Companies need more than just web analysts and data-savvy marketers to be successful–they need action heroes! While most of us never battle evil scientists or defuse nuclear warheads, successful web analysts benefit from the same attributes that fictional action heroes embody. As a web analyst, your main goal is to improve your organization’s online performance. You can become an “action hero” by translating analysis insights into action that generates significant returns for your company. How you approach analysis is critical to your overall success. In this book, web analytics expert Brent Dykes addresses the unique challenges facing analysts and online marketers working within small and large companies, teaching you how to move beyond reporting and toward analysis to drive action and change. Taking a principle-based rather than a tool-specific approach, Brent introduces you to the Action Hero Framework that breaks down the analysis process into three key stages: Prioritize (what to analyze), Analyze (how to analyze), and Mobilize (how to drive action). And he reinforces these topics with real-world examples and practical tips from seasoned analysts at leading companies. Defines the type of environment in which action heroes thrive–not just survive–as well as how to defeat the villains of web analytics that stand in the way Arms web professionals with a strategic framework for executing online analysis, as well as an arsenal of analysis techniques Reveals how companies need to be both data-driven and action-agile to drive business value from web analytics For more action hero resources and information, check out the book’s companion site at www.Analyticshero.com. "The ideas in this book will take you days (or even weeks) to work your way through, and they fly in the face of the emotional approach to marketing. The question is: would you rather have your competition lead the way with data and science when it comes to reaching your market, or are you going to go first? That's how it is with action heroes--no guts, no glory." - Seth Godin Author We Are All Weird "Don't let the jaunty, breezy style of this book throw you off. Brent successfully - and entertainingly - packs years of experience into these pages along with case studies and insightful help on getting the most out of web analytics, adding value to your company and boosting your career trajectory." - Jim Sterne Founder of eMetrics Marketing Optimization Summit, author of "Social Media Metrics" and Chairman of the Digital Analytics Association

Many people know that a major part of the functionality of the MATLAB software package is based on Java. But fewer people know how to manipulate Java to achieve improved appearance and functionality and thus heighten MATLAB software's applicability to real world, modern situations. Organized by related functionality/usage and ordered from facile to complex, this book presents examples, instruction, and code snippets in stand-alone, self-contained chapters. Requiring no prior Java knowledge, this book provides numerous online references and resources to show readers how to use and discover new components and functionalities using nothing but MATLAB itself as the discovery tool.

Chris Olsen's Teaching Elementary Statistics with JMP demonstrates this powerful software, offering the latest research on "best practice" in teaching statistics and how JMP can facilitate it. Just as statistics is data in a context, this book presents JMP in a context: teaching statistics. Olsen includes numerous examples of interesting data and intersperses JMP techniques and statistical analyses with thoughts from the statistics education literature. Intended for high school-level and college-level instructors who use JMP in teaching elementary statistics, the book uniquely provides a wide variety of data sets that will be of interest to a broad range of teachers and students. This book is part of the SAS Press program.

In Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM). You will learn how to segment customers more intelligently and to achieve, or at least get closer to, the one-to-one customer relationship that today's businesses want. Step-by-step examples and exercises clearly illustrate the concepts of segmentation and clustering in the context of CRM. The book is divided into four parts. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics such as when and how to update your models and clustering with many attributes. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner software. Part 4 takes segmentation to a new level with advanced techniques such as clustering of product associations, developing segmentation scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions.

Updates to the second edition include four new chapters in Part 4, Chapters 13-16, that introduce new and advanced analytic techniques that can be valuable in many customer segmentation applications. In addition, Chapter 9 has a new section on using the Imputation node in SAS Enterprise Miner to accomplish missing data imputation, compared to PROC MI used in earlier sections of Chapter 9. Also included are business insights and motivations for selection settings and analytical decisions on many of the examples included in this second edition.

This straightforward guide will appeal to anyone who seeks to better understand customers or prospective customers. Additionally, professors and students will find the book well suited for a business data mining analytics course in an MBA program or related course of study. You should understand basic statistics, but no prior knowledge of data mining or SAS Enterprise Miner is required.

This book is part of the SAS Press program.

Essential Statistics, Regression, and Econometrics provides students with a readable, deep understanding of the key statistical topics they need to understand in an econometrics course. It is innovative in its focus, including real data, pitfalls in data analysis, and modeling issues (including functional forms, causality, and instrumental variables). This book is unusually readable and non-intimidating, with extensive word problems that emphasize intuition and understanding. Exercises range from easy to challenging and the examples are substantial and real, to help the students remember the technique better. Readable exposition and exceptional exercises/examples that students can relate to Website includes java applets and Excel applications Focuses on key methods for econometrics students without including unnecessary topics Covers data analysis not covered in other texts Ideal presentation of material (topic order) for econometrics course

Black's latest outstanding pedagogy of Business Statistics includes the use of extra problems called "Demonstration Problems" to provide additional insight and explanation to working problems, and presents concepts, topics, formulas, and application in a manner that is palatable to a vast audience and minimizes the use of "scary" formulas. Every chapter opens up with a vignette called a "Decision Dilemma" about real companies, data, and business issues. Solutions to these dilemmas are presented as a feature called "Decision Dilemma Solved." In this edition all cases and "Decision Dilemmas" are updated and revised and 1/3 have been replaced for currency. There is also a significant number of additional problems and an extremely competitive collection of databases (containing real data) on: international stock markets, consumer food, international labor, financial, energy, agribusiness, 12-year gasoline, manufacturing, and hospital. Note: The ebook version does not provide access to the companion files.

This title includes additional digital media when purchased in print format. For this digital book edition, media content may NOT be included. Contact the publisher's customer service directly for assistance. A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including:

Use of the Excel functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations

Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.

Allan Rossman's 4 th Edition of Workshop Statistics: Discovery with Data, is enhanced from previous issues with more focus and emphasis on collaborative learning. It further requires student observation, and integrates technology for gathering, recording, and synthesizing data. The text offers more flexibility in selecting technology tools for classrooms primarily using technologies other than graphing calculators or Fathom software. Furthermore, it presents more standards for teaching statistics in an innovative, investigative, and accessible as well as provides in-depth guidance and resources to support active learning of statistics and includes updated real data sets with everyday applications in order to promote statistical literacy. TM Dynamic Data

The only official guide to building effective business solutions with TM1 from IBM Cognos IBM Cognos TM1: The Official Guide offers complete coverage of the 64-bit in-memory online analytical processing (OLAP) engine. Based on the newest release, TM1 10, this official guide offers an advanced tutorial for TM1 concepts from a technical and a business point of view. The authors, members of the TM1 team, provide typical business examples and technical insights for building practical solutions, based on their own experiences. Emphasis is placed on teaching best practices and expanding skills to learn the more powerful capabilities of TM1. The book covers analytical processing, data entry, simulation, workflow components, and more. : IBM Cognos TM1: The Official Guide The first and only officially endorsed book on IBM Cognos TM1 Written by members of IBM Cognos TM1 team with combined experience of 50 years with the product Provides working solutions for relevant business problems Offers deep insights into the most powerful and undocumented capabilities of TM1 Explains how to build actionable business intelligence

IBM Cognos 10 is the next generation off the leading performance management, analysis, and reporting standard for mid- to large-sized companies. One of the most exciting and useful aspects of IBM Cognos software is its powerful custom report creation capabilities. After learning the basics, report authors in the enterprise need to apply the technology to reports in their actual, complex work environment. This book provides that advanced know how. Using practical examples based on years of teaching experiences as IBM Cognos instructors, the authors provide you with examples of typical advanced reporting designs and complex queries in reports. The reporting solutions in this book can be directly used in a variety of real-world scenarios to provide answers to your business problems today. The complexity of the queries and the application of design principles go well beyond basic course content or introductory books. IBM Cognos 10 Report Studio: Practical Examples will help you find the answers to specific questions based on your data and your business model. It will use a combination tutorial and cookbook approach to show real-world IBM Cognos 10 Report Studio solutions. If you are still using IBM Cognos 8 BI Report Studio, many of the examples have been tested against this platform as well. The final chapter has been dedicated to showing those features that are unique to the latest version of this powerful reporting solution.

Ready to take your IT skills to the healthcare industry? This concise book provides a candid assessment of the US healthcare system as it ramps up its use of electronic health records (EHRs) and other forms of IT to comply with the government’s Meaningful Use requirements. It’s a tremendous opportunity for tens of thousands of IT professionals, but it’s also a huge challenge: the program requires a complete makeover of archaic records systems, workflows, and other practices now in place. This book points out how hospitals and doctors’ offices differ from other organizations that use IT, and explains what’s necessary to bridge the gap between clinicians and IT staff. Get an overview of EHRs and the differences among medical settings Learn the variety of ways institutions deal with patients and medical staff, and how workflows vary Discover healthcare’s dependence on paper records, and the problems involved in migrating them to digital documents Understand how providers charge for care, and how they get paid Explore how patients can use EHRs to participate in their own care Examine healthcare’s most pressing problem—avoidable errors—and how EHRs can both help and exacerbate it

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: •Create artful graphs to visualize complex data sets and functions •Write more efficient code using parallel R and vectorization •Interface R with C/C++ and Python for increased speed or functionality •Find new R packages for text analysis, image manipulation, and more •Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

An interdisciplinary approach to understanding queueing and graphical networks In today's era of interdisciplinary studies and research activities, network models are becoming increasingly important in various areas where they have not regularly been used. Combining techniques from stochastic processes and graph theory to analyze the behavior of networks, Fundamentals of Stochastic Networks provides an interdisciplinary approach by including practical applications of these stochastic networks in various fields of study, from engineering and operations management to communications and the physical sciences. The author uniquely unites different types of stochastic, queueing, and graphical networks that are typically studied independently of each other. With balanced coverage, the book is organized into three succinct parts: Part I introduces basic concepts in probability and stochastic processes, with coverage on counting, Poisson, renewal, and Markov processes Part II addresses basic queueing theory, with a focus on Markovian queueing systems and also explores advanced queueing theory, queueing networks, and approximations of queueing networks Part III focuses on graphical models, presenting an introduction to graph theory along with Bayesian, Boolean, and random networks The author presents the material in a self-contained style that helps readers apply the presented methods and techniques to science and engineering applications. Numerous practical examples are also provided throughout, including all related mathematical details. Featuring basic results without heavy emphasis on proving theorems, Fundamentals of Stochastic Networks is a suitable book for courses on probability and stochastic networks, stochastic network calculus, and stochastic network optimization at the upper-undergraduate and graduate levels. The book also serves as a reference for researchers and network professionals who would like to learn more about the general principles of stochastic networks.

Dive into the RStudio Integrated Development Environment (IDE) for using and programming R, the popular open source software for statistical computing and graphics. This concise book provides new and experienced users with an overview of RStudio, as well as hands-on instructions for analyzing data, generating reports, and developing R software packages. The open source RStudio IDE brings many powerful coding tools together into an intuitive, easy-to-learn interface. With this guide, you’ll learn how to use its main components—including the console, source code editor, and data viewer—through descriptions and case studies. Getting Started with RStudio serves as both a reference and introduction to this unique IDE. Use RStudio to provide enhanced support for interactive R sessions Clean and format raw data quickly with several RStudio components Edit R commands with RStudio’s code editor, and combine them into functions Easily locate and use more than 3,000 add-on packages in R’s CRAN service Develop and document your own R packages with the code editor and related components Create one-click PDF reports in RStudio with a mix of text and R output

Up-to-date, technically accurate coverage of essential topics in image and video processing This is the first book to combine image and video processing with a practical MATLAB®-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation. Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®. Extra features of this book include: More than 30 MATLAB® tutorials, which consist of step-by-step guides to exploring image and video processing techniques using MATLAB® Chapters supported by figures, examples, illustrative problems, and exercises Useful websites and an extensive list of bibliographical references This accessible text is ideal for upper-level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

All statistical concepts are supported by a large number of examples using data encountered in real life situations; and the text illustrates how the statistical packages MINITAB®, Microsoft Excel ®, and JMP® may be used to aid in the analysis of various data sets. The text also covers an appropriate and understandable level of the design of experiments. This includes randomized block designs, one and two-way designs, Latin square designs, factorial designs, response surface designs, and others. This text is suitable for a one- or two-semester calculus-based undergraduate statistics course for engineers and scientists, and the presentation of material gives instructors flexibility to pick and choose topics for their particular courses.

Practical Biomedical Signal Analysis Using MATLAB presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data.The first several chapters o

This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

Quickly Engages in Applying Algorithmic Techniques to Solve Practical Signal Processing Problems With its active, hands-on learning approach, this text enables readers to master the underlying principles of digital signal processing and its many applications in industries such as digital television, mobile and broadband communications, and medical/scientific devices. Carefully developed MATLAB® examples throughout the text illustrate the mathematical concepts and use of digital signal processing algorithms. Readers will develop a deeper understanding of how to apply the algorithms by manipulating the codes in the examples to see their effect. Moreover, plenty of exercises help to put knowledge into practice solving real-world signal processing challenges. Following an introductory chapter, the text explores: Sampled signals and digital processing Random signals Representing signals and systems Temporal and spatial signal processing Frequency analysis of signals Discrete-time filters and recursive filters Each chapter begins with chapter objectives and an introduction. A summary at the end of each chapter ensures that one has mastered all the key concepts and techniques before progressing in the text. Lastly, appendices listing selected web resources, research papers, and related textbooks enable the investigation of individual topics in greater depth. Upon completion of this text, readers will understand how to apply key algorithmic techniques to address practical signal processing problems as well as develop their own signal processing algorithms. Moreover, the text provides a solid foundation for evaluating and applying new digital processing signal techniques as they are developed.

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books.

For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured include descriptive statistics, one- and two-sample tests, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and sample size.

This is not a book that teaches statistics. Rather, SAS Statistics by Example is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses.

This book is part of the SAS Press program.

Assuming no knowledge of programming, this book presents both programming concepts and MATLAB’s built-in functions, providing a perfect platform for exploiting MATLAB’s extensive capabilities for tackling engineering problems. It starts with programming concepts such as variables, assignments, input/output, and selection statements, moves onto loops and then solves problems using both the ‘programming concept’ and the ‘power of MATLAB’ side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Ancillaries available with the text: Instructor solution manual (available Aug. 1st) electronic images from the text (available Aug 16th) m-files (available Aug 1st) * Presents programming concepts and MATLAB built-in functions side-by-side, giving students the ability to program efficiently and exploit the power of MATLAB to solve problems. * In depth coverage of file input/output, a topic essential for many engineering applications * Systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning * Sections on ‘common pitfalls’ and ‘programming guidelines’ direct students towards best practice * New to this edition: More engineering applications help the reader learn Matlab in the context of solving technical problems New and revised end of chapter problems Stronger coverage of loops and vectorizing in a new chapter, chapter 5 Updated to reflect current features and functions of the current release of Matlab

SAS 9.3 ODS Graphics: Getting Started with Business and Statistical Graphics provides an overview and quick-start examples of ODS Graphics functionality, which is an extension of the SAS Output Delivery System (ODS). Many SAS procedures use ODS Graphics functionality to produce graphs as automatically as they produce tables. In addition, SAS provides a full suite of ODS Graphics software that facilitates the creation of custom, stand-alone graphs. This software includes the Graph Template Language, ODS Graphics procedures, ODS Graphics Designer, and ODS Graphics Editor. This ebook also explains how the ODS Graphics software components complement each other, and how they can be used together.

This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances. The presentation is based on the naïve stochastic integration, rather than on abstract theories of measure and stochastic processes. The proofs are rather simple for practitioners and, at the same time, rather rigorous for mathematicians. Detailed application examples in natural sciences and finance are presented. Much attention is paid to simulation diffusion processes. The topics covered include Brownian motion; motivation of stochastic models with Brownian motion; Itô and Stratonovich stochastic integrals, Itô's formula; stochastic differential equations (SDEs); solutions of SDEs as Markov processes; application examples in physical sciences and finance; simulation of solutions of SDEs (strong and weak approximations). Exercises with hints and/or solutions are also provided.

R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R. About the Technology R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. About the Book R in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and nonnormal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. What's Inside Practical data analysis, step by step Interfacing R with other software Using R to visualize data Over 130 graphs Eight reference appendixes About the Reader About the Author Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net. Quotes Lucid and engaging...and fun way to learn R! - Amos A. Folarin, University College London Finally, a book that brings R to the real world. - Charles Malpas, University of Melbourne R from a programmer's point of view. - Philipp K. Janert, Principal Value, LLC A great balance of targeted tutorials and in-depth examples. - Landon Cox, 360VL, Inc. An excellent introduction and reference from the author of the best R website. - Christopher Williams, University of Idaho