This book deals with problems related to the evaluation of customer satisfaction in very different contexts and ways. Often satisfaction about a product or service is investigated through suitable surveys which try to capture the satisfaction about several partial aspects which characterize the perceived quality of that product or service. This book presents a series of statistical techniques adopted to analyze data from real situations where customer satisfaction surveys were performed. The aim is to give a simple guide of the variety of analysis that can be performed when analyzing data from sample surveys: starting from latent variable models to heterogeneity in satisfaction and also introducing some testing methods for comparing different customers. The book also discusses the construction of composite indicators including different benchmarks of satisfaction. Finally, some rank-based procedures for analyzing survey data are also shown.

This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences. The software packages used in the papers are made available by the authors. This book is a result of the 47th Scientific Meeting of the Italian Statistical Society, held at the University of Cagliari, Italy, in 2014.

This book presents a detailed description of the development of statistical theory. In the mid twentieth century, the development of mathematical statistics underwent an enduring change, due to the advent of more refined mathematical tools. New concepts like sufficiency, superefficiency, adaptivity etc. motivated scholars to reflect upon the interpretation of mathematical concepts in terms of their real-world relevance. Questions concerning the optimality of estimators, for instance, had remained unanswered for decades, because a meaningful concept of optimality (based on the regularity of the estimators, the representation of their limit distribution and assertions about their concentration by means of Anderson's Theorem) was not yet available. The rapidly developing asymptotic theory provided approximate answers to questions for which non-asymptotic theory had found no satisfying solutions. In four engaging essays, this book presents a detailed description of how the use of mathematical methods stimulated the development of a statistical theory. Primarily focused on methodology, questionable proofs and neglected questions of priority, the book offers an intriguing resource for researchers in theoretical statistics, and can also serve as a textbook for advanced courses in statisticc.

The subject of this book stands at the crossroads of ergodic theory and measurable dynamics. With an emphasis on irreversible systems, the text presents a framework of multi-resolutions tailored for the study of endomorphisms, beginning with a systematic look at the latter. This entails a whole new set of tools, often quite different from those used for the "easier" and well-documented case of automorphisms. Among them is the construction of a family of positive operators (transfer operators), arising naturally as a dual picture to that of endomorphisms. The setting (close to one initiated by S. Karlin in the context of stochastic processes) is motivated by a number of recent applications, including wavelets, multi-resolution analyses, dissipative dynamical systems, and quantum theory. The automorphism-endomorphism relationship has parallels in operator theory, where the distinction is between unitary operators in Hilbert space and more general classes of operators such as contractions. There is also a non-commutative version: While the study of automorphisms of von Neumann algebras dates back to von Neumann, the systematic study of their endomorphisms is more recent; together with the results in the main text, the book includes a review of recent related research papers, some by the co-authors and their collaborators.

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: * Covers SAS v9.2 and incorporates new commands * Uses SAS ODS (output delivery system) for reproduction of tables and graphics output * Presents new commands needed to produce ODS output * All chapters rewritten for clarity * New and updated examples throughout * All SAS outputs are new and updated, including graphics * More exercises and problems * Completely new chapter on analysis of nonlinear and generalized linear models * Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

Improve your audit results and extend your capabilities with Mastering IDEAScript: The Definitive Guide

Run audit programs and data analysis projects with ease. Create a local automated audit system. Meet current audit standards. Do it all with Mastering IDEAScript, the official guide from CaseWare IDEA(R).

Designed to help the complete novice develop the skills required to write applications using IDEAScript, this resource starts with simple topics, progressively working up to more complex areas. It helps you understand what automation can help you do and set reasonable goals for working with IDEAScript.

Although basic familiarity with the IDEA(R) program is helpful in the use of this book's concepts, neither programming skills nor special equipment is required. Here, you'll find plain-English, easy-to-follow explanations for:

Creating your first IDEAScript application

Using the IDEAScript Editor

Writing code quickly and efficiently

Building complete applications without any code at all, using the Macro Recorder

Troubleshooting errors that occur in your application

Performing basic tasks, such as indexing, sorting, and closing your database

Finding information easily within databases

And much more

Along with a companion website containing all of the scripts found in this book, Mastering IDEAScript: The Definitive Guide is packed with practical techniques and rules of thumb to help you understand the workings of IDEAScript. The days of filling in forms and answering countless questions when running audit programs are over. From now on, let IDEAScript do the work

IDEA(R) is a leading provider of data analysis software targeted to auditors to use as a tool for fraud detection and internal control assessment. IDEA(R) software is used in sixteen languages in more than ninety countries, by major accounting firms, governments, and corporations in all industry sectors, as well as by universities as a teaching tool.

IDEA(R) is a registered trademark of CaseWare International Inc.

This book presents new findings on nonregular statistical estimation. Unlike other books on this topic, its major emphasis is on helping readers understand the meaning and implications of both regularity and irregularity through a certain family of distributions. In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. This focus includes the (truncated) Pareto distribution, which is widely used in various fields such as finance, physics, hydrology, geology, astronomy, and other disciplines. The family is essential in that it links both regular and nonregular distributions, as it becomes a regular exponential family if the truncation parameter is known. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter. In order to obtain more information on the truncation, the Bayesian approach is also considered. Further, the application to some useful truncated distributions is discussed. The illustrated clarification of the nonregular structure provides researchers and practitioners with a solid basis for further research and applications.

Statistics 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 enable in-sight into this difficult topic. * Promotes the use of graphs and confidence intervals in addition to p-values. * Course materials and tutorials for teaching are available on the book's companion website. 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.

R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections.

Get to work in SAP S/4HANA with this introductory guide! Learn how to navigate your user interface, manage and report on your data, customize your reports, and tailor the system to your preferences. Then walk through daily tasks for several key lines of business: procurement, sales, and finance. With details on special functions and troubleshooting, this is the complete guide to your SAP S/4HANA workday! In this book, you'll learn about: a. User Operations Get started in your new system! Learn how to log in, navigate, and display, maintain, and report on your company's data. b. Customization Options Use SAP S/4HANA according to your specifications and preferences! Adapt your reporting settings and personalize your user interface so that it works for you, whether you're using the new UI (SAP Fiori) or the classic look of SAP GUI. c. Business Processes See how to complete day-to-day tasks in key lines of business. Perform procurement activities in materials management, ordering and invoicing in sales, and business transactions in finance such as displaying open items or a balance sheet. Highlights Include: 1) Logon and navigation 2) Reporting 3) Personalization 4) Procurement 5) Sales 6) Financial accounting 7) Special functions 8) Troubleshooting 9) SAP Fiori applications 10) SAP GUI transactions

Learn SAP's real estate management integrated solution to effectively manage the real estate portfolio at your organization. You will configure SAP REFX for business scenarios covering solutions from master data to financial posting and reporting. You will address all phases of the real estate life cycle, including real estate acquisition or disposal, portfolio management, and property and technical management. To succeed in today's global and highly competitive economy, asset optimization in real estate management has become a strategic task. Organizations need to ensure insight into their property portfolio to make informed decisions, improve portfolio performance, and reduce compliance costs. Sophisticated solutions are needed to manage changing consumer demands and the global workforce as well as information management, compliance adherence, and leasing and property management. SAP Flexible Real Estate Management by Daithankar is a full-featured book that integrates REFX with Controlling (CO), Plant and Maintenance (PM), CRM, SAP AA (ssset accounting), and SAP PS (project systems). You will refer to real-world, practical examples to illustrate configuration concepts and processes, and learn in an interactive, hands-on way through the use of screenshots, menu paths, and transaction codes throughout the book.What You Will Learn: Understand the SAP REFX Solutions landscape and industry best practices for SAP REFX implementation Configure SAP REFX Integrate REFX with other modules Understand how processes are supported by SAP REFX Who This Book Is For: CIOs/CEOs of organizations with real estate portfolios, SAP REFX purchasing decision makers, SAP REFX pre-sales teams, SAP REFX implementation/AMS consultants

Data Presentation with SPSS Explained provides students with all the information they need to conduct small scale analysis of research projects using SPSS and present their results appropriately in their reports. Quantitative data can be collected in the form of a questionnaire, survey or experimental study. This book focuses on presenting this data clearly, in the form of tables and graphs, along with creating basic summary statistics. Data Presentation with SPSS Explained uses an example survey that is clearly explained step-by-step throughout the book. This allows readers to follow the procedures, and easily apply each step in the process to their own research and findings. No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing - many of whom are unaware that this extremely helpful program is available at their institution for their use.

This proceedings volume contains eight selected papers that were presented in the International Symposium in Statistics (ISS) 2015 On Advances in Parametric and Semi-parametric Analysis of Multivariate, Time Series, Spatial-temporal, and Familial-longitudinal Data, held in St. John's, Canada from July 6 to 8, 2015. The main objective of the ISS-2015 was the discussion on advances and challenges in parametric and semi-parametric analysis for correlated data in both continuous and discrete setups. Thus, as a reflection of the theme of the symposium, the eight papers of this proceedings volume are presented in four parts. Part I is comprised of papers examining Elliptical t Distribution Theory. In Part II, the papers cover spatial and temporal data analysis. Part III is focused on longitudinal multinomial models in parametric and semi-parametric setups. Finally Part IV concludes with a paper on the inferences for longitudinal data subject to a challenge of important covariates selection from a set of large number of covariates available for the individuals in the study.

This book provides a friendly introduction to the paradigm and proposes a broad panorama of killing applications of the Infinity Computer in optimization: radically new numerical algorithms, great theoretical insights, efficient software implementations, and interesting practical case studies. This is the first book presenting to the readers interested in optimization the advantages of a recently introduced supercomputing paradigm that allows to numerically work with different infinities and infinitesimals on the Infinity Computer patented in several countries. One of the editors of the book is the creator of the Infinity Computer, and another editor was the first who has started to use it in optimization. Their results were awarded by numerous scientific prizes. This engaging book opens new horizons for researchers, engineers, professors, and students with interests in supercomputing paradigms, optimization, decision making, game theory, and foundations of mathematics and computer science. "Mathematicians have never been comfortable handling infinities... But an entirely new type of mathematics looks set to by-pass the problem... Today, Yaroslav Sergeyev, a mathematician at the University of Calabria in Italy solves this problem... " MIT Technology Review "These ideas and future hardware prototypes may be productive in all fields of science where infinite and infinitesimal numbers (derivatives, integrals, series, fractals) are used." A. Adamatzky, Editor-in-Chief of the International Journal of Unconventional Computing. "I am sure that the new approach ... will have a very deep impact both on Mathematics and Computer Science." D. Trigiante, Computational Management Science. "Within the grossone framework, it becomes feasible to deal computationally with infinite quantities, in a way that is both new (in the sense that previously intractable problems become amenable to computation) and natural". R. Gangle, G. Caterina, F. Tohme, Soft Computing. "The computational features offered by the Infinity Computer allow us to dynamically change the accuracy of representation and floating-point operations during the flow of a computation. When suitably implemented, this possibility turns out to be particularly advantageous when solving ill-conditioned problems. In fact, compared with a standard multi-precision arithmetic, here the accuracy is improved only when needed, thus not affecting that much the overall computational effort." P. Amodio, L. Brugnano, F. Iavernaro & F. Mazzia, Soft Computing

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods. This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods.

Learn how to make the most of SharePoint 2016 and its wide range of capabilities to support your information management, collaboration, and business process management needs. Whether you are using SharePoint as an intranet or business solution platform, you will learn how to use the resources (such as lists, libraries, and sites) and services (such as search, workflow, and social) that make up these environments. In the fifth edition of this bestselling book, author Tony Smith walks you through the components and capabilities that make up a SharePoint 2016 environment. He provides step-by-step instructions for using and managing these elements, as well as recommendations for how to get the best out of them. What You Will Learn Create and use common SharePoint resources like lists, libraries, sites, pages and web parts Understand when and how workflows and information management policies can be used to automate process Learn how to take advantage of records retention, management, and disposition Make the most of SharePoint search services Take advantage of social capabilities to create social solutions Who This Book Is For Whether you have not yet used SharePoint at all, have used previous versions, have just started using the basic features, or have been using it for a long of time, this book provides the skills you need to work efficiently with the capabilities SharePoint 2016 provides.

Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

Learn how to form and execute an enterprise information strategy: topics include data governance strategy, data architecture strategy, information security strategy, big data strategy, and cloud strategy. Manage information like a pro, to achieve much better financial results for the enterprise, more efficient processes, and multiple advantages over competitors. As you'll discover in Enterprise Information Management in Practice, EIM deals with both structured data (e.g. sales data and customer data) as well as unstructured data (like customer satisfaction forms, emails, documents, social network sentiments, and so forth). With the deluge of information that enterprises face given their global operations and complex business models, as well as the advent of big data technology, it is not surprising that making sense of the large piles of data is of paramount importance. Enterprises must therefore put much greater emphasis on managing and monetizing both structured and unstructured data. As Saumya Chaki-an information management expert and consultant with IBM-explains in Enterprise Information Management in Practice, it is now more important than ever before to have an enterprise information strategy that covers the entire life cycle of information and its consumption while providing security controls. With Fortune 100 consultant Saumya Chaki as your guide, Enterprise Information Management in Practice covers each of these and the other pillars of EIM in depth, which provide readers with a comprehensive view of the building blocks for EIM. Enterprises today deal with complex business environments where information demands take place in real time, are complex, and often serve as the differentiator among competitors. The effective management of information is thus crucial in managing enterprises. EIM has evolved as a specialized discipline in the business intelligence and enterprise data warehousing space to address the complex needs of information processing and delivery-and to ensure the enterprise is making the most of its information assets.

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

The Professional Product Owner's Guide to Maximizing Value with Scrum "This book presents a method of communicating our desires, cogently, coherently, and with a minimum of fuss and bother." -Ken Schwaber, Chairman & Founder, Scrum.org The role of the Product Owner is more crucial than ever. But it's about much more than mechanics: it's about taking accountability and refocusing on value as the primary objective of all you do. In The Professional Product Owner, two leading experts in successful Scrum product ownership show exactly how to do this. You'll learn how to identify where value can be found, measure it, and maximize it throughout your entire product lifecycle. Drawing on their combined 40+ years of experience in using agile and Scrum in product management, Don McGreal and Ralph Jocham guide you through all facets of envisioning, emerging, and maturing a product using the Scrum framework. McGreal and Jocham discuss strategy, showing how to connect Vision, Value, and Validation in ROI-focused agile product management. They lay out Scrum best-practices for managing complexity and continuously delivering value, and they define the concrete practices and tools you can use to manage Product Backlogs and release plans, all with the goal of making you a more successful Product Owner. Throughout, the authors share revealing personal experiences that illuminate obstacles to success and show how they can be overcome. Define success from the "outside in," using external customer-driven measurements to guide development and maximize value Bring empowerment and entrepreneurship to the Product Owner's role, and align everyone behind a shared business model Use Evidence-Based Management (EBMgt) to invest in the right places, make smarter decisions, and reduce risk Effectively apply Scrum's Product Owner role, artifacts, and events Populate and manage Product Backlogs, and use just-in-time specifications Plan and manage releases, improve transparency, and reduce technical debt Scale your product, not your Scrum Use Scrum to inject autonomy, mastery, and purpose into your product team's work Whatever your role in product management or agile development, this guide will help you deliver products that offer more value, more rapidly, and more often. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014. Teaching and learning a substantial programming language aren't always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

SAS programming is a creative and iterative process designed to empower you to make the most of your organization's data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.

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.