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JSL Companion: Applications of the JMP Scripting Language provides novice scripters with a resource that helps them go beyond the basics of the JMP Scripting Language (JSL) and serves as a companion to writing applications. Taking a task-oriented approach rather than focusing on showing the syntax, the authors help users with much more than the basic task of capturing a script. The book starts with an introduction that is suitable for someone who is just beginning to learn JSL. It quickly moves to importing and saving data, working with variables, modifying data tables, and working with JMP data structures (lists, matrices, and associative arrays). Later chapters deal with JMP output, communicating with users, and customizing displays. The last two chapters cover writing flexible code and include several tips that help solve problems with which novice users often struggle. This book is part of the SAS Press program.
This book contains the essentials for getting started with the JMP Scripting Language (JSL), which will enable you to create and use scripts to process various tasks during a single execution. Divided into two distinct sections, the first section of "Jump into JMP Scripting" includes JSL topics that are explained in an easy-to-understand style that is based on the extensive experience of Wendy Murphrey and Rosemary Lucas. Each topic includes step-by-step instructions and plenty of code examples. The second section of the book includes many examples of how to perform specific tasks in JSL. Using a unique question and answer format, each example answers a question with a script sample. In addition, many examples include a discussion section to explain functions in detail or to provide additional references. The goal is to make learning JSL easy and maybe a little bit of fun along the way.
Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions. This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to: See the sources of variation in your data Search for clues in your data to construct hypotheses about underlying behavior Identify key drivers and models Shape and build your own real-world Six Sigma experience Whether you work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the strategies, process, and road map to put Visual Six Sigma to work for your company. Broaden and deepen your implementation of Visual Six Sigma with the intuitive and easy-to-use tools found in Visual Six Sigma: Making Data Analysis Lean.
For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide, Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this new edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, and of course new versions of SAS and JMP software. Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you.
Microsoft's Power Pivot is an add-on to Excel that enables you to produce new kinds of reports and analyses that were simply impossible before. This book is the first to tackle DAX formulas, the core capability of Power Pivot, from the perspective of the Excel audience. Written by a leading Power Pivot educator (and former leader on the Power Pivot and Excel teams at Microsoft), the book's concepts and approach are introduced in a simple, step-by-step manner tailored to the learning style of Excel users everywhere.The techniques presented allow users to produce, in hours or even minutes, results that formerly would have taken entire teams weeks or months to produce. In this book you will learn how Power Pivot:1) Gives you "portable" formulas that can be re-used across multiple different reports with a single click.2) Removes the need to ever write a VLOOKUP formula again.3) Allows you to add smart calculations to pivots, such as "Year over Year" and "Moving Averages" which auto-adjust as the pivot changes.4) Effortlessly merges disjointed sets of data into unified insight.As a bonus, Power Pivot and DAX formulas are both the heart AND brain of Microsoft's "Power BI" system, giving us a long-needed bridge between the worlds of Excel and Business Intelligence – a bridge that any Excel PivotTable user can cross with the help of this easy-to-follow book. Your new career – and your organization's future – starts within these pages
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.
This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.

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