An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables. With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research.

Master the application of artificial intelligence in your enterprise with the book series trusted by millions In Enterprise AI For Dummies, author Zachary Jarvinen simplifies and explains to readers the complicated world of artificial intelligence for business. Using practical examples, concrete applications, and straightforward prose, the author breaks down the fundamental and advanced topics that form the core of business AI. Written for executives, managers, employees, consultants, and students with an interest in the business applications of artificial intelligence, Enterprise AI For Dummies demystifies the sometimes confusing topic of artificial intelligence. No longer will you lag behind your colleagues and friends when discussing the benefits of AI and business. The book includes discussions of AI applications, including: Streamlining business operations Improving decision making Increasing automation Maximizing revenue The For Dummies series makes topics understandable, and as such, this book is written in an easily understood style that's perfect for anyone who seeks an introduction to a usually unforgiving topic.

Create project plans that make the most of your money and time

Get your projects on track, manage resources, and share information online

Project 2007 helps you keep your projects on track by providing sophisticated tools for building task outlines and important timing relationships; efficiently assigning people, cost, and material resources; and keeping everyone and everything on schedule. Get an overview of the benefits of Project Server and Project Web Access for communicating with your team and managing your project online.

All this on the bonus CD-ROMTools for creating enhanced graphics and reportsStrategic planning and brainstorming toolsProject add-ons that improve your time reporting and tracking capabilitiesFor details and complete system requirements, see the CD-ROM appendix.

Discover how toEmploy the powerful new features of Project 2007Track down problems with Task DriversExplore Project's new Visual ReportsGet tips for saving time and money on your projects

Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Die Evolution grosser Software-Systeme halt fur viele Unternehmen immer wieder UEberraschungen bereit. Software-Konfigurationsmanagement dient dazu, Zeit und Aufwand bei der Entwicklung und Pflege langlebiger komplexer Softwaresysteme zu reduzieren und die Software-Evolution beherrschbar zu machen. Das Buch beschreibt die Einfuhrung und effiziente Anwendung von Konfigurationsmanagement und stellt die Integration in das AEnderungsmanagement ausfuhrlich dar.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Der enorme Kostendruck in Industrieunternehmen sowie der erkennbare Wandel der Wertschopfungsketten hin zu Wertschopfungsnetzwerken werden die Bedeutung der Beschaffung auf den Unternehmenserfolg sowie die Komplexitat der Beschaffungsaufgaben noch weiter erhohen. Diese Herausforderung kann nur durch den verstarkten Einsatz geeigneter, prozessorientierter Informationstechnologie bei der Beschaffung direkter Guter bewaltigt werden.

Dieses Buch bietet durch die Darstellung des State-of-the-Art und der Entwicklungstendenzen aus Sicht der Wissenschaft sowie namhafter IT-Anbieter-, Beratungs- und Industrieunternehmen erstmals einen ganzheitlichen Uberblick uber Strategien, Prozesse und Systeme bei der Beschaffung direkter Guter. Daraus konnen Handlungsempfehlungen fur die konkrete Ausgestaltung in den Unternehmen gewonnen werden."

E(lectronic)- und M(obile)-Learning: das Lernen und Lehren mittels Informations- und Kommunikationstechnologien wird bereits in vielen Bereichen erfolgreich eingesetzt. In (Hoch)schulen sowie in der beruflichen Aus-, Fort- und Weiterbildung von Auszubildenden bis hin zu Top-Managern. Dieser Sammelband beschreibt den Status Quo und aktuelle Projekte. Er identifiziert und analysiert wichtige E-Learning-Trends und zukunftsgerichtete Entwicklungen.

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.

Compositional Data Analysis in Practice is a user-oriented practical guide to the analysis of data with the property of a constant sum, for example percentages adding up to 100%. Compositional data can give misleading results if regular statistical methods are applied, and are best analysed by first transforming them to logarithms of ratios. This book explains how this transformation affects the analysis, results and interpretation of this very special type of data. All aspects of compositional data analysis are considered: visualization, modelling, dimension-reduction, clustering and variable selection, with many examples in the fields of food science, archaeology, sociology and biochemistry, and a final chapter containing a complete case study using fatty acid compositions in ecology. The applicability of these methods extends to other fields such as linguistics, geochemistry, marketing, economics and finance. R Software The following repository contains data files and R scripts from the book https://github.com/michaelgreenacre/CODAinPractice. The R package easyCODA, which accompanies this book, is available on CRAN -- note that you should have version 0.25 or higher. The latest version of the package will always be available on R-Forge and can be installed from R with this instruction: install.packages("easyCODA", repos="http://R-Forge.R-project.org").

Watch Andy talk about the new version of his book for R: click here Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next the importance of exploring and graphing data will be discovered, before moving onto statistical tests that are the foundations of the rest of the book (for e.g. correlation and regression). Readers will then stride confidently into intermediate level analyses such as ANOVA, before ending their journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help the reader gain the necessary conceptual understanding of what they're doing, the emphasis is on applying what's learned to playful and real-world examples that should make the experience more fun than expected. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more (at www.sagepub.co.uk/fieldandmilesR). Given this book's accessibility, fun spirit, and use of bizarre real-w

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that are easily implemented by the readers. Uses real data sets available online to illustrate the usage and power of the described methods. Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones. The author Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

Erfolgreiche Veranderung hangt von der zielgerichteten Umsetzung pragmatischer Konzepte ab. Das Business Engineering liefert diese Konzepte. Das Buch zeigt, wie sie in der betrieblichen Realitat zu erfolgreichen Projekten fuhren. Die Nutzung der Informationstechnologie ist dabei das verbindende Element. Die von erfahrenen Praktikern des Business Engineering verfassten Beitrage drehen sich zum einen um technologiegetriebene Wertschopfungspotenziale und zum anderen um den methodischen Transformationsprozess zum Unternehmen des Informationszeitalters. Sie beschaftigen sich mit den zentralen Fragen des unternehmerischen Wandels: Wie andert sich die Geschaftslogik z.B. von Finanzdienstleistern, Industrieunternehmen oder Immobilienmanagement-Gesellschaften unterstutzt durch innovative Anwendungen? Welche Potenziale ergeben sich fur Supply-Chain-Management-Prozesse oder fur ein innovatives HR-Management? Welche Effekte ergeben sich in Netzwerken? Wie lassen sich die Erkenntnisse in KMU anwenden?

"

This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.

Explore the inner workings of environmental processes using a mathematical approach. Environmental Systems Analysis with MATLAB (R) combines environmental science concepts and system theory with numerical techniques to provide a better understanding of how our environment works. The book focuses on building mathematical models of environmental systems, and using these models to analyze their behaviors. Designed with the environmental professional in mind, it offers a practical introduction to developing the skills required for managing environmental modeling and data handling. The book follows a logical sequence from the basic steps of model building and data analysis to implementing these concepts into working computer codes, and then on to assessing their results. It describes data processing (rarely considered in environmental analysis); outlines the tools needed to successfully analyze data and develop models, and moves on to real-world problems. The author illustrates in the first four chapters the methodological aspects of environmental systems analysis, and in subsequent chapters applies them to specific environmental concerns. The accompanying software bundle is freely downloadable from the book web site. It follows the chapters sequence and provides a hands-on experience, allowing the reader to reproduce the figures in the text and experiment by varying the problem setting. A basic MATLAB literacy is required to get the most out of the software. Ideal for coursework and self-study, this offering: Deals with the basic concepts of environmental modeling and identification, both from the mechanistic and the data-driven viewpoint Provides a unifying methodological approach to deal with specific aspects of environmental modeling: population dynamics, flow systems, and environmental microbiology Assesses the similarities and the differences of microbial processes in natural and man-made environments Analyzes several aquatic ecosystems' case studies Presents an application of an extended Streeter & Phelps (S&P) model Describes an ecological method to estimate the bioavailable nutrients in natural waters Considers a lagoon ecosystem from several viewpoints, including modeling and management, and more

Der Einsatz von Software-Agenten zur Koordination wirtschaftlicher Prozesse und auf elektronischen Marktpl tzen ist Kernthema dieses Buches. Dabei werden Potenziale und Chancen, Anwendungen und Prototypen, aber auch Herausforderungen, Grenzen und Risiken der Agententechnologie f r den Einsatz aufgezeigt. Theoretische Grundlagen und Beispiele aus Projekten und deren Konzepte dienen als Basis f r die Realisierung eines eigenen agentenbasierten Markplatzes in Java. Im Vordergrund stehen daher praktische Ans tze zur Realisierung wirtschaftlicher Mechanismen und deren Implementierungen in eigenen Software-Agenten.
Die beigef gte CD enth lt eine eingeschr nkte Lizenzversion des kommerziellen Agentensystems "Living Agents Runtime System" sowie die im Buch beschriebenen Java-Klassen.

This book shows the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn't your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in marketing courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Marketing Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand marketing problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

It's no secret that cloud-based computing is the next big movement in IT, and Microsoft is right there in the market with Office 365a cloud-based productivity suite which includes a hosted, cloud-focused version of SharePoint 2010 SharePoint 2010 developers who have traditionally developed for on-premise environments will suddenly find themselves being asked to develop for the cloud. While there is a lot of overlap between cloud-based and traditional SharePoint development, there are also some important differences and considerations that must be taken into account as well. In particular, the proliferation of cloud-based solutions was a driving force behind certain new features in SharePoint 2010, like sandboxed solutions and the new client object model. As the devil is always in the details, Pro SharePoint 2010 Development for Office 365 helps you navigate the changes and develop compelling applications and solutions for SharePoint Online in Office 365. Authors Dave Milner, Bart McDonough, and Paul Stork bring to the table decades of experience in real-world development of solutions for customersexpertise that is the practical result of what works in real-world customer environments. This proven team will cover with you the architectural landscape that SharePoint in the cloud represents, discuss the steps in setting up a development environment, and cover multiple real-world development approaches, technologies, and considerations. What you'll learn Explicit advice for setting up development environments to work with Office 365 Coverage of possibilities for development including browser, SharePoint Designer, and Visual Studio Real-world development approaches In-depth coverage of sandboxed solutions including specific Office 365 considerations How to integrate InfoPath into an Office 365 SharePoint Online environment How to develop and deploy Silverlight applications within SharePoint Online Instructions for incorporating the most popular web development language JavaScriptand the most popular add-onjQuery Instructions for working with HTML5 and CSS3 with SharePoint Online Who this book is for

Online developers will findPro SharePoint 2010 Development for Office 365 most useful. Developers for SharePoint and .NET developers interested in SharePoint solutions for Office 365 will greatly benefit from a clear approach and road map to get into developing for SharePoint in an Office 365 environment. Online developers without a background in SharePoint will also greatly benefit from a concise approach to focusing on necessary concepts and components to get up to speed quickly in developing solutions for SharePoint Office 365. Table of Contents Getting Started with Office 365 and SharePoint Online SharePoint Online Development Overview Setting Up a Development Environment for SharePoint Online Basic Customization Using Only a Browser Taking It to the Next Level with SharePoint Designer InfoPath Forms and SharePoint Online Custom Development with Visual Studio SharePoint Designer Intro to Client-Side Development Client-Side Development with Silverlight Developing with jQuery, HTML5, and CSS3 Hybrid On-Premise/Online Solutions Office 365 Preview (Office 2013)

R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible. This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model. Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.

Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.* Covers the freely-available R language for statistics * Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more * Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done What you'll learn * Acquire and install R * Import and export data and scripts * Generate basic statistics and graphics * Program in R to write custom functions * Use R for interactive statistical explorations * Implement simulations and other advanced techniques Who this book is for Beginning R: An Introduction to Statistical Programming is an easy-to-read book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use.

In today's competitive business environment, most companies realize that the better they can manage their customer relationships, the more successful they will become. Customer Relationship Management (CRM) software systems are key tools for companies to manage the customer-facing processes of their businesses. However, many companies have resisted implementing this most critical customer-oriented application due in large part to the lack of a single-point resource on implementing a CRM system. This book attempts to fill that gap. Implementing SAP (R) CRM will help technologists and managers come to grips with the vision, concept, and technology of CRM. It begins by laying out the groundwork for understanding CRM. It explains the concept and context of CRM and the tangible business benefits of CRM adoption. Demonstrating a professional approach to the evaluation and selection of SAP, it details the critical success factors (CSFs), patterns, and anti-patterns of a successful SAP CRM implementation. CRM implementations can add significant benefit to the company's bottom line only if the company first transforms itself into a customer-centric and customer-responsive enterprise. This book explains what it means to be a customer-centric and responsive enterprise, and provides a framework for business operations based on customer relationships, rather than the traditional four Ps (product, positioning, price, promotion). It further spells out business process reengineering (BPR) strategies to configure internal business processes and operations with SAP CRM to improve customer-facing strategies, services, and relationships.

You think agile techniques might be for you, but your projects and organization are unique. An "out-of-the-box" agile approach won't work. Instead, unite agile and lean principles for your project. See how to design a custom approach, reap the benefits of collaboration, and deliver value. For project managers who want to use agile techniques, managers who want to start, and technical leaders who want to know more and succeed, this book is your first step toward agile project success. You've tried to use an off-the-shelf approach to agile techniques, and it's not working. Instead of a standard method or framework, work from agile and lean principles to design your own agile approach in a way that works for you. Build collaborative, cross-functional teams. See how small batch sizes and frequent delivery create an environment of trust and transparency between the team, management, and customers. Learn about the interpersonal skills that help agile teams work together so well. In addition to seeing work and knowing what "done" means, you'll see examples of many possible team-based measurements. Look at tools you can use for status reporting, and how to use those measurements to help your managers understand what agile techniques buy them. Recognize the traps that prevent agile principles from working in too many organizations, and what to do about those traps. Use agile techniques for workgroups, and see what managers can do to create and nurture an agile culture. You might be surprised at how few meetings and rituals you need to still work in an agile way. Johanna's signature frankness and humor will get you on the right track to design your agile project to succeed. What You Need: No technical expertise or experience needed, just a desire to know more about how you might use agile in your project.

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB (R), Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third EditionThis third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web ResourceThe authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do simulations and experiments. R can be useful for student projects, for creating graphics for teaching, as well as for scholarly work. This book presents ways R, which is freely available, can enhance the teaching of mathematics and statistics. R has the potential to help students learn mathematics due to the need for precision, understanding of symbols and functions, and the logical nature of code. Moreover, the text provides students the opportunity for experimenting with concepts in any mathematics course. Features: Does not require previous experience with R Promotes the use of R in typical mathematics and statistics course work Organized by mathematics topics Utilizes an example-based approach Chapters are largely independent of each other