntergeladen werden. Sie bietet eine interaktive Arbeitsumgebung, um statistische Funktionen und Darstellungsmöglichkeiten leicht und intuitiv bearbeiten zu können, und erlaubt einen wesentlich einfacheren Zugang zu der umfangreichen Funktionalität der Statistik-Programmierumgebung R.Table of ContentsEine erste Beispielauswertung.- Die Oberfläche.- Ein- und Ausgabe.- Statistische Objekte.- Der Kalkulator.- Einiges zu R.- R-Grafik.- Spezielle Aspekte des Labors.- Beschreibung von Daten.- Wahrscheinlichkeitsrechnung.- Stichproben und Punktschätzungen.- Tests und Konfidenzintervalle.- Regression.- Tabellarische Überblicke.- Referenzen von R-Funktionen.- Liste typischer Auswertungen
£24.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Selected Applications of Convex Optimization
Book SynopsisThis book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.Trade Review“Selected Applications of Convex Optimization is a brief book, only 140 pages, and includes exercises with each chapter. It would be a good supplemental text for an optimization or machine learning course.” (John D. Cook, MAA Reviews, maa.org, December, 2015)Table of ContentsPreliminary Knowledge.- Support Vector Machines.- Parameter Estimations.- Norm Approximation and Regulariztion.- Semi-Definite Programing and Linear Matrix Inequalities.- Convex Relaxation.- Geometric Problems.
£44.99
Independently Published Manim User Guide
£12.00
Independently Published R Programming for Beginners
£12.47
Amazon Digital Services LLC - Kdp Mastering Data Science
£24.09
Elsevier Science Publishing Co Inc Mathematica by Example
Book SynopsisTable of Contents1. Getting Started 2. Numbers, Expressions and Functions 3. Calculus 4. Introduction to Lists and Tables 5. Nested Lists: Matrices and Vectors 6. Applications Related to Ordinary and Partial Differential Equations
£84.00
Springer New York Functional and Phylogenetic Ecology in R Use R
Book SynopsisFunctional and Phylogenetic Ecology in R is designed to teach readers to use R for phylogenetic and functional trait analyses. Researchers getting started in R can use this volume as a step-by-step entryway into phylogenetic and functional analyses for ecology in R.Trade ReviewFrom the book reviews:“This book is structured in nine interlinked chapters … . Each chapter is built in a lecture-style incremental manner and does not assume an extensive previous knowledge of R. All chapters conclude with a series of exercises that consolidate the presented notions. This approach makes the book suitable for undergraduates and postgraduates, as well as researchers with an interest in the field. Its structure and detailed examples supported with exercises make it a timely addition for the scientific community.” (Irina Ioana Mohorianu, zbMATH, Vol. 1300, 2015)“This book is based on a course taught by the author and has therefore gone through rigorous user testing, which shows in the clear layout and detailed step-by-step guidance through sophisticated statistical analyses. … Anyone embarking on related research will benefit from this.” (Markus Eichhorn, Frontiers of Biogeography, Vol. 6 (2), 2014)Table of ContentsPreface.- Introduction.- Phylogenetic Data in R.- Phylogenetic Diversity.- Functional Diversity.- Phylogenetic & Functional Beta Diversity.- Null Models.- Comparative Methods & Phylogenetic Signal.- Partitioning the Phylogenetic, Functional, Environmental and Spatial Components of Community Diversity.- Integrating R with Other Phylogenetic and Functional Trait Analytical Software.- References.- Index
£79.99
Taylor & Francis Inc Implementing Reproducible Research
Book SynopsisIn computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden.Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron syTrade Review"This collection brings together the expertise and experience of numerous authors and is likely to be valuable to scientists and statisticians alike. … This book should have broad appeal … introduces some extremely useful tools and practices from leaders in the field. On top of that, it also contains an exciting vision for the future of scientific research. … The challenge of reproducibility in the computational era is being confronted across the sciences, with each field developing its own tools and best practices. This book is an important step in bringing together a broad group of scientists to share what has been learned."—Journal of the American Statistical Association, June 2015 "The book as a whole has something for everybody and provides an interesting snapshot of the available tools, platforms, and good practices for researchers as the scientific community aims to be more self-correcting."—Journal of Statistical Software, October 2014 "Three recent books have significantly influenced how I use R in reproducible work: Dynamic Documents with R and knitr by Yihui Xie, Reproducible Research with R and RStudio by Christopher Gandrud, and Implementing Reproducible Research edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng … I recommend all three books to R users at any level. There really is something here for everyone."—Richard Layton, PhD, PE, Rose-Hulman Institute of Technology, Terre Haute, Indiana, USA "In total, this book provides information on almost all aspects of reproducible research in the open science environment … I would recommend this book to anybody who wants to learn more about reproducible research in the context of open science."—Biometrical Journal Table of ContentsTools: knitr: A Comprehensive Tool for Reproducible Research in R. Reproducibility Using VisTrails. Sumatra: A Toolkit for Reproducible Research. CDE: Automatically Package and Reproduce Computational Experiments. Reproducible Physical Science and the Declaratron. Practices and Guidelines: Developing Open-Source Scientific Practice. Reproducible Bioinformatics Research for Biologists. Reproducible Research for Large-Scale Data Analysis. Practicing Open Science. Reproducibility, Virtual Appliances, and Cloud Computing. The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility—Open Science Collaboration. What Computational Scientists Need to Know about Intellectual Property Law: A Primer. Platforms: Open Science in Machine Learning. RunMyCode.org: A Research-Reproducibility Tool for Computational Sciences. Open Science and the Role of Publishers in Reproducible Research. Index.
£68.39
Springer Us Essentials of Statistics for Scientists and Technologists
Book SynopsisStatistics is of ever-increasing importance in Science and Technology and this book presents the essentials of the subject in a form suitable either as the basis of a course of lectures or to be read and/or used on its own.Table of Contents1 Introduction or ‘What is statistics?’.- 2 The presentation of data.- 3 Probability, its meaning, real and theoretical populations.- 4 Basic properties of the normal distribution.- 5 Some properties of sampling distributions.- 6 Applications of normal sampling theory; significance tests.- 7 Normal sampling theory: test for difference between several sample means, analysis of variance, design of experiments.- 8 Normal sampling theory: estimation of ‘parameters’ by confidence intervals, by maximum likelihood.- 9 The binomial distribution: laws of probability, applications of the binomial distribution, the multinomial distribution.- 10 The Poisson, negative exponential, and rectangular distributions.- 11 The ?2 test for ‘goodness of fit’: test for ‘association’.- 12 Fitting lines and curves to data, least squares method.- 13 Regression curves and lines, correlation coefficient, normal bivariate distribution.- 14 Some distribution-independent (or ‘distribution-free’ or ‘non-parametric’) tests.- 15 Note on sampling techniques and quality control.- 16 Some problems of practical origin.- Answers.
£42.74
Stata Press The Mata Book: A Book for Serious Programmers and
Book SynopsisThe Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge".The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp. Table of ContentsThe mechanics of using Mata. A programmer’s tour of Mata. Mata’s programming statements. Mata’s expressions. Mata’s variable types. Mata’s strict option and Mata’s pragmas. Mata’s function arguments. Programming example: n_choose_k() three ways. Mata’s structures. Programming example: Linear regression. Mata’s classes. Programming example: Linear regression 2. Better variable types. Programming constants. Mata’s associative arrays. Programming example: Sparse matrices. Programming example: Sparse matrices, continued. The Mata Reference Manual. Writing Mata code to add new commands to Stata. Mata’s storage type for complex numbers. How Mata differs from C and C++. Three-dimensional arrays (advanced use of pointers).
£56.99
Springer International Publishing AG Applied Statistical Methods in Agriculture,
Book SynopsisThis textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examples is approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter.Table of ContentsTable of Contents attached as well. Introduction.- Frequency Distributions.- Numerical Description of Data.- Probability and Probability Distributions.- Estimation and Hypothesis Testing.- Regression Analysis.- Categorical Data Analysis.- Experimental Design.- The Completely Randomized Design.- The Randomized Complete Block Design.- Multiple Blocking Designs.- Analysis of Covariance.- Factorial Treatments Designs.- The Split-Plot Design.- Incomplete Block Design.- Quantal-Bioassay.- Repeated Measures Design.- Survival Analysis.
£80.99
Springer New York Monte Carlo Statistical Methods Springer Texts in Statistics
a huge range and FREE tracked UK delivery on ALL orders.
£127.49
Springer Intuitive Probability and Random Processes using
Book SynopsisComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.Trade ReviewFrom the reviews:"The book is composed of 22 chapters. … This is a very readable book. … Kay’s book undoubtedly will see its greatest use in engineering schools, but I think it would work nicely in other settings as well. … It is written in a clear and informal style that students will appreciate, its coverage is excellent, and the author’s stated objective (to lessen the difficulty that students usually experience assimilating and applying probability and random processes) will, I predict, be met." (Ralph P. Russo, The American Statistician, Vol. 62 (2), May, 2008)“Kay’s book occupies a unique place in the overcrowded market of textbooks on probability and random processes. … This new textbook is a breath of fresh air in the market of books devoted to probability and random processes. The book lives up to its ambition of setting a new standard for a modern, computer-based treatment of the subject. … I fully recommend its use in undergraduate and first-year graduate courses.” (Osvaldo Simeone, IEEE Control Systems Magazine, Vol. 27, June, 2007)Table of ContentsComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.
£98.99
John Wiley & Sons Inc SAS For Dummies
Book SynopsisThe fun and easy way to learn to use this leading business intelligence tool Written by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to worTable of ContentsIntroduction. Part I: Welcome to SAS! Chapter 1:Touring the Wonderful World of SAS. Chapter 2: Your Connection to SAS: Using SAS Enterprise Guide. Chapter 3: Six-Minute Abs: Getting Miraculous Results with SAS. Part II: Gathering Data and Presenting Information. Chapter 4: Accessing Data: Oh, the Choices! Chapter 5: Managing Data: I Can Do That? Chapter 6: Show Me a Report in Less Than a Minute. Chapter 7: Graphs: More Value with SAS. Part III: Impressing Your Boss with Your SAS Business Intelligence. Chapter 8: A Painless Introduction to Analytics. Chapter 9: More Analytics to Enlighten and Entertain. Chapter 10: Data Mining: Making the Leap from Guesses to Smart Choices. Part IV: Enhancing and Sharing Your SAS Masterpieces. Chapter 11: Leveraging Work from SAS to Those Less Fortunate. Chapter 12: Use OLAP and Impress Your Coworkers. Chapter 13: Supercharge Microsoft Offi ce with SAS. Chapter 14: Web Reporting Fever: SAS Has That Covered. Part V: Getting SAS Ready to Rock and Roll. Chapter 15: Setting Up SAS. Chapter 16: SAS Programming for the Faint of Heart. Chapter 17: The New World Meets the Old: Programmers and SAS Enterprise Guide. Part VI: The Part of Tens. Chapter 18: Ten SAS Enterprise Guide Productivity Tips. Chapter 19: Ten Tips for Administrators. Chapter 20: Ten (or More) Web Resources for Extra Information. Index.
£22.09
John Wiley & Sons Inc Modern Analysis of Customer Surveys
Book SynopsisModern Analysis of Customer Surveys: with applications using R Customer survey studies deal with customer, consumer and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. This book demonstrates how integrating such basic analysis with more advanced tools, provides insights into non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. Key features: Provides an integrated case studies-based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization's business cycle. Contains classical techniques with modern and non standard tools.Table of ContentsForeword xvii Preface xix Contributors xxiii Part I Basic Aspects of Customer Satisfaction Survey Data Analysis 1 Standards and Classical Techniques in Data Analysis of Customer Satisfaction Surveys 3 Silvia Salini and Ron S. Kenett 1.1 Literature on customer satisfaction surveys 4 1.2 Customer satisfaction surveys and the business cycle 4 1.3 Standards used in the analysis of survey data 7 1.4 Measures and models of customer satisfaction 12 1.4.1 The conceptual construct 12 1.4.2 The measurement process 13 1.5 Organization of the book 15 1.6 Summary 17 References 17 2 The ABC Annual Customer Satisfaction Survey 19 Ron S. Kenett and Silvia Salini 2.1 The ABC company 19 2.2 ABC 2010 ACSS: Demographics of respondents 20 2.3 ABC 2010 ACSS: Overall satisfaction 22 2.4 ABC 2010 ACSS: Analysis of topics 24 2.5 ABC 2010 ACSS: Strengths and weaknesses and decision drivers 27 2.6 Summary 28 References 28 Appendix 29 3 Census and Sample Surveys 37 Giovanna Nicolini and Luciana Dalla Valle 3.1 Introduction 37 3.2 Types of surveys 39 3.2.1 Census and sample surveys 39 3.2.2 Sampling design 40 3.2.3 Managing a survey 40 3.2.4 Frequency of surveys 41 3.3 Non-sampling errors 41 3.3.1 Measurement error 42 3.3.2 Coverage error 42 3.3.3 Unit non-response and non-self-selection errors 43 3.3.4 Item non-response and non-self-selection error 44 3.4 Data collection methods 44 3.5 Methods to correct non-sampling errors 46 3.5.1 Methods to correct unit non-response errors 46 3.5.2 Methods to correct item non-response 49 3.6 Summary 51 References 52 4 Measurement Scales 55 Andrea Bonanomi and Gabriele Cantaluppi 4.1 Scale construction 55 4.1.1 Nominal scale 56 4.1.2 Ordinal scale 57 4.1.3 Interval scale 58 4.1.4 Ratio scale 59 4.2 Scale transformations 60 4.2.1 Scale transformations referred to single items 61 4.2.2 Scale transformations to obtain scores on a unique interval scale 66 Acknowledgements 69 References 69 5 Integrated Analysis 71 Silvia Biffignandi 5.1 Introduction 71 5.2 Information sources and related problems 73 5.2.1 Types of data sources 73 5.2.2 Advantages of using secondary source data 73 5.2.3 Problems with secondary source data 74 5.2.4 Internal sources of secondary information 75 5.3 Root cause analysis 78 5.3.1 General concepts 78 5.3.2 Methods and tools in RCA 81 5.3.3 Root cause analysis and customer satisfaction 85 5.4 Summary 87 Acknowledgement 87 References 87 6 Web Surveys 89 Roberto Furlan and Diego Martone 6.1 Introduction 89 6.2 Main types of web surveys 90 6.3 Economic benefits of web survey research 91 6.3.1 Fixed and variable costs 92 6.4 Non-economic benefits of web survey research 94 6.5 Main drawbacks of web survey research 96 6.6 Web surveys for customer and employee satisfaction projects 100 6.7 Summary 102 References 102 7 The Concept and Assessment of Customer Satisfaction 107 Irena Ograjenšek and Iddo Gal 7.1 Introduction 107 7.2 The quality–satisfaction–loyalty chain 108 7.2.1 Rationale 108 7.2.2 Definitions of customer satisfaction 108 7.2.3 From general conceptions to a measurement model of customer satisfaction 110 7.2.4 Going beyond SERVQUAL: Other dimensions of relevance to the B2B context 112 7.2.5 From customer satisfaction to customer loyalty 113 7.3 Customer satisfaction assessment: Some methodological considerations 115 7.3.1 Rationale 115 7.3.2 Think big: An assessment programme 115 7.3.3 Back to basics: Questionnaire design 116 7.3.4 Impact of questionnaire design on interpretation 118 7.3.5 Additional concerns in the B2B setting 119 7.4 The ABC ACSS questionnaire: An evaluation 119 7.4.1 Rationale 119 7.4.2 Conceptual issues 119 7.4.3 Methodological issues 120 7.4.4 Overall ABC ACSS questionnaire asssessment 121 7.5 Summary 121 References 122 Appendix 126 8 Missing Data and Imputation Methods 129 Alessandra Mattei, Fabrizia Mealli and Donald B. Rubin 8.1 Introduction 129 8.2 Missing-data patterns and missing-data mechanisms 131 8.2.1 Missing-data patterns 131 8.2.2 Missing-data mechanisms and ignorability 132 8.3 Simple approaches to the missing-data problem 134 8.3.1 Complete-case analysis 134 8.3.2 Available-case analysis 135 8.3.3 Weighting adjustment for unit nonresponse 135 8.4 Single imputation 136 8.5 Multiple imputation 138 8.5.1 Multiple-imputation inference for a scalar estimand 138 8.5.2 Proper multiple imputation 139 8.5.3 Appropriately drawing imputations with monotone missing-data patterns 140 8.5.4 Appropriately drawing imputations with nonmonotone missing-data patterns 141 8.5.5 Multiple imputation in practice 142 8.5.6 Software for multiple imputation 143 8.6 Model-based approaches to the analysis of missing data 144 8.7 Addressing missing data in the ABC annual customer satisfaction survey: An example 145 8.8 Summary 149 Acknowledgements 150 References 150 9 Outliers and Robustness for Ordinal Data 155 Marco Riani, Francesca Torti and Sergio Zani 9.1 An overview of outlier detection methods 155 9.2 An example of masking 157 9.3 Detection of outliers in ordinal variables 159 9.4 Detection of bivariate ordinal outliers 160 9.5 Detection of multivariate outliers in ordinal regression 161 9.5.1 Theory 161 9.5.2 Results from the application 163 9.6 Summary 168 References 168 Part II Modern Techniques in Customer Satisfaction Survey Data Analysis 10 Statistical Inference for Causal Effects 173 Fabrizia Mealli, Barbara Pacini and Donald B. Rubin 10.1 Introduction to the potential outcome approach to causal inference 173 10.1.1 Causal inference primitives: Units, treatments, and potential outcomes 175 10.1.2 Learning about causal effects: Multiple units and the stable unit treatment value assumption 176 10.1.3 Defining causal estimands 177 10.2 Assignment mechanisms 179 10.2.1 The criticality of the assignment mechanism 179 10.2.2 Unconfounded and strongly ignorable assignment mechanisms 180 10.2.3 Confounded and ignorable assignment mechanisms 181 10.2.4 Randomized and observational studies 181 10.3 Inference in classical randomized experiments 182 10.3.1 Fisher’s approach and extensions 183 10.3.2 Neyman’s approach to randomization-based inference 183 10.3.3 Covariates, regression models, and Bayesian model-based inference 184 10.4 Inference in observational studies 185 10.4.1 Inference in regular designs 186 10.4.2 Designing observational studies: The role of the propensity score 186 10.4.3 Estimation methods 188 10.4.4 Inference in irregular designs 188 10.4.5 Sensitivity and bounds 189 10.4.6 Broken randomized experiments as templates for the analysis of some irregular designs 189 References 190 11 Bayesian Networks Applied to Customer Surveys 193 Ron S. Kenett, Giovanni Perruca and Silvia Salini 11.1 Introduction to Bayesian networks 193 11.2 The Bayesian network model in practice 197 11.2.1 Bayesian network analysis of the ABC 2010 ACSS 197 11.2.2 Transport data analysis 201 11.2.3 R packages and other software programs used for studying BNs 210 11.3 Prediction and explanation 211 11.4 Summary 213 References 213 12 Log-linear Model Methods 217 Stephen E. Fienberg and Daniel Manrique-Vallier 12.1 Introduction 217 12.2 Overview of log-linear models and methods 218 12.2.1 Two-way tables 218 12.2.2 Hierarchical log-linear models 220 12.2.3 Model search and selection 222 12.2.4 Sparseness in contingency tables and its implications 223 12.2.5 Computer programs for log-linear model analysis 223 12.3 Application to ABC survey data 224 12.4 Summary 227 References 228 13 CUB Models: Statistical Methods and Empirical Evidence 231 Maria Iannario and Domenico Piccolo 13.1 Introduction 231 13.2 Logical foundations and psychological motivations 233 13.3 A class of models for ordinal data 233 13.4 Main inferential issues 236 13.5 Specification of CUB models with subjects’ covariates 238 13.6 Interpreting the role of covariates 240 13.7 A more general sampling framework 241 13.7.1 Objects’ covariates 241 13.7.2 Contextual covariates 243 13.8 Applications of CUB models 244 13.8.1 Models for the ABC annual customer satisfaction survey 245 13.8.2 Students’ satisfaction with a university orientation service 246 13.9 Further generalizations 248 13.10 Concluding remarks 251 Acknowledgements 251 References 251 Appendix 255 A program in R for CUB models 255 A.1 Main structure of the program 255 A.2 Inference on CUB models 255 A.3 Output of CUB models estimation program 256 A.4 Visualization of several CUB models in the parameter space 257 A.5 Inference on CUB models in a multi-object framework 257 A.6 Advanced software support for CUB models 258 14 The Rasch Model 259 Francesca De Battisti, Giovanna Nicolini and Silvia Salini 14.1 An overview of the Rasch model 259 14.1.1 The origins and the properties of the model 259 14.1.2 Rasch model for hierarchical and longitudinal data 263 14.1.3 Rasch model applications in customer satisfaction surveys 265 14.2 The Rasch model in practice 267 14.2.1 Single model 267 14.2.2 Overall model 268 14.2.3 Dimension model 272 14.3 Rasch model software 277 14.4 Summary 278 References 279 15 Tree-based Methods and Decision Trees 283 Giuliano Galimberti and Gabriele Soffritti 15.1 An overview of tree-based methods and decision trees 283 15.1.1 The origins of tree-based methods 283 15.1.2 Tree graphs, tree-based methods and decision trees 284 15.1.3 CART 287 15.1.4 CHAID 293 15.1.5 PARTY 295 15.1.6 A comparison of CART, CHAID and PARTY 297 15.1.7 Missing values 297 15.1.8 Tree-based methods for applications in customer satisfaction surveys 298 15.2 Tree-based methods and decision trees in practice 300 15.2.1 ABC ACSS data analysis with tree-based methods 300 15.2.2 Packages and software implementing tree-based methods 303 15.3 Further developments 304 References 304 16 PLS Models 309 Giuseppe Boari and Gabriele Cantaluppi 16.1 Introduction 309 16.2 The general formulation of a structural equation model 310 16.2.1 The inner model 310 16.2.2 The outer model 312 16.3 The PLS algorithm 313 16.4 Statistical interpretation of PLS 319 16.5 Geometrical interpretation of PLS 320 16.6 Comparison of the properties of PLS and LISREL procedures 321 16.7 Available software for PLS estimation 323 16.8 Application to real data: Customer satisfaction analysis 323 References 329 17 Nonlinear Principal Component Analysis 333 Pier Alda Ferrari and Alessandro Barbiero 17.1 Introduction 333 17.2 Homogeneity analysis and nonlinear principal component analysis 334 17.2.1 Homogeneity analysis 334 17.2.2 Nonlinear principal component analysis 336 17.3 Analysis of customer satisfaction 338 17.3.1 The setting up of indicator 338 17.3.2 Additional analysis 340 17.4 Dealing with missing data 340 17.5 Nonlinear principal component analysis versus two competitors 343 17.6 Application to the ABC ACSS data 344 17.6.1 Data preparation 344 17.6.2 The homals package 345 17.6.3 Analysis on the ‘complete subset’ 346 17.6.4 Comparison of NLPCA with PCA and Rasch analysis 350 17.6.5 Analysis of ‘entire data set’ for the comparison of missing data treatments 352 17.7 Summary 355 References 355 18 Multidimensional Scaling 357 Nadia Solaro 18.1 An overview of multidimensional scaling techniques 357 18.1.1 The origins of MDS models 358 18.1.2 MDS input data 359 18.1.3 MDS models 362 18.1.4 Assessing the goodness of MDS solutions 369 18.1.5 Comparing two MDS solutions: Procrustes analysis 371 18.1.6 Robustness issues in the MDS framework 371 18.1.7 Handling missing values in MDS framework 373 18.1.8 MDS applications in customer satisfaction surveys 373 18.2 Multidimensional scaling in practice 374 18.2.1 Data sets analysed 375 18.2.2 MDS analyses of overall satisfaction with a set of ABC features: The complete data set 375 18.2.3 Weighting objects or items 381 18.2.4 Robustness analysis with the forward search 382 18.2.5 MDS analyses of overall satisfaction with a set of ABC features: The incomplete data set 383 18.2.6 Package and software for MDS methods 384 18.3 Multidimensional scaling in a future perspective 386 18.4 Summary 386 References 387 19 Multilevel Models for Ordinal Data 391 Leonardo Grilli and Carla Rampichini 19.1 Ordinal variables 391 19.2 Standard models for ordinal data 393 19.2.1 Cumulative models 394 19.2.2 Other models 395 19.3 Multilevel models for ordinal data 395 19.3.1 Representation as an underlying linear model with thresholds 396 19.3.2 Marginal versus conditional effects 397 19.3.3 Summarizing the cluster-level unobserved heterogeneity 397 19.3.4 Consequences of adding a covariate 398 19.3.5 Predicted probabilities 399 19.3.6 Cluster-level covariates and contextual effects 399 19.3.7 Estimation of model parameters 400 19.3.8 Inference on model parameters 401 19.3.9 Prediction of random effects 402 19.3.10 Software 403 19.4 Multilevel models for ordinal data in practice: An application to student ratings 404 References 408 20 Quality Standards and Control Charts Applied to Customer Surveys 413 Ron S. Kenett, Laura Deldossi and Diego Zappa 20.1 Quality standards and customer satisfaction 413 20.2 ISO 10004 guidelines for monitoring and measuring customer satisfaction 414 20.3 Control Charts and ISO 7870 417 20.4 Control charts and customer surveys: Standard assumptions 420 20.4.1 Introduction 420 20.4.2 Standard control charts 420 20.5 Control charts and customer surveys: Non-standard methods 426 20.5.1 Weights on counts: Another application of the c chart 426 20.5.2 The χ2 chart 427 20.5.3 Sequential probability ratio tests 428 20.5.4 Control chart over items: A non-standard application of SPC methods 429 20.5.5 Bayesian control chart for attributes: A modern application of SPC methods 432 20.5.6 Control chart for correlated Poisson counts: When things become fairly complicated 433 20.6 The M-test for assessing sample representation 433 20.7 Summary 435 References 436 21 Fuzzy Methods and Satisfaction Indices 439 Sergio Zani, Maria Adele Milioli and Isabella Morlini 21.1 Introduction 439 21.2 Basic definitions and operations 440 21.3 Fuzzy numbers 441 21.4 A criterion for fuzzy transformation of variables 443 21.5 Aggregation and weighting of variables 445 21.6 Application to the ABC customer satisfaction survey data 446 21.6.1 The input matrices 446 21.6.2 Main results 448 21.7 Summary 453 References 455 Appendix an Introduction to R 457 Stefano Maria Iacus A.1 Introduction 457 A.2 How to obtain R 457 A.3 Type rather than ‘point and click’ 458 A.3.1 The workspace 458 A.3.2 Graphics 458 A.3.3 Getting help 459 A.3.4 Installing packages 459 A.4 Objects 460 A.4.1 Assignments 460 A.4.2 Basic object types 462 A.4.3 Accessing objects and subsetting 466 A.4.4 Coercion between data types 469 A.5 S4 objects 470 A.6 Functions 472 A.7 Vectorization 473 A.8 Importing data from different sources 475 A.9 Interacting with databases 476 A.10 Simple graphics manipulation 477 A.11 Basic analysis of the ABC data 481 A.12 About this document 496 A.13 Bibliographical notes 496 References 496 Index 499
£78.26
John Wiley & Sons Inc Regression and ANOVA
Book SynopsisThe information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations. The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided Trade Review“…very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics…” (Zentralblatt Math, Vol.1039, No.8, 2004)Table of ContentsPreface. Examples and Limits of the GLM. Statement of the Model, Estimation, and Testing. Some Distributions for the GLM. Multiple Regression: General Considerations. Testing Hypotheses in Multiple Regression. Correlations. GLM Assumption Diagnostics. GLM Computation Diagnostics. Polynomial Regression. Transformations. Selecting the Best Model. Coding Schemes for Regression. One-Way ANOVA. Complete, Two-Way Factorial ANOVA. Special Cases of Two-Way ANOVA and Random Effects Basics. The Full Model in Every Cell (ANCOVA as a Special Case). Understanding and Computing Power for the GLM. Appendix A. Matrix Algebra for Linear Models. Appendix B. Statistical Tables. Appendix C. Study Guide for Linear Model Theory. Appendix D. Homework and Example Data. Appendix E. Introduction to SAS/IML. Appendix F. A Brief Manual to LINMOD. Appendix G. SAS/IML Power Program User's Guide. Appendix H. Regression Model Selection Data. References. Index.
£95.36
Wiley Statistical Computing An Introduction to Data
Book SynopsisOffers coverage of basic and advanced statistical methods, concentrating on graphical inspection, and featuring step-by-step instruction to help non-statisticians understand the methodology.Trade Review"...suitable as a reference book for experienced statisticians, a vehicle for learning the S statistical computing language, or a resource for statistics instructors..." (The American Statistician, Vol. 58, No. 1, February 2004) "...especially useful as an introduction to a wide variety of data analysis techniques." (R News) "...The book is well written - there is an air of common sense throughout - and is at a level which ensures its usefulness for a wide range of readers..." (Zentralblatt Math, Vol. 1001, No.01, 2003) "...the book is a useful and practical introduction to many areas of statistical data analysis." (Computational STatistics & Data Analysis) "...surely not the last statistics book you’ll ever need, but it might well be the first you will ever really use." (Basic Applied Ecology, Vol. 4, No. 3) "...recommended...contains a wealth of sage advice..." (Technometrics, Vol. 45, No. 4, November 2003) “...a practical introduction to statistics...does not cover all...sophisticated statistical and graphical features of the S-Plus system, but provides a first class starting point—and, probably, for most readers, a sufficient end point.” (Quarterly of Applied Mathematics, LXI, No. 4, December 2003) “…a valiant and useful first attempt to present both statistics and S-PLUS together…” (Journal of The Royal Statistical Society Vol.167 No.4) Table of ContentsStatistical methods Introduction to S-Plus Experimental design Central tendency Probability Variance The Normal distribution Power calculations Understanding data: graphical analysis Understanding data: tabular analysis Classical tests Bootstrap and jackknife Statistical models in S-Plus Regression Analysis of variance Analysis of covariance Model criticism Contrasts Split-plot Anova Nested designs and variance components analysis Graphs, functions and transformations Curve fitting and piecewise regression Non-linear regression Multiple regression Model simplification Probability distributions Generalised linear models Proportion data: binomial errors Count data: Poisson errors Binary response variables Tree models Non-parametric smoothing Survival analysis Time series analysis Mixed effects models Spatial statistics Bibliography Index
£105.26
John Wiley & Sons Inc Introduction to Statistics Through Resampling
Book SynopsisLearn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote.Trade Review“…the books have plenty of wise advice for the application of statistics…” (Bulletin of Mathematical Biology,2007)Table of ContentsPreface. 1. Variation (or What Statistics Is All About). 2. Probability. 3. Distributions. 4. Testing Hypotheses. 5. Designing an Experiment or Survey. 6. Analyzing Complex Experiments. 7. Developing Models. 8. Reporting Your Findings. 9. Problem Solving. Appendix: An Microsoft Office Excel Primer. Index to Excel and Excel Add-In Functions. Subject Index.
£90.86
Princeton University Press Digital Dice
Book SynopsisSome probability problems are so difficult that they stump the smartest mathematicians. But even the hardest of these problems can often be solved with a computer and a Monte Carlo simulation, in which a random-number generator simulates a physical process, such as a million rolls of a pair of dice. This is what Digital Dice is all about: how to geTrade Review"The problems are accessible but still realistic enough to be engaging, and the solutions in the back of the book will get you through any sticky spots. Writing your own versions of a few of these programs will acquaint you with a useful approach to problem solving and a novel style of thinking."--Brian Hayes, American Scientist "Digital Dice will appeal to recreational mathematicians who have even a limited knowledge of computer programming, and even nonprogrammers will find most of the problems entertaining to ponder."--Games Magazine "[An] enjoyable read, as [Nahin] writes clearly, with humour and is not afraid to include equations where necessary. Nahin spices the book throughout with factual and anecdotal snippets. Digital Dice will appeal to all who like recreational mathematics."--Alan Stevens, Mathematics Today "[T]he book is targeted at teachers and students of probability theory or computer science, as well as aficionados of recreational mathematics, but anyone who is familiar with the basics of probability and is capable of writing simple computer programs will have no problem working their way through this interesting and rewarding book."--Physics World "After the appearance of the author's earlier book on probability problems, [Duelling Idiots And Other Probability Puzzlers], one has high expectations for this book, and one is not disappointed... The book will certainly have great appeal to all three of the targeted audiences."--G A. Hewer, Mathematical Reviews "This well-written entertaining collection of twenty-one probability problems presents their origin and history as well as their computer solutions... These problems could be used in a computer programming course or a probability course that includes Monte Carlo simulations."--Thomas Sonnabend, Mathematics Teacher "All of the books by Nahin and Havil are worth having, including others not listed here. I particularly recommend Digital Dice for the task of teaching undergraduates in mathematics the fundamentals of computation and simulation."--James M. Cargal, The UMAP JournalTable of ContentsPreface to the Paperback Edition xiii Introduction 1 The Problems 35 1. The Clumsy Dishwasher Problem 37 2. Will Lil and Bill Meet at the Malt Shop? 38 3. A Parallel Parking Question 40 4. A Curious Coin-Flipping Game 42 5. The Gamow-Stern Elevator Puzzle 45 6. Steve's Elevator Problem 48 7. The Pipe Smoker's Discovery 51 8. A Toilet Paper Dilemma 53 9. The Forgetful Burglar Problem 59 10. The Umbrella Quandary 61 11. The Case of the Missing Senators 63 12. How Many Runners in a Marathon? 65 13. A Police Patrol Problem 69 14. Parrondo's Paradox 74 15. How Long Is the Wait to Get the Potato Salad? 77 16. The Appeals Court Paradox 81 17. Waiting for Buses 83 18. Waiting for Stoplights 85 19. Electing Emperors and Popes 87 20. An Optimal Stopping Problem 91 21. Chain Reactions, Branching Processes, and Baby Boys 96 MATLAB Solutions To The Problems 101 1. The Clumsy Dishwasher Problem 103 2. Will Lil and Bill Meet at the Malt Shop? 105 3. A Parallel Parking Question 109 4. A Curious Coin-Flipping Game 114 5. The Gamow-Stern Elevator Puzzle 120 6. Steve's Elevator Problem 124 7. The Pipe Smoker's Discovery 129 8. A Toilet Paper Dilemma 140 9. The Forgetful Burglar Problem 144 10. The Umbrella Quandary 148 11. The Case of the Missing Senators 153 12. How Many Runners in a Marathon? 157 13. A Police Patrol Problem 160 14. Parrondo's Paradox 169 15. How Long is the Wait to Get the Potato Salad? 176 16. The Appeals Court Paradox 184 17. Waiting for Buses 187 18. Waiting for Stoplights 191 19. Electing Emperors and Popes 197 20. An Optimal Stopping Problem 204 21. Chain Reactions, Branching Processes, and Baby Boys 213 Appendix 1. One Way to Guess on a Test 221 Appendix 2. An Example of Variance-Reduction in the Monte Carlo Method 223 Appendix 3. Random Harmonic Sums 229 Appendix 4. Solving Montmort's Problem by Recursion 231 Appendix 5. An Illustration of the Inclusion-Exclusion Principle 237 Appendix 6. Solutions to the Spin Game 244 Appendix 7. How to Simulate Kelvin's Fair Coin with a Biased Coin 248 Appendix 8. How to Simulate an Exponential Random Variable 252 Appendix 9. Index to Author-Created MATLAB m-Files in the Book 255 Glossary 257 Acknowledgments 259 Index 261 Also by Paul J. Nahin 265
£15.29
Princeton University Press Phylogenetic Comparative Methods in R
Book Synopsis
£40.50
Society for Industrial and Applied Mathematics Insight Through Computing A MATLAB Introduction
Book SynopsisThis introduction to computer-based problem-solving using the MATLAB environment is highly recommended for students wishing to learn the concepts and develop the programming skills that are fundamental to computational science and engineering (CSE). Through a 'teaching by examples' approach, the authors pose strategically chosen problems to help first-time programmers learn these necessary concepts and skills. Each section formulates a problem and then introduces those new MATLAB language features that are necessary to solve it. This approach puts problem-solving and algorithmic thinking first and syntactical details second. Each solution is followed by a 'talking point' that concerns some related, larger issue associated with CSE. Collectively, the worked examples, talking points, and 300+ homework problems build intuition for the process of discretization and an appreciation for dimension, inexactitude, visualization, randomness, and complexity. This sets the stage for further cour
£59.36
John Wiley & Sons Inc Analysis of Biomarker Data
Book SynopsisA how to guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: ATable of ContentsPreface xiii Acknowledgements xvii 1 Introduction 1 1.1 What is a Biomarker? 1 1.2 Biomarkers Versus Surrogate Endpoints 2 1.3 Organization of This Book 3 2 Designing Biomarker Studies 5 2.1 Introduction 5 2.2 Designing the Study 6 2.2.1 The Exposure–Disease Association 6 2.2.2 Cross-sectional Studies 7 2.2.3 Case–Control Studies 7 2.2.4 Retrospective Cohort Studies 9 2.2.5 Prospective Cohort Studies 9 2.2.6 Observational Studies 10 2.2.7 Randomized Controlled Trials 11 2.3 Designing the Analysis 13 2.3.1 Choosing the Appropriate Measure of Association 15 2.3.1.1 Odds Ratio versus Risk Ratio 15 2.3.1.2 Consequences of Not Choosing the Appropriate Measure of Association 16 2.3.2 Choosing the Appropriate Statistical Analysis 16 2.3.3 Choosing the Appropriate Sample Size 17 2.4 Presenting Statistical Results 18 Problems 20 3 Elementary Statistical Methods for Analyzing Biomarker Data 21 3.1 Introduction 21 3.2 Graphical and Tabular Summaries 21 3.3 Descriptive Statistics 26 3.4 Describing the Shape of Distributions 31 3.5 Sampling Distributions 33 3.6 Introduction to Statistical Inference 34 3.6.1 Point Estimation and Confidence Interval Estimation 34 3.6.2 Hypothesis Testing 38 3.7 Comparing Means Across Groups 43 3.7.1 Two Group Comparisons 44 3.7.2 Multiple-Group Comparisons 45 3.8 Correlation Analysis 50 3.9 Regression Analysis 52 3.9.1 Simple Linear Regression 52 3.9.2 Multiple Regression 55 3.9.3 Analysis of Covariance 58 3.10 Analyzing Cross-Classified Data 61 3.10.1 Testing for Independence 61 3.10.2 Comparison of Proportions 65 Problems 69 4 Frequently Encountered Challenges in Analyzing Biomarker Data and How to Deal with Them 72 4.1 Introduction 72 4.2 Non-Normally Distributed Data 73 4.2.1 The Effects of Non-Normality 73 4.2.2 Testing Distributional Assumptions 74 4.2.2.1 Graphical Methods for Assessing Normality 74 4.2.2.2 Measures of Skewness and Kurtosis 81 4.2.2.3 Formal Hypothesis Tests of the Normality Assumption 83 4.2.3 Remedial Measures for Violation of a Distributional Assumption 86 4.2.3.1 Choosing a Transformation 86 4.2.3.2 Using a Robust Statistical Procedure 92 4.2.3.3 Distribution-Free Alternatives 93 4.3 Heterogeneity of Variance 113 4.3.1 The Effects of Heterogeneity 113 4.3.2 The Importance of Heterogeneity in the Comparison of Means 113 4.3.2.1 Comparisons of Two Groups 113 4.3.2.2 Comparisons of More Than Two Groups 116 4.3.2.3 Multiple Comparisons 118 4.4 Dependent Groups 122 4.4.1 The Consequences of Ignoring Dependence Among Groups 122 4.4.2 Comparing Two Dependent Means 124 4.4.2.1 Paired t-test 124 4.4.2.2 Wilcoxon Signed Ranks Test 127 4.4.2.3 Sign Test 128 4.4.3 Tests of Dependent Proportions 134 4.4.3.1 McNemar’s Test 134 4.4.3.2 Cochran’s Q test 138 4.4.3.3 Sample Size and Power Considerations 142 4.5 Correlated Outcomes 144 4.5.1 Choosing the Appropriate Measure of Association 144 4.5.1.1 Spearman’s rho 144 4.5.1.2 Kendall’s tau-b 146 4.5.2 Recommended Methods of Statistical Analysis for Correlation Coefficients 148 4.5.3 Recommended Methods for Interpreting Correlation Coefficient Results 156 4.5.4 Sample Size Issues in Correlation Analysis 157 4.5.5 Comparison of Correlation Coefficients 171 4.5.5.1 Comparison of Independent Correlation Coefficients 172 4.5.5.2 Comparison of Dependent Correlation Coefficients 174 4.5.6 Sample Size Issues When Comparing Two Correlation Coefficients 181 4.5.6.1 Sample Size Issues When Comparing Independent Correlation Coefficients 181 4.5.6.2 Sample Size Issues When Comparing Dependent Correlation Coefficients 183 4.6 Clustered Data 184 4.7 Outliers 199 4.7.1 The Effects of Outliers 199 4.7.2 Detection of Outliers 199 4.7.3 Methods for Accommodating Outliers 207 4.8 Limits of Detection and Non-Detected Observations 208 4.8.1 Statistical Inference When NDs Are Present 210 4.8.2 Maximum Likelihood Estimation of a Correlation Coefficient When Both X and Y Are Subject to Non-Detects 210 4.8.3 Comparison of Confidence Interval Methods for Correlation Coefficients When Both Variables Are Subject to Limits of Detection 212 4.9 The Analysis of Cross-Classified Categorical Data 221 4.9.1 Choosing the Appropriate Measure of Association 221 4.9.1.1 The Odds Ratio 221 4.9.1.2 Risk Ratio 223 4.9.1.3 Risk Difference 224 4.9.1.4 Odds Ratio for Paired Data 225 4.9.2 Choosing the Appropriate Statistical Analysis 225 4.9.3 Choosing the Appropriate Sample Size 226 4.9.4 Choosing a Statistical Method When Both the Predictor and the Outcome Are Dichotomous 226 4.9.4.1 Comparing Two Independent Groups in Terms of a Binomial Proportion 226 4.9.4.2 Exact Test for Independence of Rows and Columns in a 2 × 2 Table 230 4.9.4.3 Exact Inference for Odds Ratios 232 4.9.4.4 Inference for the Odds Ratio for Paired Data 234 4.9.5 Choice of a Statistical Method When the Predictor is Ordinal and the Outcome is Dichotomous 237 4.9.5.1 Tests for a Significant Trend in Proportions 237 4.9.6 Choice of a Statistical Method When Both the Predictor and the Outcome are Ordinal 240 4.9.6.1 Test for Linear-by-Linear Association 240 4.9.7 Choice of a Statistical Method When Both the Predictor and the Outcome are Nominal 243 4.9.7.1 Fisher–Freeman–Halton Test 243 Problems 246 5 Validation of Biomarkers 255 5.1 Overview of Methods for Assessing Characteristics of Biomarkers 255 5.2 General Description of Measures of Agreement 257 5.2.1 Discrete Variables 257 5.2.1.1 Cohen’s Kappa 257 5.2.1.2 Extensions of Coefficient Kappa 265 5.2.1.3 Weighted Kappa 273 5.2.2 Continuous Variables 275 5.2.2.1 Pearson’s Correlation Coefficient 275 5.2.2.2 Alternatives to Pearson’s Correlation Coefficient 277 5.3 Assessing Reliability of a Biomarker 287 5.3.1 General Considerations 287 5.3.2 Assessing Reliability of a Dichotomous Biomarker 287 5.3.2.1 Dichotomous Biomarker, More Than Two Raters 289 5.3.3 Assessing Reliability of a Continuous Biomarker 291 5.3.4 Assessing Inter-Subject, Intra-Subject, and Analytical Measurement Variability 292 5.4 Assessing Validity 294 5.4.1 General Considerations 294 5.4.2 Assessing Validity When a Gold Standard is Available 295 5.4.2.1 Dichotomous Biomarkers 295 5.4.2.2 Comparing Several Dichotomous Biomarkers 302 5.4.2.3 Continuous Biomarkers 304 5.4.3 Assessing Validity When a Gold Standard is Not Available 314 5.4.3.1 Dichotomous Biomarkers 315 5.4.3.2 Continuous Biomarkers 319 5.4.4 Assessing Criterion Validity in Method Comparison Studies 328 5.4.5 Assessing Construct Validity in Method Comparison Studies 329 Problems 329 References 332 Solutions to Problems 348 Index 391
£99.86
John Wiley & Sons Inc Nonlinear Parameter Optimization Using R Tools
Book SynopsisNonlinear Parameter Optimization Using R John C.Trade Review"The book chapters are enriched by little anecdotes, and the reader obviously benefits from John C. Nash's experience of more than 30 years in the field of nonlinear optimization. This experience translates into many practical recommendations and tweaks. The book provides plenty of code examples and useful code snippets." (Biometrical Journal, 2016)Table of ContentsPreface xv 1 Optimization problem tasks and how they arise 1 1.1 The general optimization problem 1 1.2 Why the general problem is generally uninteresting 2 1.3 (Non-)Linearity 4 1.4 Objective function properties 4 1.4.1 Sums of squares 4 1.4.2 Minimax approximation 5 1.4.3 Problems with multiple minima 5 1.4.4 Objectives that can only be imprecisely computed 5 1.5 Constraint types 5 1.6 Solving sets of equations 6 1.7 Conditions for optimality 7 1.8 Other classifications 7 References 8 2 Optimization algorithms – an overview 9 2.1 Methods that use the gradient 9 2.2 Newton-like methods 12 2.3 The promise of Newton’s method 13 2.4 Caution: convergence versus termination 14 2.5 Difficulties with Newton’s method 14 2.6 Least squares: Gauss–Newton methods 15 2.7 Quasi-Newton or variable metric method 17 2.8 Conjugate gradient and related methods 18 2.9 Other gradient methods 19 2.10 Derivative-free methods 19 2.10.1 Numerical approximation of gradients 19 2.10.2 Approximate and descend 19 2.10.3 Heuristic search 20 2.11 Stochastic methods 20 2.12 Constraint-based methods – mathematical programming 21 References 22 3 Software structure and interfaces 25 3.1 Perspective 25 3.2 Issues of choice 26 3.3 Software issues 27 3.4 Specifying the objective and constraints to the optimizer 28 3.5 Communicating exogenous data to problem definition functions 28 3.5.1 Use of “global” data and variables 31 3.6 Masked (temporarily fixed) optimization parameters 32 3.7 Dealing with inadmissible results 33 3.8 Providing derivatives for functions 34 3.9 Derivative approximations when there are constraints 36 3.10 Scaling of parameters and function 36 3.11 Normal ending of computations 36 3.12 Termination tests – abnormal ending 37 3.13 Output to monitor progress of calculations 37 3.14 Output of the optimization results 38 3.15 Controls for the optimizer 38 3.16 Default control settings 39 3.17 Measuring performance 39 3.18 The optimization interface 39 References 40 4 One-parameter root-finding problems 41 4.1 Roots 41 4.2 Equations in one variable 42 4.3 Some examples 42 4.3.1 Exponentially speaking 42 4.3.2 A normal concern 44 4.3.3 Little Polly Nomial 46 4.3.4 A hypothequial question 49 4.4 Approaches to solving 1D root-finding problems 51 4.5 What can go wrong? 52 4.6 Being a smart user of root-finding programs 54 4.7 Conclusions and extensions 54 References 55 5 One-parameter minimization problems 56 5.1 The optimize() function 56 5.2 Using a root-finder 57 5.3 But where is the minimum? 58 5.4 Ideas for 1D minimizers 59 5.5 The line-search subproblem 61 References 62 6 Nonlinear least squares 63 6.1 nls() from package stats 63 6.1.1 A simple example 63 6.1.2 Regression versus least squares 65 6.2 A more difficult case 65 6.3 The structure of the nls() solution 72 6.4 Concerns with nls() 73 6.4.1 Small residuals 74 6.4.2 Robustness – “singular gradient” woes 75 6.4.3 Bounds with nls() 77 6.5 Some ancillary tools for nonlinear least squares 79 6.5.1 Starting values and self-starting problems 79 6.5.2 Converting model expressions to sum-of-squares functions 80 6.5.3 Help for nonlinear regression 80 6.6 Minimizing Rfunctions that compute sums of squares 81 6.7 Choosing an approach 82 6.8 Separable sums of squares problems 86 6.9 Strategies for nonlinear least squares 93 References 93 7 Nonlinear equations 95 7.1 Packages and methods for nonlinear equations 95 7.1.1 BB 96 7.1.2 nleqslv 96 7.1.3 Using nonlinear least squares 96 7.1.4 Using function minimization methods 96 7.2 A simple example to compare approaches 97 7.3 A statistical example 103 References 106 8 Function minimization tools in the base R system 108 8.1 optim() 108 8.2 nlm() 110 8.3 nlminb() 111 8.4 Using the base optimization tools 112 References 114 9 Add-in function minimization packages for R 115 9.1 Package optimx 115 9.1.1 Optimizers in optimx 116 9.1.2 Example use of optimx() 117 9.2 Some other function minimization packages 118 9.2.1 nloptr and nloptwrap 118 9.2.2 trust and trustOptim 119 9.3 Should we replace optim() routines? 121 References 122 10 Calculating and using derivatives 123 10.1 Why and how 123 10.2 Analytic derivatives – by hand 124 10.3 Analytic derivatives – tools 125 10.4 Examples of use of R tools for differentiation 125 10.5 Simple numerical derivatives 127 10.6 Improved numerical derivative approximations 128 10.6.1 The Richardson extrapolation 128 10.6.2 Complex-step derivative approximations 128 10.7 Strategy and tactics for derivatives 129 References 131 11 Bounds constraints 132 11.1 Single bound: use of a logarithmic transformation 132 11.2 Interval bounds: Use of a hyperbolic transformation 133 11.2.1 Example of the tanh transformation 134 11.2.2 A fly in the ointment 134 11.3 Setting the objective large when bounds are violated 135 11.4 An active set approach 136 11.5 Checking bounds 138 11.6 The importance of using bounds intelligently 138 11.6.1 Difficulties in applying bounds constraints 139 11.7 Post-solution information for bounded problems 139 Appendix 11.A Function transfinite 141 References 142 12 Using masks 143 12.1 An example 143 12.2 Specifying the objective 143 12.3 Masks for nonlinear least squares 147 12.4 Other approaches to masks 148 References 148 13 Handling general constraints 149 13.1 Equality constraints 149 13.1.1 Parameter elimination 151 13.1.2 Which parameter to eliminate? 153 13.1.3 Scaling and centering? 154 13.1.4 Nonlinear programming packages 154 13.1.5 Sequential application of an increasing penalty 156 13.2 Sumscale problems 158 13.2.1 Using a projection 162 13.3 Inequality constraints 163 13.4 A perspective on penalty function ideas 167 13.5 Assessment 167 References 168 14 Applications of mathematical programming 169 14.1 Statistical applications of math programming 169 14.2 R packages for math programming 170 14.3 Example problem: L1 regression 171 14.4 Example problem: minimax regression 177 14.5 Nonlinear quantile regression 179 14.6 Polynomial approximation 180 References 183 15 Global optimization and stochastic methods 185 15.1 Panorama of methods 185 15.2 R packages for global and stochastic optimization 186 15.3 An example problem 187 15.3.1 Method SANN from optim() 187 15.3.2 Package GenSA 188 15.3.3 Packages DEoptim and RcppDE 189 15.3.4 Package smco 191 15.3.5 Package soma 192 15.3.6 Package Rmalschains 193 15.3.7 Package rgenoud 193 15.3.8 Package GA 194 15.3.9 Package gaoptim 195 15.4 Multiple starting values 196 References 202 16 Scaling and reparameterization 203 16.1 Why scale or reparameterize? 203 16.2 Formalities of scaling and reparameterization 204 16.3 Hobbs’ weed infestation example 205 16.4 The KKT conditions and scaling 210 16.5 Reparameterization of the weeds problem 214 16.6 Scale change across the parameter space 214 16.7 Robustness of methods to starting points 215 16.7.1 Robustness of optimization techniques 218 16.7.2 Robustness of nonlinear least squares methods 220 16.8 Strategies for scaling 222 References 223 17 Finding the right solution 224 17.1 Particular requirements 224 17.1.1 A few integer parameters 225 17.2 Starting values for iterative methods 225 17.3 KKT conditions 226 17.3.1 Unconstrained problems 226 17.3.2 Constrained problems 227 17.4 Search tests 228 References 229 18 Tuning and terminating methods 230 18.1 Timing and profiling 230 18.1.1 rbenchmark 231 18.1.2 microbenchmark 231 18.1.3 Calibrating our timings 232 18.2 Profiling 234 18.2.1 Trying possible improvements 235 18.3 More speedups of R computations 238 18.3.1 Byte-code compiled functions 238 18.3.2 Avoiding loops 238 18.3.3 Package upgrades - an example 239 18.3.4 Specializing codes 241 18.4 External language compiled functions 242 18.4.1 Building an R function using Fortran 244 18.4.2 Summary of Rayleigh quotient timings 246 18.5 Deciding when we are finished 247 18.5.1 Tests for things gone wrong 248 References 249 19 Linking R to external optimization tools 250 19.1 Mechanisms to link R to external software 251 19.1.1 R functions to call external (sub)programs 251 19.1.2 File and system call methods 251 19.1.3 Thin client methods 252 19.2 Prepackaged links to external optimization tools 252 19.2.1 NEOS 252 19.2.2 Automatic Differentiation Model Builder (ADMB) 252 19.2.3 NLopt 253 19.2.4 BUGS and related tools 253 19.3 Strategy for using external tools 253 References 254 20 Differential equation models 255 20.1 The model 255 20.2 Background 256 20.3 The likelihood function 258 20.4 A first try at minimization 258 20.5 Attempts with optimx 259 20.6 Using nonlinear least squares 260 20.7 Commentary 261 Reference 262 21 Miscellaneous nonlinear estimation tools for R 263 21.1 Maximum likelihood 263 21.2 Generalized nonlinear models 266 21.3 Systems of equations 268 21.4 Additional nonlinear least squares tools 268 21.5 Nonnegative least squares 270 21.6 Noisy objective functions 273 21.7 Moving forward 274 References 275 Appendix A R packages used in examples 276 Index 279
£56.00
John Wiley & Sons Inc Engineering Applications
Book SynopsisENGINEERING APPLICATIONS A comprehensive text on the fundamental principles of mechanical engineering Engineering Applications presents the fundamental principles and applications of the statics and mechanics of materials in complex mechanical systems design. Using MATLAB to help solve problems with numerical and analytical calculations, authors and noted experts on the topic Mihai Dupac and Dan B. Marghitu offer an understanding of the static behaviour of engineering structures and components while considering the mechanics of materials knowledge as the most important part of their design. The authors explore the concepts, derivations, and interpretations of general principles and discuss the creation of mathematical models and the formulation of mathematical equations. This practical text also highlights the solutions of problems solved analytically and numerically using MATLAB. The figures generated with MATLAB reinforce visual learning for students andTable of Contents1 Forces 1 1.1 Terminology and Notation 1 1.2 Resolution of Forces 3 1.3 Angle Between Two Forces 3 1.4 Force Vector 4 1.5 Scalar (Dot) Product of Two Forces 5 1.6 Cross Product of Two Forces 5 1.7 Examples 6 2 Moments and Couples 15 2.1 Types of Moments 15 2.2 Moment of a Force About a Point 15 2.3 Moment of a Force About a Line 18 2.4 Couples 20 2.5 Examples 21 3 Equilibrium of Structures 55 3.1 Equilibrium Equations 55 3.2 Supports 57 3.3 Free-Body Diagrams 59 3.4 Two-Force and Three-Force Members 60 3.5 Plane Trusses 61 3.6 Analysis of Simple Trusses 62 3.6.1 Method of Joints 62 3.6.2 Method of Sections 65 3.7 Examples 67 4 Centroids and Moments of Inertia 129 4.1 Centre of the Mass and Centroid 129 4.2 Centroid and Centre of the Mass of a Solid Region, Surface or Curve 130 4.3 Method of Decomposition 134 4.4 First Moment of an Area 134 4.5 The Centre of Gravity 135 4.6 Examples 136 5 Stress, Strain and Deflection 185 5.1 Stress 185 5.2 Elastic Strain 185 5.3 Shear and Moment 186 5.4 Deflections of Beams 189 5.5 Examples 193 6 Friction 211 6.1 Coefficient of Static Friction 212 6.2 Coefficient of Kinetic Friction 213 6.3 Friction Models 213 6.3.1 Coulomb Friction Model 214 6.3.2 Coulomb Model with Viscous Friction 216 6.3.3 Coulomb Model with Stiction 217 6.4 Angle of Friction 218 6.5 Examples 219 7 Work, Energy and Power 255 7.1 Work 255 7.2 Kinetic Energy 256 7.3 Work and Power 258 7.4 Conservative Forces 259 7.5 Work Done by the Gravitational Force 259 7.6 Work Done by the Friction Force 260 7.7 Potential Energy and Conservation of Energy 261 7.8 Work Done and Potential Energy of an Elastic Force 261 7.9 Potential Energy Due to the Gravitational Force 262 7.9.1 Potential Energy Due to the Gravitational Force for a Particle 262 7.9.2 Potential Energy Due to the Gravitational Force for a Rigid Body 263 7.10 Examples 264 8 Simple Machines 295 8.1 Load and Effort, Mechanical Advantage, Velocity Ratio and Efficiency of a Simple Machine 295 8.1.1 Load and Effort 295 8.1.2 Mechanical Advantage 296 8.1.3 Velocity Ratio and Efficiency 296 8.2 Effort and Load of an Ideal Machine 297 8.3 The Lever 297 8.4 Inclined Plane (Wedge) 298 8.5 Screws 299 8.6 Simple Screwjack 299 8.6.1 Motion Impending Upwards 301 8.6.2 Motion Impending Downwards 302 8.6.3 Efficiency While Hoisting Load 303 8.7 Differential Screwjack 303 8.8 Pulleys 304 8.8.1 First-order Pulley System 304 8.8.2 Second-order Pulley System 306 8.8.3 Third-order Pulley System 307 8.9 Differential Pulley 308 8.10 Wheel and Axle 309 8.11 Wheel and Differential Axle 310 8.12 Examples 312 References 353 Index 357
£75.56
John Wiley & Sons Inc Statistics with JMP Hypothesis Tests ANOVA and
Book SynopsisStatistics with JMP: Hypothesis Tests, ANOVA and Regression Peter Goos, University of Leuven and University of Antwerp, Belgium David Meintrup, University of Applied Sciences Ingolstadt, Germany A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). Discusses the power of various statistical tests, along with examples in JMP to Trade Review"Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering." (Zentralblatt MATH 2016)Table of ContentsDedication iii Preface xiii Acknowledgements xvii Part One Estimators and tests 1 1 Estimating population parameters 3 2 Interval estimators 37 3 Hypothesis tests 71 Part Two One population 103 4 Hypothesis tests for a population mean, proportion or variance 105 5 Two hypothesis tests for the median of a population 149 6 Hypothesis tests for the distribution of a population 175 Part Three Two populations 7 Independent versus paired samples 213 8 Hypothesis tests for means, proportions and variances of two independent samples 219 9 A nonparametric hypothesis test for the medians of two independent samples 263 10 Hypothesis tests for the population mean of two paired samples 285 11 Two nonparametric hypothesis tests for paired samples 305 Part Four More than two populations 325 12 Hypothesis tests for more than two population means: one-way analysis of variance 327 13 Nonparametric alternatives to an analysis of variance 375 14 Hypothesis tests for more than two population variances 401 Part Five More useful tests and procedures 417 15 Design of experiments and data collection 419 16 Testing equivalence 427 17 Estimation and testing of correlation and association 445 18 An introduction to regression modeling 481 19 Simple linear regression 493 A Binomial distribution 589 B Standard normal distribution 593 C X2-distribution 595 D Student’s t-distribution 597 E Wilcoxon signed-rank test 599 F Critical values for the Shapiro-Wilk test 605 G Fisher’s F-distribution 607 H Wilcoxon rank-sum test 615 I Studentized range or Q-distribution 625 J Two-sided Dunnett test 629 K One-sided Dunnett test 633 L Kruskal-Wallis-Test 637 M Rank correlation test 641 Index 643
£57.90
John Wiley & Sons Inc Financial Risk Modelling and Portfolio
Book SynopsisA must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk coTable of ContentsPreface to the Second Edition xi Preface xiii Abbreviations xv About the Companion Website xix PART I MOTIVATION 1 1 Introduction 3 Reference 5 2 A brief course in R 6 2.1 Origin and development 6 2.2 Getting help 7 2.3 Working with R 10 2.4 Classes, methods, and functions 12 2.5 The accompanying package FRAPO 22 References 28 3 Financial market data 29 3.1 Stylized facts of financial market returns 29 3.1.1 Stylized facts for univariate series 29 3.1.2 Stylized facts for multivariate series 32 3.2 Implications for risk models 35 References 36 4 Measuring risks 37 4.1 Introduction 37 4.2 Synopsis of risk measures 37 4.3 Portfolio risk concepts 42 References 44 5 Modern portfolio theory 46 5.1 Introduction 46 5.2 Markowitz portfolios 47 5.3 Empirical mean-variance portfolios 50 References 52 PART II RISK MODELLING 55 6 Suitable distributions for returns 57 6.1 Preliminaries 57 6.2 The generalized hyperbolic distribution 57 6.3 The generalized lambda distribution 60 6.4 Synopsis of R packages for GHD 66 6.4.1 The package fBasics 66 6.4.2 The package GeneralizedHyperbolic 67 6.4.3 The package ghyp 69 6.4.4 The package QRM 70 6.4.5 The package SkewHyperbolic 70 6.4.6 The package VarianceGamma 71 6.5 Synopsis of R packages for GLD 71 6.5.1 The package Davies 71 6.5.2 The package fBasics 72 6.5.3 The package gld 73 6.5.4 The package lmomco 73 6.6 Applications of the GHD to risk modelling 74 6.6.1 Fitting stock returns to the GHD 74 6.6.2 Risk assessment with the GHD 77 6.6.3 Stylized facts revisited 80 6.7 Applications of the GLD to risk modelling and data analysis 82 6.7.1 VaR for a single stock 82 6.7.2 Shape triangle for FTSE 100 constituents 84 References 86 7 Extreme value theory 89 7.1 Preliminaries 89 7.2 Extreme value methods and models 90 7.2.1 The block maxima approach 90 7.2.2 The rth largest order models 91 7.2.3 The peaks-over-threshold approach 92 7.3 Synopsis of R packages 94 7.3.1 The package evd 94 7.3.2 The package evdbayes 95 7.3.3 The package evir 96 7.3.4 The packages extRemes and in2extRemes 98 7.3.5 The package fExtremes 99 7.3.6 The package ismev 101 7.3.7 The package QRM 101 7.3.8 The packages Renext and RenextGUI 102 7.4 Empirical applications of EVT 103 7.4.1 Section outline 103 7.4.2 Block maxima model for Siemens 103 7.4.3 r-block maxima for BMW 107 7.4.4 POT method for Boeing 110 References 115 8 Modelling volatility 116 8.1 Preliminaries 116 8.2 The class of ARCH models 116 8.3 Synopsis of R packages 120 8.3.1 The package bayesGARCH 120 8.3.2 The package ccgarch 121 8.3.3 The package fGarch 122 8.3.4 The package GEVStableGarch 122 8.3.5 The package gogarch 123 8.3.6 The package lgarch 123 8.3.7 The packages rugarch and rmgarch 125 8.3.8 The package tseries 127 8.4 Empirical application of volatility models 128 References 130 9 Modelling dependence 133 9.1 Overview 133 9.2 Correlation, dependence, and distributions 133 9.3 Copulae 136 9.3.1 Motivation 136 9.3.2 Correlations and dependence revisited 137 9.3.3 Classification of copulae 139 9.4 Synopsis of R packages 142 9.4.1 The package BLCOP 142 9.4.2 The package copula 144 9.4.3 The package fCopulae 146 9.4.4 The package gumbel 147 9.4.5 The package QRM 148 9.5 Empirical applications of copulae 148 9.5.1 GARCH–copula model 148 9.5.2 Mixed copula approaches 155 References 157 PART III PORTFOLIO OPTIMIZATION APPROACHES 161 10 Robust portfolio optimization 163 10.1 Overview 163 10.2 Robust statistics 164 10.2.1 Motivation 164 10.2.2 Selected robust estimators 165 10.3 Robust optimization 168 10.3.1 Motivation 168 10.3.2 Uncertainty sets and problem formulation 168 10.4 Synopsis of R packages 174 10.4.1 The package covRobust 174 10.4.2 The package fPortfolio 174 10.4.3 The package MASS 175 10.4.4 The package robustbase 176 10.4.5 The package robust 176 10.4.6 The package rrcov 178 10.4.7 Packages for solving SOCPs 179 10.5 Empirical applications 180 10.5.1 Portfolio simulation: robust versus classical statistics 180 10.5.2 Portfolio back test: robust versus classical statistics 186 10.5.3 Portfolio back-test: robust optimization 190 References 195 11 Diversification reconsidered 198 11.1 Introduction 198 11.2 Most-diversified portfolio 199 11.3 Risk contribution constrained portfolios 201 11.4 Optimal tail-dependent portfolios 204 11.5 Synopsis of R packages 207 11.5.1 The package cccp 207 11.5.2 The packages DEoptim, DEoptimR, and RcppDE 207 11.5.3 The package FRAPO 210 11.5.4 The package PortfolioAnalytics 211 11.6 Empirical applications 212 11.6.1 Comparison of approaches 212 11.6.2 Optimal tail-dependent portfolio against benchmark 216 11.6.3 Limiting contributions to expected shortfall 221 References 226 12 Risk-optimal portfolios 228 12.1 Overview 228 12.2 Mean-VaR portfolios 229 12.3 Optimal CVaR portfolios 234 12.4 Optimal draw-down portfolios 238 12.5 Synopsis of R packages 241 12.5.1 The package fPortfolio 241 12.5.2 The package FRAPO 243 12.5.3 Packages for linear programming 245 12.5.4 The package PerformanceAnalytics 249 12.6 Empirical applications 251 12.6.1 Minimum-CVaR versus minimum-variance portfolios 251 12.6.2 Draw-down constrained portfolios 254 12.6.3 Back-test comparison for stock portfolio 260 12.6.4 Risk surface plots 265 References 272 13 Tactical asset allocation 274 13.1 Overview 274 13.2 Survey of selected time series models 275 13.2.1 Univariate time series models 275 13.2.2 Multivariate time series models 281 13.3 The Black–Litterman approach 289 13.4 Copula opinion and entropy pooling 292 13.4.1 Introduction 292 13.4.2 The COP model 292 13.4.3 The EP model 293 13.5 Synopsis of R packages 295 13.5.1 The package BLCOP 295 13.5.2 The package dse 297 13.5.3 The package fArma 300 13.5.4 The package forecast 301 13.5.5 The package MSBVAR 302 13.5.6 The package PortfolioAnalytics 304 13.5.7 The packages urca and vars 304 13.6 Empirical applications 307 13.6.1 Black–Litterman portfolio optimization 307 13.6.2 Copula opinion pooling 313 13.6.3 Entropy pooling 318 13.6.4 Protection strategies 324 References 334 14 Probabilistic utility 339 14.1 Overview 339 14.2 The concept of probabilistic utility 340 14.3 Markov chain Monte Carlo 342 14.3.1 Introduction 342 14.3.2 Monte Carlo approaches 343 14.3.3 Markov chains 347 14.3.4 Metropolis–Hastings algorithm 349 14.4 Synopsis of R packages 354 14.4.1 Packages for conducting MCMC 354 14.4.2 Packages for analyzing MCMC 358 14.5 Empirical application 362 14.5.1 Exemplary utility function 362 14.5.2 Probabilistic versus maximized expected utility 366 14.5.3 Simulation of asset allocations 369 References 375 Appendix A Package overview 378 A.1 Packages in alphabetical order 378 A.2 Packages ordered by topic 382 References 386 Appendix B Time series data 391 B.1 Date/time classes 391 B.2 The ts class in the base package stats 395 B.3 Irregularly spaced time series 395 B.4 The package timeSeries 397 B.5 The package zoo 399 B.6 The packages tframe and xts 401 References 404 Appendix C Back-testing and reporting of portfolio strategies 406 C.1 R packages for back-testing 406 C.2 R facilities for reporting 407 C.3 Interfacing with databases 407 References 408 Appendix D Technicalities 411 Reference 411 Index 413
£63.86
John Wiley & Sons Inc Sports Research with Analytical Solution using
Book SynopsisA step-by-step approach to problem-solving techniques using SPSS in the fields of sports science and physical education Featuring a clear and accessible approach to the methods, processes, and statistical techniques used in sports science and physical education, Sports Research with Analytical Solution using SPSS emphasizes how to conduct and interpret a range of statistical analysis using SPSS. The book also addresses issues faced by research scholars in these fields by providing analytical solutions to various research problems without reliance on mathematical rigor. Logically arranged to cover both fundamental and advanced concepts, the book presents standard univariate and complex multivariate statistical techniques used in sports research such as multiple regression analysis, discriminant analysis, cluster analysis, and factor analysis. The author focuses on the treatment of various parametric and nonparametric statistical tests, which are shown throTable of ContentsPreface xv About the Companion Website xviii Acknowledgments xix 1 Introduction to Data Types and SPSS Operations 1 1.1 Introduction 1 1.2 Types of data 2 1.2.1 Qualitative Data 2 1.2.2 Quantitative Data 3 1.3 Important definitions 4 1.3.1 Variable 4 1.4 Data Cleaning 4 1.5 Detection of Errors 5 1.5.1 Using Frequencies 5 1.5.2 Using Mean and Standard Deviation 5 1.5.3 Logic Checks 5 1.5.4 Outlier Detection 5 1.6 How to Start Spss? 6 1.6.1 Preparing Data File 7 1.7 Exercise 10 1.7.1 Short Answer Questions 10 1.7.2 Multiple Choice Questions 11 2 Descriptive Profile 14 2.1 Introduction 14 2.2 Explanation of Various Descriptive Statistics 16 2.2.1 Mean 16 2.2.2 Variance 16 2.2.3 Standard Error of Mean 17 2.2.4 Skewness 17 2.2.5 Kurtosis 18 2.2.6 Percentiles 19 2.3 Application of Descriptive Statistics 19 2.3.1 Testing Normality of Data and Identifying Outliers 20 2.4 Computation of Descriptive Statistics Using Spss 25 2.4.1 Preparation of Data File 25 2.4.2 Defining Variables 26 2.4.3 Entering Data 26 2.4.4 SPSS Commands 26 2.5 Interpretations of the Results 29 2.6 Developing Profile Chart 31 2.7 Summary of Spss Commands 33 2.8 Exercise 33 2.8.1 Short Answer Questions 33 2.8.2 Multiple Choice Questions 34 2.9 Case Study on Descriptive Analysis 36 3 Correlation Coefficient and Partial Correlation 41 3.1 Introduction 41 3.2 Correlation Matrix and Partial Correlation 43 3.2.1 Product Moment Correlation Coefficient 43 3.2.2 Partial Correlation 45 3.3 Application of Correlation Matrix and Partial Correlation 46 3.4 Correlation Matrix with Spss 46 3.4.1 Computation in Correlation Matrix 46 3.4.2 Interpretations of Findings 51 3.5 Partial Correlation with Spss 51 3.5.1 Computation of Partial Correlations 52 3.5.2 Interpretation of Partial Correlation 55 3.6 Summary of the Spss Commands 56 3.6.1 For Computing Correlation Matrix 56 3.6.2 For Computing Partial Correlations 57 3.7 Exercise 57 3.7.1 Short Answer Questions 57 3.7.2 Multiple Choice Questions 57 3.7.3 Assignment 60 3.8 Case Study on Correlation 60 4 Comparing Means 65 4.1 Introduction 65 4.2 One‐Sample t‐Test 66 4.2.1 Application of One‐Sample t‐Test 67 4.3 Two‐Sample t‐Test for Unrelated Groups 67 4.3.1 Assumptions While Using t‐Test 67 4.3.2 Case I: Two‐Tailed Test 68 4.3.3 Case II: Right Tailed Test 68 4.3.4 Case III: Left Tailed Test 69 4.3.5 Application of Two‐Sample t-Test 70 4.4 Paired t‐Test for Related Groups 70 4.4.1 Case I: Two‐Tailed Test 71 4.4.2 Case II: Right Tailed Test 71 4.4.3 Case III: Left Tailed Test 72 4.4.4 Application of Paired t‐Test 73 4.5 One‐Sample t‐Test with Spss 73 4.5.1 Computation in t‐Test for Single Group 74 4.5.2 Interpretation of Findings 77 4.6 Two‐Sample t‐Test for Independent Groups with Spss 78 4.6.1 Computation in Two‐Sample t‐Test 79 4.6.2 Interpretation of Findings 83 4.7 Paired t‐Test for Related Groups with Spss 85 4.7.1 Computation in Paired t‐Test 86 4.7.2 Interpretation of Findings 89 4.8 Summary of Spss Commands for t‐Tests 90 4.8.1 One‐Sample t‐Test 90 4.8.2 Two‐Sample t‐Test for Independent Groups 90 4.8.3 Paired t‐Test 91 4.9 Exercise 91 4.9.1 Short Answer Questions 91 4.9.2 Multiple Choice Questions 91 4.9.3 Assignment 93 4.10 Case Study 94 5 Independent Measures Anova 100 5.1 Introduction 101 5.2 One‐Way Analysis of Variance 101 5.2.1 One‐Way ANOVA Model 102 5.2.2 Post Hoc Test 102 5.2.3 Application of One‐Way ANOVA 103 5.3 One‐Way Anova with Spss (Equal Sample Size) 103 5.3.1 Computation in One‐Way ANOVA (Equal Sample Size) 104 5.3.2 Interpretation of Findings 107 5.4 One‐Way Anova with Spss (Unequal Sample Size) 110 5.4.1 Computation in One‐Way ANOVA (Unequal Sample Size) 111 5.4.2 Interpretation of Findings 114 5.5 Two‐Way Analysis of Variance 115 5.5.1 Assumptions in Two‐Way Analysis of Variance 116 5.5.2 Hypotheses in Two‐Way ANOVA 116 5.5.3 Factors 117 5.5.4 Treatment Groups 117 5.5.5 Main Effect 117 5.5.6 Interaction Effect 117 5.5.7 Within‐Groups Variation 117 5.5.8 F‐Statistic 117 5.5.9 Two‐Way ANOVA Table 118 5.5.10 Interpretation 118 5.5.11 Application of Two‐Way Analysis of Variance 118 5.6 Two‐Way Anova Using Spss 119 5.6.1 Computation in Two‐Way ANOVA 121 5.6.2 Interpretation of Findings 126 5.7 Summary of the Spss Commands 137 5.7.1 One‐Way ANOVA 137 5.7.2 Two‐Way ANOVA 138 5.8 Exercise 138 5.8.1 Short Answer Questions 138 5.8.2 Multiple Choice Questions 139 5.8.3 Assignment 142 5.9 Case Study on One‐Way Anova Design 143 5.10 Case Study on Two‐Way Anova 147 6 Repeated Measures Anova 153 6.1 Introduction 153 6.2 One‐Way Repeated Measures Anova 154 6.2.1 Assumptions in One‐Way Repeated Measures ANOVA 155 6.2.2 Application in Sports Research 155 6.2.3 Steps in Solving One‐Way Repeated Measures ANOVA 156 6.3 One‐Way Repeated Measures Anova Using Spss 157 6.3.1 Computation in the One‐Way Repeated Measures ANOVA 157 6.3.2 Interpretation of Findings 161 6.3.3 Findings of the Study 165 6.3.4 Inference 166 6.4 Two‐Way Repeated Measures Anova 166 6.4.1 Assumptions in Two‐Way Repeated Measures ANOVA 166 6.4.2 Application in Sports Research 167 6.4.3 Steps in Solving Two‐Way Repeated Measures ANOVA 167 6.5 Two‐Way Repeated Measures Anova Using Spss 168 6.5.1 Computation in Two‐Way Repeated Measures ANOVA 170 6.5.2 Interpretation of Findings 173 6.5.3 Findings of the Study 181 6.5.4 Inference 181 6.6 Summary of the Spss Commands for One‐Way Repeated Measures Anova 182 6.7 Summary of the Spss Commands for Two‐Way Repeated Measures Anova 182 6.8 Exercise 183 6.8.1 Short Answer Questions 183 6.8.2 Multiple Choice Questions 183 6.8.3 Assignment 185 6.9 Case Study on Repeated Measures Design 186 7 Analysis of Covariance 190 7.1 Introduction 190 7.2 Conceptual Framework of Analysis of Covariance 191 7.3 Application of ANCOVA 192 7.4 ANCOVA with Spss 193 7.4.1 Computation in ANCOVA 194 7.5 Summary of the Spss Commands 201 7.6 Exercise 202 7.6.1 Short Answer Questions 202 7.6.2 Multiple Choice Questions 202 7.6.3 Assignment 203 7.7 Case Study on ANCOVA Design 204 8 Nonparametric Tests in Sports Research 209 8.1 Introduction 209 8.2 Chi‐Square Test 211 8.2.1 Testing Goodness of Fit 211 8.2.2 Yates’ Correction 212 8.2.3 Contingency Coefficient 212 8.3 Goodness of Fit with Spss 212 8.3.1 Computation in Goodness of Fit 213 8.3.2 Interpretation of Findings 216 8.4 Testing Independence of Two Attributes 216 8.4.1 Interpretation 218 8.5 Testing Association with Spss 219 8.5.1 Computation in Chi‐Square 219 8.5.2 Interpretation of Findings 223 8.6 Mann–Whitney U Test: Comparing Two Independent Samples 224 8.6.1 Computation in Mann–Whitney U Statistic Using SPSS 224 8.6.2 Interpretation of Findings 226 8.7 Wilcoxon Signed‐Rank Test: For Comparing Two Related Groups 227 8.7.1 Computation in Wilcoxon Signed‐Rank Test Using SPSS 228 8.7.2 Interpretation of Findings 230 8.8 Kruskal–Wallis Test 231 8.8.1 Computation in Kruskal–Wallis Test Using SPSS 232 8.8.2 Interpretation of Findings 234 8.9 Friedman Test 234 8.9.1 Computation in Friedman Test Using SPSS 235 8.9.2 Interpretation of Findings 237 8.10 Summary of the Spss Commands 237 8.10.1 Computing Chi‐Square Statistic (for Testing Goodness of Fit) 237 8.10.2 Computing Chi‐Square Statistic (for Testing Independence) 238 8.10.3 Computation in Mann–Whitney U Test 238 8.10.4 Computation in Wilcoxon Signed‐Rank Test 239 8.10.5 Computation in Kruskal–Wallis Test 239 8.10.6 Computation in Friedman Test 239 8.11 Exercise 240 8.11.1 Short Answer Questions 240 8.11.2 Multiple Choice Questions 241 8.11.3 Assignment 243 8.12 Case Study on Testing Independence of Attributes 243 9 Regression Analysis and Multiple Correlations 246 9.1 Introduction 246 9.2 Understanding Regression Equation 247 9.2.1 Methods of Regression Analysis 247 9.2.2 Multiple Correlation 248 9.3 Application of Regression Analysis 248 9.4 Multiple Regression Analysis with Spss 249 9.4.1 Computation in Regression Analysis 249 9.4.2 Interpretation of Findings 254 9.5 Summary of Spss Commands for Regression Analysis 259 9.6 Exercise 259 9.6.1 Short Answer Questions 259 9.6.2 Multiple Choice Questions 260 9.6.3 Assignment 261 9.7 Case Study on Regression Analysis 263 10 Application of Discriminant Function Analysis 267 10.1 Introduction 268 10.2 Basics of Discriminant Function Analysis 268 10.2.1 Discriminating Variables 268 10.2.2 Dependent Variable 268 10.2.3 Discriminant Function 268 10.2.4 Classification Matrix 269 10.2.5 Stepwise Method of Discriminant Analysis 269 10.2.6 Power of Discriminating Variable 269 10.2.7 Canonical Correlation 269 10.2.8 Wilks’ Lambda 270 10.3 Assumptions in Discriminant Analysis 270 10.4 Why to Use Discriminant Analysis 270 10.5 Steps in Discriminant Analysis 271 10.6 Application of Discriminant Function Analysis 272 10.7 Discriminant Analysis Using Spss 274 10.7.1 Computation in Discriminant Analysis 274 10.7.2 Interpretation of Findings 279 10.8 Summary of the Spss Commands for Discriminant Analysis 284 10.9 Exercise 284 10.9.1 Short Answer Questions 284 10.9.2 Multiple Choice Questions 285 10.9.3 Assignment 286 10.10 Case Study on Discriminant Analysis 288 11 Logistic Regression for Developing Logit Model in Sport 293 11.1 Introduction 293 11.2 Understanding Logistic Regression 294 11.3 Application of Logistic Regression in Sports Research 295 11.4 Assumptions in Logistic Regression 297 11.5 Steps in Developing Logistic Model 297 11.6 Logistic Analysis Using Spss 297 11.6.1 Block 0 299 11.6.2 Block 1 299 11.6.3 Computation in Logistic Regression with SPSS 299 11.7 Interpretation of Findings 304 11.7.1 Case Processing and Coding Summary 304 11.7.2 Analyzing Logistic Models 305 11.8 Summary of the Spss Commands for Logistic Regression 310 11.9 Exercise 310 11.9.1 Short Answer Questions 310 11.9.2 Multiple Choice Questions 311 11.9.3 Assignment 312 11.10 Case Study on Logistic Regression 313 12 Application of Factor Analysis 319 12.1 Introduction 319 12.2 Terminologies Used in Factor Analysis 320 12.2.1 Principal Component Analysis 320 12.2.2 Eigenvalue 320 12.2.3 Kaiser Criterion 321 12.2.4 The Scree Test 321 12.2.5 Communality 321 12.2.6 Factor Loading 322 12.2.7 Varimax Rotation 322 12.3 Assumptions in Factor Analysis 322 12.4 Steps in Factor Analysis 323 12.5 Application of Factor Analysis 323 12.6 Factor Analysis with Spss 324 12.6.1 Computation in Factor Analysis Using SPSS 326 12.7 Summary of the Spss Commands for Factor Analysis 336 12.8 Exercise 336 12.8.1 Short Answer Questions 336 12.8.2 Multiple Choice Questions 337 12.8.3 Assignment 338 12.9 Case Study on Factor Analysis 339 Appendix 346 Bibliography 360 Index 368
£89.96
John Wiley & Sons Inc Micromechanics With Mathematica
Book SynopsisDemonstrates the simplicity and effectiveness of Mathematica as the solution to practical problems in composite materials. Designed for those who need to learn how micromechanical approaches can help understand the behaviour of bodies with voids, inclusions, defects, this book is perfect for readers without a programming background.Table of ContentsPreface ix About the Companion Website xi 1 Coordinate Transformation and Tensors 1 1.1 Index Notation 1 1.1.1 Some Examples of Index Notation in 3-D 3 1.1.2 Mathematica Implementation 3 1.1.3 Kronecker Delta 6 1.1.4 Permutation Symbols 9 1.1.5 Product of Matrices 10 1.2 Coordinate Transformations (Cartesian Tensors) 11 1.3 Definition of Tensors 13 1.3.1 Tensor of Rank 0 (Scalar) 13 1.3.2 Tensor of Rank 1 (Vector) 14 1.3.3 Tensor of Rank 2 15 1.3.4 Tensor of Rank 3 17 1.3.5 Tensor of Rank 4 17 1.3.6 Differentiation 19 1.3.7 Differentiation of Cartesian Tensors 20 1.4 Invariance of Tensor Equations 21 1.5 Quotient Rule 22 1.6 Exercises 23 References 24 2 Field Equations 25 2.1 Concept of Stress 25 2.1.1 Properties of Stress 29 2.1.2 (Stress) Boundary Conditions 30 2.1.3 Principal Stresses 31 2.1.4 Stress Deviator 35 2.1.5 Mohr’s Circle 38 2.2 Strain 40 2.2.1 Shear Deformation 47 2.3 Compatibility Condition 49 2.4 Constitutive Relation, Isotropy, Anisotropy 50 2.4.1 Isotropy 52 2.4.2 Elastic Modulus 54 2.4.3 Orthotropy 56 2.4.4 2-D Orthotropic Materials 57 2.4.5 Transverse Isotropy 57 2.5 Constitutive Relation for Fluids 58 2.5.1 Thermal Effect 58 2.6 Derivation of Field Equations 59 2.6.1 Divergence Theorem (Gauss Theorem) 59 2.6.2 Material Derivative 60 2.6.3 Equation of Continuity 62 2.6.4 Equation of Motion 62 2.6.5 Equation of Energy 63 2.6.6 Isotropic Solids 65 2.6.7 Isotropic Fluids 65 2.6.8 Thermal Effects 66 2.7 General Coordinate System 66 2.7.1 Introduction to Tensor Analysis 66 2.7.2 Definition of Tensors in Curvilinear Systems 68 2.7.3 Metric Tensor10, gij 69 2.7.4 Covariant Derivatives 70 2.7.5 Examples 73 2.7.6 Vector Analysis 75 2.8 Exercises 77 References 80 3 Inclusions in Infinite Media 81 3.1 Eshelby’s Solution for an Ellipsoidal Inclusion Problem 82 3.1.1 Eigenstrain Problem 85 3.1.2 Eshelby Tensors for an Ellipsoidal Inclusion 87 3.1.3 Inhomogeneity (Inclusion) Problem 95 3.2 Multilayered Inclusions 104 3.2.1 Background 104 3.2.2 Implementation of Index Manipulation in Mathematica 105 3.2.3 General Formulation 108 3.2.4 Exact Solution for Two-Phase Materials 116 3.2.5 Exact Solution for Three-Phase Materials 123 3.2.6 Exact Solution for Four-Phase Materials 132 3.2.7 Exact Solution for 2-D Multiphase Materials 137 3.3 Thermal Stress 137 3.3.1 Thermal Stress Due to Heat Source 138 3.3.2 Thermal Stress Due to Heat Flow 146 3.4 Airy’s Stress Function Approach 155 3.4.1 Airy’s Stress Function 156 3.4.2 Mathematica Programming of Complex Variables 161 3.4.3 Multiphase Inclusion Problems Using Airy’s Stress Function 163 3.5 Effective Properties 172 3.5.1 Upper and Lower Bounds of Effective Properties 173 3.5.2 Self-Consistent Approximation 175 3.5.3 Source Code for micromech.m 178 3.6 Exercises 188 References 189 4 Inclusions in Finite Matrix 191 4.1 General Approaches for Numerically Solving Boundary Value Problems 192 4.1.1 Method of Weighted Residuals 192 4.1.2 Rayleigh–Ritz Method 203 4.1.3 Sturm–Liouville System 205 4.2 Steady-State Heat Conduction Equations 213 4.2.1 Derivation of Permissible Functions 213 4.2.2 Finding Temperature Field Using Permissible Functions 227 4.3 Elastic Fields with Bounded Boundaries 232 4.4 Numerical Examples 238 4.4.1 Homogeneous Medium 238 4.4.2 Single Inclusion 240 4.5 Exercises 251 References 252 Appendix A Introduction to Mathematica 253 A.1 Essential Commands/Statements 255 A.2 Equations 256 A.3 Differentiation/Integration 260 A.4 Matrices/Vectors/Tensors 260 A.5 Functions 262 A.6 Graphics 263 A.7 Other Useful Functions 265 A.8 Programming in Mathematica 267 A.8.1 Control Statements 268 A.8.2 Tensor Manipulations 270 References 272 Index 273
£83.55
John Wiley & Sons Inc Theory of Lift
Book SynopsisThis introductory text walks readers from the fundamental mechanics of lift to the stage of being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and platform geometries.Trade Review“This book is a very useful digest of key points from the literature, carefully structured and presented with helpful pointers as to how the successive aerodynamical models can be implemented in the ‘now so readily available interactive matrix computation systems.” (Aeronautical Journal, 1 August 2013)Table of ContentsPreface xvii Series Preface xxiii Part One Plane Ideal Aerodynamics 1 Preliminary Notions 3 1.1 Aerodynamic Force and Moment 3 1.1.1 Motion of the Frame of Reference 3 1.1.2 Orientation of the System of Coordinates 4 1.1.3 Components of the Aerodynamic Force 4 1.1.4 Formulation of the Aerodynamic Problem 4 1.2 Aircraft Geometry 5 1.2.1 Wing Section Geometry 6 1.2.2 Wing Geometry 7 1.3 Velocity 8 1.4 Properties of Air 8 1.4.1 Equation of State: Compressibility and the Speed of Sound 8 1.4.2 Rheology: Viscosity 10 1.4.3 The International Standard Atmosphere 12 1.4.4 Computing Air Properties 12 1.5 Dimensional Theory 13 1.5.1 Alternative methods 16 1.5.2 Example: Using Octave to Solve a Linear System 16 1.6 Example: NACA Report No. 502 18 1.7 Exercises 19 1.8 Further Reading 22 References 22 2 Plane Ideal Flow 25 2.1 Material Properties: The Perfect Fluid 25 2.2 Conservation of Mass 26 2.2.1 Governing Equations: Conservation Laws 26 2.3 The Continuity Equation 26 2.4 Mechanics: The Euler Equations 27 2.4.1 Rate of Change of Momentum 27 2.4.2 Forces Acting on a Fluid Particle 28 2.4.3 The Euler Equations 29 2.4.4 Accounting for Conservative External Forces 29 2.5 Consequences of the Governing Equations 30 2.5.1 The Aerodynamic Force 30 2.5.2 Bernoulli’s Equation 33 2.5.3 Circulation, Vorticity, and Irrotational Flow 33 2.5.4 Plane Ideal Flows 35 2.6 The Complex Velocity 35 2.6.1 Review of Complex Variables 35 2.6.2 Analytic Functions and Plane Ideal Flow 38 2.6.3 Example: the Polar Angle Is Nowhere Analytic 40 2.7 The Complex Potential 41 2.8 Exercises 42 2.9 Further Reading 44 References 45 3 Circulation and Lift 47 3.1 Powers of z 47 3.1.1 Divergence and Vorticity in Polar Coordinates 48 3.1.2 Complex Potentials 48 3.1.3 Drawing Complex Velocity Fields with Octave 49 3.1.4 Example: k = 1, Corner Flow 50 3.1.5 Example: k = 0, Uniform Stream 51 3.1.6 Example: k =−1, Source 51 3.1.7 Example: k =−2, Doublet 52 3.2 Multiplication by a Complex Constant 53 3.2.1 Example: w = const., Uniform Stream with Arbitrary Direction 53 3.2.2 Example: w = i/z, Vortex 54 3.2.3 Example: Polar Components 54 3.3 Linear Combinations of Complex Velocities 54 3.3.1 Example: Circular Obstacle in a Stream 54 3.4 Transforming the Whole Velocity Field 56 3.4.1 Translating the Whole Velocity Field 56 3.4.2 Example: Doublet as the Sum of a Source and Sink 56 3.4.3 Rotating the Whole Velocity Field 56 3.5 Circulation and Outflow 57 3.5.1 Curve-integrals in Plane Ideal Flow 57 3.5.2 Example: Numerical Line-integrals for Circulation and Outflow 58 3.5.3 Closed Circuits 59 3.5.4 Example: Powers of z and Circles around the Origin 60 3.6 More on the Scalar Potential and Stream Function 61 3.6.1 The Scalar Potential and Irrotational Flow 61 3.6.2 The Stream Function and Divergence-free Flow 62 3.7 Lift 62 3.7.1 Blasius’s Theorem 62 3.7.2 The Kutta–Joukowsky Theorem 63 3.8 Exercises 64 3.9 Further Reading 65 References 66 4 Conformal Mapping 67 4.1 Composition of Analytic Functions 67 4.2 Mapping with Powers of ζ 68 4.2.1 Example: Square Mapping 68 4.2.2 Conforming Mapping by Contouring the Stream Function 69 4.2.3 Example: Two-thirds Power Mapping 69 4.2.4 Branch Cuts 70 4.2.5 Other Powers 71 4.3 Joukowsky’s Transformation 71 4.3.1 Unit Circle from a Straight Line Segment 71 4.3.2 Uniform Flow and Flow over a Circle 72 4.3.3 Thin Flat Plate at Nonzero Incidence 73 4.3.4 Flow over the Thin Flat Plate with Circulation 74 4.3.5 Joukowsky Aerofoils 75 4.4 Exercises 75 4.5 Further Reading 78 References 78 5 Flat Plate Aerodynamics 79 5.1 Plane Ideal Flow over a Thin Flat Plate 79 5.1.1 Stagnation Points 80 5.1.2 The Kutta–Joukowsky Condition 80 5.1.3 Lift on a Thin Flat Plate 81 5.1.4 Surface Speed Distribution 82 5.1.5 Pressure Distribution 83 5.1.6 Distribution of Circulation 84 5.1.7 Thin Flat Plate as Vortex Sheet 85 5.2 Application of Thin Aerofoil Theory to the Flat Plate 87 5.2.1 Thin Aerofoil Theory 87 5.2.2 Vortex Sheet along the Chord 87 5.2.3 Changing the Variable of Integration 88 5.2.4 Glauert’s Integral 88 5.2.5 The Kutta–Joukowsky Condition 89 5.2.6 Circulation and Lift 89 5.3 Aerodynamic Moment 89 5.3.1 Centre of Pressure and Aerodynamic Centre 90 5.4 Exercises 90 5.5 Further Reading 91 References 91 6 Thin Wing Sections 93 6.1 Thin Aerofoil Analysis 93 6.1.1 Vortex Sheet along the Camber Line 93 6.1.2 The Boundary Condition 93 6.1.3 Linearization 94 6.1.4 Glauert’s Transformation 95 6.1.5 Glauert’s Expansion 95 6.1.6 Fourier Cosine Decomposition of the Camber Line Slope 97 6.2 Thin Aerofoil Aerodynamics 98 6.2.1 Circulation and Lift 98 6.2.2 Pitching Moment about the Leading Edge 99 6.2.3 Aerodynamic Centre 100 6.2.4 Summary 101 6.3 Analytical Evaluation of Thin Aerofoil Integrals 101 6.3.1 Example: the NACA Four-digit Wing Sections 104 6.4 Numerical Thin Aerofoil Theory 105 6.5 Exercises 109 6.6 Further Reading 109 References 109 7 Lumped Vortex Elements 111 7.1 The Thin Flat Plate at Arbitrary Incidence, Again 111 7.1.1 Single Vortex 111 7.1.2 The Collocation Point 111 7.1.3 Lumped Vortex Model of the Thin Flat Plate 112 7.2 Using Two Lumped Vortices along the Chord 114 7.2.1 Postprocessing 116 7.3 Generalization to Multiple Lumped Vortex Panels 117 7.3.1 Postprocessing 117 7.4 General Considerations on Discrete Singularity Methods 117 7.5 Lumped Vortex Elements for Thin Aerofoils 119 7.5.1 Panel Chains for Camber Lines 119 7.5.2 Implementation in Octave 121 7.5.3 Comparison with Thin Aerofoil Theory 122 7.6 Disconnected Aerofoils 123 7.6.1 Other Applications 124 7.7 Exercises 125 7.8 Further Reading 125 References 126 8 Panel Methods for Plane Flow 127 8.1 Development of the CUSSSP Program 127 8.1.1 The Singularity Elements 127 8.1.2 Discretizing the Geometry 129 8.1.3 The Influence Matrix 131 8.1.4 The Right-hand Side 132 8.1.5 Solving the Linear System 134 8.1.6 Postprocessing 135 8.2 Exercises 137 8.2.1 Projects 138 8.3 Further Reading 139 References 139 8.4 Conclusion to Part I: The Origin of Lift 139 Part Two Three-dimensional Ideal Aerodynamics 9 Finite Wings and Three-Dimensional Flow 143 9.1 Wings of Finite Span 143 9.1.1 Empirical Effect of Finite Span on Lift 143 9.1.2 Finite Wings and Three-dimensional Flow 143 9.2 Three-Dimensional Flow 145 9.2.1 Three-dimensional Cartesian Coordinate System 145 9.2.2 Three-dimensional Governing Equations 145 9.3 Vector Notation and Identities 145 9.3.1 Addition and Scalar Multiplication of Vectors 145 9.3.2 Products of Vectors 146 9.3.3 Vector Derivatives 147 9.3.4 Integral Theorems for Vector Derivatives 148 9.4 The Equations Governing Three-Dimensional Flow 149 9.4.1 Conservation of Mass and the Continuity Equation 149 9.4.2 Newton’s Law and Euler’s Equation 149 9.5 Circulation 150 9.5.1 Definition of Circulation in Three Dimensions 150 9.5.2 The Persistence of Circulation 151 9.5.3 Circulation and Vorticity 151 9.5.4 Rotational Form of Euler’s Equation 153 9.5.5 Steady Irrotational Motion 153 9.6 Exercises 154 9.7 Further Reading 155 References 155 10 Vorticity and Vortices 157 10.1 Streamlines, Stream Tubes, and Stream Filaments 157 10.1.1 Streamlines 157 10.1.2 Stream Tubes and Stream Filaments 158 10.2 Vortex Lines, Vortex Tubes, and Vortex Filaments 159 10.2.1 Strength of Vortex Tubes and Filaments 159 10.2.2 Kinematic Properties of Vortex Tubes 159 10.3 Helmholtz’s Theorems 159 10.3.1 ‘Vortex Tubes Move with the Flow’ 159 10.3.2 ‘The Strength of a Vortex Tube is Constant’ 160 10.4 Line Vortices 160 10.4.1 The Two-dimensional Vortex 160 10.4.2 Arbitrarily Oriented Rectilinear Vortex Filaments 160 10.5 Segmented Vortex Filaments 161 10.5.1 The Biot–Savart Law 161 10.5.2 Rectilinear Vortex Filaments 162 10.5.3 Finite Rectilinear Vortex Filaments 164 10.5.4 Infinite Straight Line Vortices 164 10.5.5 Semi-infinite Straight Line Vortex 164 10.5.6 Truncating Infinite Vortex Segments 165 10.5.7 Implementing Line Vortices in Octave 165 10.6 Exercises 166 10.7 Further Reading 167 References 167 11 Lifting Line Theory 169 11.1 Basic Assumptions of Lifting Line Theory 169 11.2 The Lifting Line, Horseshoe Vortices, and the Wake 169 11.2.1 Deductions from Vortex Theorems 169 11.2.2 Deductions from the Wing Pressure Distribution 170 11.2.3 The Lifting Line Model of Air Flow 170 11.2.4 Horseshoe Vortex 170 11.2.5 Continuous Trailing Vortex Sheet 171 11.2.6 The Form of the Wake 172 11.3 The Effect of Downwash 173 11.3.1 Effect on the Angle of Incidence: Induced Incidence 173 11.3.2 Effect on the Aerodynamic Force: Induced Drag 174 11.4 The Lifting Line Equation 174 11.4.1 Glauert’s Solution of the Lifting Line Equation 175 11.4.2 Wing Properties in Terms of Glauert’s Expansion 176 11.5 The Elliptic Lift Loading 178 11.5.1 Properties of the Elliptic Lift Loading 179 11.6 Lift–Incidence Relation 180 11.6.1 Linear Lift–Incidence Relation 181 11.7 Realizing the Elliptic Lift Loading 182 11.7.1 Corrections to the Elliptic Loading Approximation 182 11.8 Exercises 182 11.9 Further Reading 183 References 183 12 Nonelliptic Lift Loading 185 12.1 Solving the Lifting Line Equation 185 12.1.1 The Sectional Lift–Incidence Relation 185 12.1.2 Linear Sectional Lift–Incidence Relation 185 12.1.3 Finite Approximation: Truncation and Collocation 185 12.1.4 Computer Implementation 187 12.1.5 Example: a Rectangular Wing 187 12.2 Numerical Convergence 188 12.3 Symmetric Spanwise Loading 189 12.3.1 Example: Exploiting Symmetry 191 12.4 Exercises 192 References 192 13 Lumped Horseshoe Elements 193 13.1 A Single Horseshoe Vortex 193 13.1.1 Induced Incidence of the Lumped Horseshoe Element 195 13.2 Multiple Horseshoes along the Span 195 13.2.1 A Finite-step Lifting Line in Octave 197 13.3 An Improved Discrete Horseshoe Model 200 13.4 Implementing Horseshoe Vortices in Octave 203 13.4.1 Example: Yawed Horseshoe Vortex Coefficients 205 13.5 Exercises 206 13.6 Further Reading 207 References 207 14 The Vortex Lattice Method 209 14.1 Meshing the Mean Lifting Surface of a Wing 209 14.1.1 Plotting the Mesh of a Mean Lifting Surface 210 14.2 A Vortex Lattice Method 212 14.2.1 The Vortex Lattice Equations 213 14.2.2 Unit Normals to the Vortex-lattice 215 14.2.3 Spanwise Symmetry 215 14.2.4 Postprocessing Vortex Lattice Methods 215 14.3 Examples of Vortex Lattice Calculations 216 14.3.1 Campbell’s Flat Swept Tapered Wing 216 14.3.2 Bertin’s Flat Swept Untapered Wing 218 14.3.3 Spanwise and Chordwise Refinement 219 14.4 Exercises 220 14.5 Further Reading 221 14.5.1 Three-dimensional Panel Methods 222 References 222 Part Three Nonideal Flow in Aerodynamics 15 Viscous Flow 225 15.1 Cauchy’s First Law of Continuum Mechanics 225 15.2 Rheological Constitutive Equations 227 15.2.1 Perfect Fluid 227 15.2.2 Linearly Viscous Fluid 227 15.3 The Navier–Stokes Equations 228 15.4 The No-Slip Condition and the Viscous Boundary Layer 228 15.5 Unidirectional Flows 229 15.5.1 Plane Couette and Poiseuille Flows 229 15.6 A Suddenly Sliding Plate 230 15.6.1 Solution by Similarity Variable 230 15.6.2 The Diffusion of Vorticity 233 15.7 Exercises 234 15.8 Further Reading 234 References 235 16 Boundary Layer Equations 237 16.1 The Boundary Layer over a Flat Plate 237 16.1.1 Scales in the Conservation of Mass 237 16.1.2 Scales in the Streamwise Momentum Equation 238 16.1.3 The Reynolds Number 239 16.1.4 Pressure in the Boundary Layer 239 16.1.5 The Transverse Momentum Balance 239 16.1.6 The Boundary Layer Momentum Equation 240 16.1.7 Pressure and External Tangential Velocity 241 16.1.8 Application to Curved Surfaces 241 16.2 Momentum Integral Equation 241 16.3 Local Boundary Layer Parameters 243 16.3.1 The Displacement and Momentum Thicknesses 243 16.3.2 The Skin Friction Coefficient 243 16.3.3 Example: Three Boundary Layer Profiles 244 16.4 Exercises 248 16.5 Further Reading 249 References 249 17 Laminar Boundary Layers 251 17.1 Boundary Layer Profile Curvature 251 17.1.1 Pressure Gradient and Boundary Layer Thickness 252 17.2 Pohlhausen’s Quartic Profiles 252 17.3 Thwaites’s Method for Laminar Boundary Layers 254 17.3.1 F(λ) ≈ 0.45 − 6λ 255 17.3.2 Correlations for Shape Factor and Skin Friction 256 17.3.3 Example: Zero Pressure Gradient 256 17.3.4 Example: Laminar Separation from a Circular Cylinder 257 17.4 Exercises 260 17.5 Further Reading 261 References 262 18 Compressibility 263 18.1 Steady-State Conservation of Mass 263 18.2 Longitudinal Variation of Stream Tube Section 265 18.2.1 The Design of Supersonic Nozzles 266 18.3 Perfect Gas Thermodynamics 266 18.3.1 Thermal and Caloric Equations of State 266 18.3.2 The First Law of Thermodynamics 267 18.3.3 The Isochoric and Isobaric Specific Heat Coefficients 267 18.3.4 Isothermal and Adiabatic Processes 267 18.3.5 Adiabatic Expansion 268 18.3.6 The Speed of Sound and Temperature 269 18.3.7 The Speed of Sound and the Speed 269 18.3.8 Thermodynamic Characteristics of Air 270 18.3.9 Example: Stagnation Temperature 270 18.4 Exercises 270 18.5 Further Reading 271 References 271 19 Linearized Compressible Flow 273 19.1 The Nonlinearity of the Equation for the Potential 273 19.2 Small Disturbances to the Free-Stream 274 19.3 The Uniform Free-Stream 275 19.4 The Disturbance Potential 275 19.5 Prandtl–Glauert Transformation 276 19.5.1 Fundamental Linearized Compressible Flows 277 19.5.2 The Speed of Sound 278 19.6 Application of the Prandtl–Glauert Rule 279 19.6.1 Transforming the Geometry 279 19.6.2 Computing Aerodynamical Forces 280 19.6.3 The Prandlt–Glauert Rule in Two Dimensions 282 19.6.4 The Critical Mach Number 284 19.7 Sweep 284 19.8 Exercises 285 19.9 Further Reading 285 References 286 Appendix A Notes on Octave Programming 287 A. 1 Introduction 287 A. 2 Vectorization 287 A.2. 1 Iterating Explicitly 288 A.2. 2 Preallocating Memory 288 A.2. 3 Vectorizing Function Calls 288 A.2. 4 Many Functions Act Elementwise on Arrays 289 A.2. 5 Functions Primarily Defined for Arrays 289 A.2. 6 Elementwise Arithmetic with Single Numbers 289 A.2. 7 Elementwise Arithmetic between Arrays 290 A.2. 8 Vector and Matrix Multiplication 290 A. 3 Generating Arrays 290 A.3. 1 Creating Tables with bsxfun 290 A. 4 Indexing 291 A.4. 1 Indexing by Logical Masks 291 A.4. 2 Indexing Numerically 291 A. 5 Just-in-Time Compilation 291 A. 6 Further Reading 292 References 292 Glossary 293 Nomenclature 305 Index 309
£76.46
John Wiley & Sons Inc A Workout in Computational Finance with Website
Book SynopsisA comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. A thorough grounding in numerical methods is necessary, as is the ability to assess their quality, advantages, and limitations. This book offers a thorough introduction to each method, revealing the numerical traps that practitioners frequently fall into. Each method is referenced with practical, real-world examples in the areas of valuation, risk analysis, and calibration of specific financial instruments and models. It features a strong emphasis on robust schemes for the numerical treatment of problems within computational finance. Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration oTable of ContentsAcknowledgements xiii About the Authors xv 1 Introduction and Reading Guide 1 2 Binomial Trees 7 2.1 Equities and Basic Options 7 2.2 The One Period Model 8 2.3 The Multiperiod Binomial Model 9 2.4 Black-Scholes and Trees 10 2.5 Strengths and Weaknesses of Binomial Trees 12 2.5.1 Ease of Implementation 12 2.5.2 Oscillations 12 2.5.3 Non-recombining Trees 14 2.5.4 Exotic Options and Trees 14 2.5.5 Greeks and Binomial Trees 15 2.5.6 Grid Adaptivity and Trees 15 2.6 Conclusion 16 3 Finite Differences and the Black-Scholes PDE 17 3.1 A Continuous Time Model for Equity Prices 17 3.2 Black-Scholes Model: From the SDE to the PDE 19 3.3 Finite Differences 23 3.4 Time Discretization 27 3.5 Stability Considerations 30 3.6 Finite Differences and the Heat Equation 30 3.6.1 Numerical Results 34 3.7 Appendix: Error Analysis 36 4 Mean Reversion and Trinomial Trees 39 4.1 Some Fixed Income Terms 39 4.1.1 Interest Rates and Compounding 39 4.1.2 Libor Rates and Vanilla Interest Rate Swaps 40 4.2 Black76 for Caps and Swaptions 43 4.3 One-Factor Short Rate Models 45 4.3.1 Prominent Short Rate Models 45 4.4 The Hull-White Model in More Detail 46 4.5 Trinomial Trees 47 5 Upwinding Techniques for Short Rate Models 55 5.1 Derivation of a PDE for Short Rate Models 55 5.2 Upwind Schemes 56 5.2.1 Model Equation 57 5.3 A Puttable Fixed Rate Bond under the Hull-White One Factor Model 63 5.3.1 Bond Details 64 5.3.2 Model Details 64 5.3.3 Numerical Method 65 5.3.4 An Algorithm in Pseudocode 68 5.3.5 Results 69 6 Boundary, Terminal and Interface Conditions and their Influence 71 6.1 Terminal Conditions for Equity Options 71 6.2 Terminal Conditions for Fixed Income Instruments 72 6.3 Callability and Bermudan Options 74 6.4 Dividends 74 6.5 Snowballs and TARNs 75 6.6 Boundary Conditions 77 6.6.1 Double Barrier Options and Dirichlet Boundary Conditions 77 6.6.2 Artificial Boundary Conditions and the Neumann Case 78 7 Finite Element Methods 81 7.1 Introduction 81 7.1.1 Weighted Residual Methods 81 7.1.2 Basic Steps 82 7.2 Grid Generation 83 7.3 Elements 85 7.3.1 1D Elements 86 7.3.2 2D Elements 88 7.4 The Assembling Process 90 7.4.1 Element Matrices 93 7.4.2 Time Discretization 97 7.4.3 Global Matrices 98 7.4.4 Boundary Conditions 101 7.4.5 Application of the Finite Element Method to Convection-Diffusion-Reaction Problems 103 7.5 A Zero Coupon Bond Under the Two Factor Hull-White Model 105 7.6 Appendix: Higher Order Elements 107 7.6.1 3D Elements 109 7.6.2 Local and Natural Coordinates 111 8 Solving Systems of Linear Equations 117 8.1 Direct Methods 118 8.1.1 Gaussian Elimination 118 8.1.2 Thomas Algorithm 119 8.1.3 LU Decomposition 120 8.1.4 Cholesky Decomposition 121 8.2 Iterative Solvers 122 8.2.1 Matrix Decomposition 123 8.2.2 Krylov Methods 125 8.2.3 Multigrid Solvers 126 8.2.4 Preconditioning 129 9 Monte Carlo Simulation 133 9.1 The Principles of Monte Carlo Integration 133 9.2 Pricing Derivatives with Monte Carlo Methods 134 9.2.1 Discretizing the Stochastic Differential Equation 135 9.2.2 Pricing Formalism 137 9.2.3 Valuation of a Steepener under a Two Factor Hull-White Model 137 9.3 An Introduction to the Libor Market Model 139 9.4 Random Number Generation 146 9.4.1 Properties of a Random Number Generator 147 9.4.2 Uniform Variates 148 9.4.3 Random Vectors 150 9.4.4 Recent Developments in Random Number Generation 151 9.4.5 Transforming Variables 152 9.4.6 Random Number Generation for Commonly Used Distributions 155 10 Advanced Monte Carlo Techniques 161 10.1 Variance Reduction Techniques 161 10.1.1 Antithetic Variates 161 10.1.2 Control Variates 163 10.1.3 Conditioning 166 10.1.4 Additional Techniques for Variance Reduction 168 10.2 Quasi Monte Carlo Method 169 10.2.1 Low-Discrepancy Sequences 169 10.2.2 Randomizing QMC 174 10.3 Brownian Bridge Technique 175 10.3.1 A Steepener under a Libor Market Model 177 11 Valuation of Financial Instruments with Embedded American/Bermudan Options within Monte Carlo Frameworks 179 11.1 Pricing American options using the Longstaff and Schwartz algorithm 179 11.2 A Modified Least Squares Monte Carlo Algorithm for Bermudan Callable Interest Rate Instruments 181 11.2.1 Algorithm: Extended LSMC Method for Bermudan Options 182 11.2.2 Notes on Basis Functions and Regression 185 11.3 Examples 186 11.3.1 A Bermudan Callable Floater under Different Short-rate Models 186 11.3.2 A Bermudan Callable Steepener Swap under a Two Factor Hull-White Model 188 11.3.3 A Bermudan Callable Steepener Cross Currency Swap in a 3D IR/FX Model Framework 189 12 Characteristic Function Methods for Option Pricing 193 12.1 Equity Models 194 12.1.1 Heston Model 196 12.1.2 Jump Diffusion Models 198 12.1.3 Infinite Activity Models 199 12.1.4 Bates Model 200 12.2 Fourier Techniques 201 12.2.1 Fast Fourier Transform Methods 201 12.2.2 Fourier-Cosine Expansion Methods 203 13 Numerical Methods for the Solution of PIDEs 209 13.1 A PIDE for Jump Models 209 13.2 Numerical Solution of the PIDE 210 13.2.1 Discretization of the Spatial Domain 211 13.2.2 Discretization of the Time Domain 211 13.2.3 A European Option under the Kou Jump Diffusion Model 212 13.3 Appendix: Numerical Integration via Newton-Cotes Formulae 214 14 Copulas and the Pitfalls of Correlation 217 14.1 Correlation 218 14.1.1 Pearson’s ρ 218 14.1.2 Spearman’s ρ 218 14.1.3 Kendall’s τ 220 14.1.4 Other Measures 221 14.2 Copulas 221 14.2.1 Basic Concepts 222 14.2.2 Important Copula Functions 222 14.2.3 Parameter estimation and sampling 229 14.2.4 Default Probabilities for Credit Derivatives 234 15 Parameter Calibration and Inverse Problems 239 15.1 Implied Black-Scholes Volatilities 239 15.2 Calibration Problems for Yield Curves 240 15.3 Reversion Speed and Volatility 245 15.4 Local Volatility 245 15.4.1 Dupire’s Inversion Formula 246 15.4.2 Identifying Local Volatility 246 15.4.3 Results 247 15.5 Identifying Parameters in Volatility Models 248 15.5.1 Model Calibration for the FTSE- 100 249 16 Optimization Techniques 253 16.1 Model Calibration and Optimization 255 16.1.1 Gradient-Based Algorithms for Nonlinear Least Squares Problems 256 16.2 Heuristically Inspired Algorithms 258 16.2.1 Simulated Annealing 259 16.2.2 Differential Evolution 260 16.3 A Hybrid Algorithm for Heston Model Calibration 261 16.4 Portfolio Optimization 265 17 Risk Management 269 17.1 Value at Risk and Expected Shortfall 269 17.1.1 Parametric VaR 270 17.1.2 Historical VaR 272 17.1.3 Monte Carlo VaR 273 17.1.4 Individual and Contribution VaR 274 17.2 Principal Component Analysis 276 17.2.1 Principal Component Analysis for Non-scalar Risk Factors 276 17.2.2 Principal Components for Fast Valuation 277 17.3 Extreme Value Theory 278 18 Quantitative Finance on Parallel Architectures 285 18.1 A Short Introduction to Parallel Computing 285 18.2 Different Levels of Parallelization 288 18.3 GPU Programming 288 18.3.1 CUDA and OpenCL 289 18.3.2 Memory 289 18.4 Parallelization of Single Instrument Valuations using (Q)MC 290 18.5 Parallelization of Hybrid Calibration Algorithms 291 18.5.1 Implementation Details 292 18.5.2 Results 295 19 Building Large Software Systems for the Financial Industry 297 Bibliography 301 Index 307
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