Thoroughly updated with improved pedagogy, the fifth edition of this classic textbook continues to provide students with a clear and comprehensive introduction the fundamentals of the finite element method. New features include enhanced coverage of introductory topics in the context of simple 1D problems, providing students with a solid base from which to advance to 2D and 3D problems; expanded coverage of more advanced concepts, to reinforce students' understanding; over 30 additional solved problems; and downloadable MATLAB, Python, C, Javascript, Fortran and Excel VBA code packages, providing students with hands-on experience, and preparing them for commercial software. Accompanied by online solutions for instructors, this is the definitive text for senior undergraduate and graduate students studying a first course in the finite element method and finite element analysis, and for professional engineers keen to shore up their understanding of finite element fundamentals.

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

This book brings together some of the finest minds in the statistical and neural network research communities. It provides a broad overview of important current developments and highlights the likely future trends in the area of neural networks.

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R's syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

Mathematical Explorations with MATLAB examines the mathematics most frequently encountered in first-year university courses. A key feature of the book is its use of MATLAB, a popular and powerful software package. The book's emphasis is on understanding and investigating the mathematics by putting the mathematical tools into practice in a wide variety of modeling situations. Even readers who have no prior experience with MATLAB will gain fluency. The book covers a wide range of material: matrices, whole numbers, complex numbers, geometry of curves and families of lines, data analysis, random numbers and simulations, and differential equations from the basic mathematics. These lessons are applied to a rich variety of investigations and modeling problems, from sequences of real numbers to cafeteria queues, from card shuffling to models of fish growth. All extras to the standard MATLAB package are supplied on the World Wide Web.

Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

Mathematica (R) in the Laboratory is a hands-on guide which shows how to harness the power and flexibility of Mathematica in the control of data-acquisition equipment and the analysis of experimental data. It explains how to use Mathematica to import, manipulate, visualise and analyse data from existing files. The generation and export of test data are also covered. The control of laboratory equipment is dealt with in detail, including the use of Mathematica's MathLink (R) system in instrument control, data processing, and interfacing. Many practical examples are given, which can either be used directly or adapted to suit a particular application. The book sets out clearly how Mathematica can provide a truly unified data-handling environment, and will be invaluable to anyone who collects or analyses experimental data, including astronomers, biologists, chemists, mathematicians, geologists, physicists and engineers. The book is fully compatible with Mathematica 3.0.

With over a million users around the world, the Mathematica software system created by Stephen Wolfram has defined the direction of technical computing for the past decade. The enhanced text and hypertext processing and state-of-the-art numerical computation features ensure that Mathematica 4 takes scientific computing into the next century. New to this version: visual tour of key features, practical tutorial introduction, full descriptions of 1100 built-in functions, a thousand illustrative examples, easy-to-follow descriptive tables, essays highlighting key concepts, examples of data import and export, award-winning gallery of Mathematica graphics, gallery of mathematical typesetting, dictionary of 700 special characters, a complete guide to the MathLink API, notes on internal implementation, and an index with over 10,000 entries copublished with Wolfram Media.

System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today's debugging team's point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources. The book presents the rapidly growing application areas of systems and software modeling, including intelligent synthetic characters, human-machine interface, menu generators, user acceptance analysis, picture archiving and software systems. Students, research scholars, academicians, scientists and industry practitioners will benefit from the book as it provides better insights into modern related global trends, issues and practices.

Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: * Original case studies and data sets * Practical exercises and lists of commands for each chapter * Downloadable Stata programmes created to work alongside chapters * A wide range of detailed applications using Stata * Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.

Previous editions of this popular textbook offered an accessible and practical introduction to numerical analysis. An Introduction to Numerical Methods: A MATLAB (R) Approach, Fourth Edition continues to present a wide range of useful and important algorithms for scientific and engineering applications. The authors use MATLAB to illustrate each numerical method, providing full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes a new chapter on Dynamical Systems and Chaos. Features Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Presents numerous examples and exercises, with selected answers at the back of the book

The interaction between computers and mathematics is becoming more and more important at all levels as computers become more sophisticated. This book shows how simple programs can be used to do significant mathematics. The purpose of this book is to give those with some mathematical background a wealth of material with which to appreciate both the power of the microcomputer and its relevance to the study of mathematics. The authors cover topics such as number theory, approximate solutions, differential equations and iterative processes, with each chapter self contained. Many exercises and projects are included giving ready-made material for demonstrating mathematical ideas. Only fundamental knowledge of mathematics is assumed and programming is restricted to "basic BASIC" which will be understood by any microcomputer. The book may be used as a textbook for algorithmic mathematics at several levels, since all the topics covered appear in any undergraduate mathematics course.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Need to Learn MATLAB? Problem SOLVED! Get started using MATLAB right away with help from this hands-on guide. MATLAB Demystified offers an effective and enlightening method for learning how to get the most out this powerful computational mathematics tool. Using an easy-to-follow format, this book explains the basics of MATLAB up front. You'll find out how to plot functions, solve algebraic equations, and compute integrals. You'll also learn how to solve differential equations, generate numerical solutions of ODEs, and work with special functions. Packed with hundreds of sample equations and explained solutions, and featuring end-of-chapter quizzes and a final exam, this book will teach you MATLAB essentials in no time at all. This self-teaching guide offers: The quickest way to get up and running on MATLAB Hundreds of worked examples with solutions Coverage of MATLAB 7 A quiz at the end of each chapter to reinforce learning and pinpoint weaknesses A final exam at the end of the book A time-saving approach to performing better on homework or on the job Simple enough for a beginner, but challenging enough for an advanced user, MATLAB Demystified is your shortcut to computational precision.

This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

This innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB (R), and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.

"Mathematica Cookbook" helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music.

Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models.Learn how to use Mathematica at a higher level with functional programming and pattern matchingDelve into the rich library of functions for string and structured text manipulationLearn how to apply the tools to physics and engineering problemsDraw on Mathematica's access to physics, chemistry, and biology dataGet techniques for solving equations in computational financeLearn how to use Mathematica for sophisticated image processingProcess music and audio as musical notes, analog waveforms, or digital sound samples

Im Mittelpunkt dieses essentials steht eine Einfuhrung in ein bekanntes statistisches Modell, das Hidden-Markov-Modell.Damit koennen Probleme bewaltigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll.Die Anwendungen des Hidden-Markov-Modells liegen hauptsachlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung.In diesem Buchlein werden die beiden zentralen Problemstellungen in HMMs behandelt.Das Problem der Inferenz wird mit dem beruhmten Viterbi-Algorithmus geloest, und das Problem der Parameterschatzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

In diesem anwendungsorientierten Lehrbuch werden kompakt alle elementaren statistischen Verfahren fur die OEkonomie anschaulich erklart. Der leicht verstandliche Text ist mit vielen Beispielen und UEbungen erganzt. Die praxisnahe Darstellung der Methoden wird durch die Erklarung und Anwendung der Statistikprogramme R (Open-Source-Progamm) und SPSS vervollstandigt. Im Text sind fur beide Programme viele Programmanweisungen enthalten. Zielgruppe sind insbesondere wirtschaftswissenschaftlich orientierte Studierende. Fur die 4. Auflage wurde das Buch uberarbeitet und erganzt. Leser des gedruckten Buchs erhalten nun in der Springer Nature Flashcards-App zusatzlich kostenfreien Zugriff auf 99 exklusive Lernfragen, mit denen sie ihr Wissen uberprufen koennen.

This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15-18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, sensor signals, and time series.

The 18th Conference of IASC-ERS, COMPSTAT'2008,is held in Porto,P- tugal,fromAugust24thtoAugust29th2008,locallyorganisedbytheFaculty of Economics of the University of Porto. COMPSTAT is an initiative of the European Regional Section of the Int- national Association for Statistical Computing (IASC-ERS), a section of the International Statistical Institute (ISI). COMPSTAT conferences started in 1974 in Wien; previous editions of COMPSTAT were held in Berlin (2002), Prague (2004) and Rome (2006). It is one of the most prestigious world conferences in Computational Statistics, regularly attracting hundreds of - searchers and practitioners, and has gained a reputation as an ideal forum for presenting top qualitytheoretical and applied work,promoting interdis- plinary researchand establishing contacts amongstresearcherswith common interests. COMPSTAT'2008 is the ?rst edition of COMPSTAT to be hosted by a Portuguese institution. Keynote lectures are addressed by Peter Hall (Department of Mathematics and Statistics, The University of Melbourne), Heikki Mannila (Department of Computer Science, Faculty of Science, University of Helsinki) and Timo Ter. asvirta (School of Economics and Management, University of Aarhus). The conference program includes two tutorials: "Computational Methods in Finance"byJamesGentle(DepartmentofComputationalandDataSciences, George Mason University) and "Writing R Packages" by Friedrich Leisch (Institut fur .. Statistik, Ludwig-Maximilians-Universit. at). Each COMPSTAT meeting is organised with a number of topics highlighted, which lead to - vited Sessions. The Conference program includes also contributed sessions in di?erent topics (both oral communications and posters).

Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria. This new edition includes the latest advances in R and related software and has been thoroughly "road-tested" over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis. The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.