Contains the complete reference for all Base SAS procedures. Provides information about what each procedure does and, if relevant, the kind of output that it produces.

Explains how to increase the modularity, flexibility, and maintainability of your SAS code using the SAS macro facility. Provides complete information about macro language elements,

Describes the basics of using the SQL procedure and provides comprehensive reference information. The usage information includes retrieving data from single and multiple tables; selecting specific data from tables; subsetting, ordering, and summarizing data; updating tables; combining tables to create new tables and useful reports; performing queries on database management system (DBMS) tables; using PROC SQL with the SAS macro facility; and debugging and optimizing PROC SQL code. The reference information includes statements, dictionary components, and system options.

The best-selling Little SAS Book just got even better. Readers worldwide study this easy-to-follow book to help them learn the basics of SAS programming. Now Rebecca Ottesen has teamed up with the original authors, Lora Delwiche and Susan Slaughter, to provide a new way to challenge and improve your SAS skills through thought-provoking questions, exercises, and projects. Each chapter in Exercises and Projects for The Little SAS Book is matched to a chapter in The Little SAS Book, Fifth Edition. This hands-on workbook is designed to hone your SAS skills whether you are a

student or a professional.

This book covers DATA step programming, report writing, statistical and graphical procedures, the Output Delivery System, macros, and more; contains a mixture of multiple-choice questions, open-ended discussion topics, and programming exercises with selected answers and hints; and includes comprehensive programming projects that are designed to encourage self-study and to test the skills developed by The Little SAS Book.

PIntroduces the core functionality of SAS Enterprise Miner and shows how to perform basic data mining tasks. Provides step-by-step examples that create a complete process-flow diagram including graphic results.

Provides conceptual information for the Base SAS language. Major topics include SAS keywords and naming conventions, SAS variables and expressions, error processing and debugging, SAS data sets and files, creating and customizing output, DATA step concepts and DATA step processing, reading raw data, and creating and managing SAS libraries.

Provides usage information and examples for Output Delivery System (ODS) capabilities. The document describes a wide range of formatting options and shows how to gain greater flexibility in generating, storing, and reproducing SAS procedure and DATA step output.

Explore the practical applications of predictive analytics with a focus on leveraging Rattle and Qlik Sense in your projects. From setting up your environment to constructing comprehensive data applications, this book provides a complete roadmap for mastering these tools and techniques. Gain valuable insights into your data and elevate your analytical skills for real-world business scenarios. What this Book will help me do Set up your analytics environment with Qlik Sense, R, and Rattle to kick-start your projects. Master visualization techniques and statistical methods to uncover meaningful insights. Develop data-driven predictive models and evaluate their performances effectively. Learn unsupervised and supervised machine learning techniques for diverse data problems. Build data storytelling and dashboards in Qlik Sense to showcase your results compellingly. Author(s) Ferran Garcia Pagans is a seasoned data analyst and author with extensive experience in predictive analytics and data visualization. His deep knowledge of modern analytics tools like Rattle and Qlik Sense enables him to guide professionals in deriving actionable insights from data. He excels in breaking down complex concepts into accessible knowledge, making his work invaluable for learners. Who is it for? This book is perfect for business analysts, data professionals, and enthusiasts looking to enhance their understanding of predictive analytics. It suits those with foundational knowledge of statistics and tools like Excel and R. If your goal is to implement real-world predictive models and data storytelling using Rattle and Qlik Sense, this book is an ideal companion.

Using JMP Student Edition is the official user guide for JMP Student Edition, the streamlined edition of JMP for first year statistics students. Clearly written, with easy-to-follow, step-by-step instructions, this book provides engaging illustrations and concept overviews. Chapters cover JMP basics such as importing data, creating formulas, creating graphs with Graph Builder, and performing univariate and bivariate data analysis. More complex analyses are covered, including Partition, Multiple Regression, Time Series, Design of Experiments, Variability Gauge Charts, and Quality Control, and more. JMP Student Edition software is available from major textbook publishers when packaged with their textbooks for course use.

An abundance of real-world examples highlights Lauren Haworth Lake’s and Julie McKnight's PROC TABULATE by Example, Second Edition. Beginning and intermediate SAS® users will find this step-by-step guide to producing tables and reports using the TABULATE procedure both convenient and inviting. Topics are presented in order of increasing complexity, making this an excellent training manual or self-tutorial. The concise format also makes this a quick reference guide for specific applications for more advanced users. A very handy section on common problems and their solutions is also included. With this book, you will quickly learn how to generate tables using macros, handle percentages and missing data, modify row and column headings, and produce one-, two-, and three-dimensional tables using PROC TABULATE. Also provided are more advanced tips on complex formatting with the Output Delivery System (ODS) and exporting PROC TABULATE output to other applications.

As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Topics addressed include: Appropriate methods for binary, ordinal, and continuous measures Computations using PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros Comparing the ROC curves of several markers and adjusting them for covariates ROC curves with censored data Using the ROC curve for evaluating multivariable prediction models via bootstrap and cross-validation ROC curves in SAS Enterprise Miner And more! Written for any statistician interested in learning more about ROC curve methodology, the book assumes readers have a basic understanding of regression procedures and moderate familiarity with Base SAS and SAS/STAT. Some familiarity with SAS/GRAPH is helpful but not essential. This book is part of the SAS Press program.

Thoroughly updated for SAS 9, Cody's Data Cleaning Techniques Using SAS, Second Edition, addresses tasks that nearly every SAS programmer needs to do - that is, make sure that data errors are located and corrected. Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify for your own special data cleaning needs. Each topic is developed through specific examples, and every program and macro is explained in detail.

Master simple-to-complex techniques for transporting and managing data between SAS and Excel William Benjamin's Exchanging Data between SAS and Microsoft Excel: Tips and Techniques to Transfer and Manage Data More Efficiently describes many of the options and methods that enable a SAS programmer to transport data between SAS and Excel. The book includes examples that all levels of SAS and Excel users can apply to their everyday programming tasks. Because the book makes no assumptions about the skill levels of either SAS or Excel users, it has a wide-ranging application, providing detailed instructions about how to apply the techniques shown. It contains sections that gather instructional and syntactical information together that are otherwise widely dispersed, and it provides detailed examples about how to apply the software to everyday applications. These examples enable novice users and power developers alike the chance to expand their capabilities and enhance their skillsets. By moving from simple-to-complex applications and examples, the layout of the book allows it to be used as both a training and a reference tool. Excel users and SAS programmers are presented with tools that will assist in the integration of SAS and Excel processes in order to automate reporting and programming interfaces. This enables programming staff to request their own reports or processes and, in turn, support a much larger community.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified "white box" approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Discovering JMP 12 provides a basic introduction to using JMP. For new users of JMP, this is a great place to start. The book also includes details about importing your data into JMP, analyzing the data, and sharing the results.

JMP 12 Basic Analysis covers the initial types of analyses that you often perform in JMP, such as univariate, bivariate, and oneway analyses. Creating tables of summary statistics with the Tabulate platform is included along with approximating sampling distributions using bootstrapping. Find information about how to clean up your data before performing analyses.

JMP 12 Consumer Research focuses on analyses that help users observe and predict subject's behavior, particularly those in the market research field. The Uplift platform predicts consumer behavior based on shifts in marketing efforts. Learn how to tabulate and summarize categorical responses with the Categorical platform. Factor Analysis rotates principal components to help identify which directions have the most variation among the variables. The book also covers Item Analysis, a method for identifying latent traits that might affect an individual's choices. And read about the Choice platform, which market researchers use to estimate probability in consumer spending.

JMP 12 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response surface, and mixture designs). Read about more flexible custom designs, which you generate to fit your particular experimental situation. Discover JMP’s definitive screening designs, an efficient way to identify important factor interactions using fewer runs than required by traditional designs. And read about creating designs that test systems where failures occur as a result of interactions among components or subsystems. The book also provides guidance on determining an appropriate sample size for your study.

Start with JMP 12 Essential Graphing to find the ideal graph for your data. The book begins with Graph Builder, a quick way to create graphs in a drag-and-drop window. Line charts, ellipses, box plots, and maps are just a few of the graphs available in Graph Builder. Find information about creating other types of plots: bubble plots, scatterplots, parallel plots, and more.

JMP 12 Fitting Linear Models focuses on the Fit Model platform and many of its personalities. Linear and logistic regression, analysis of variance and covariance, and stepwise procedures are covered. Also included are multivariate analysis of variance, mixed models, generalized models, and models based on penalized regression techniques.

The JMP 12 JSL Syntax Reference focuses on functions and their arguments, and messages that you send to objects and display boxes. Notes and examples are included.

JMP 12 Multivariate Methods describes techniques for analyzing several variables simultaneously. The book covers descriptive measures, such as correlations. It also describes methods that give insight into the structure of the multivariate data, such as clustering, principal components, discriminant analysis, and partial least squares.

JMP 12 Profilers covers the family of interactive profiling tools, which enable you to view cross-sections of any response surface. The book also includes details about plotting points and surfaces in a three-dimensional graph.

JMP 12 Quality and Process Methodsfocuses on the Fit Model platform and many of its personalities. Linear and logistic regression, analysis of variance and covariance, and stepwise procedures are covered. Also included are multivariate analysis of variance, mixed models, generalized models, and models based on penalized regression techniques.

JMP 12 Reliability and Survival Methods provides details about evaluating and improving reliability in a product or system and analyzing survival data for people and products. The book explains how to fit the best distribution to your time-to-event data or analyze destruction data. A few other topics include analyzing competing causes of failure, modeling reliability as improvements are made over time, and analyzing recurring events.