Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

"Finite Difference Fundamentals in MATLAB" is devoted to the solution of numerical problems employing basic finite difference (FD) methods in MATLAB platform. FD is one momentous tool of numerical analysis on science and engineering problems. Advent of faster speed computer processors and user-friendliness of MATLAB have marvelously facilitated FD solution obtaining what is demonstrated in every chapter. Another aspect of the text is juxtaposition on computing and graphing features. The coverage narrates key executional MATLAB style of FD terminologies without arithmetic complexity. Self-training illustrations and end-of-chapter exercises inspire the reader a checkup on thorough understanding. The comprehensive introduction will benefit science and engineering undergraduates studying numerical analysis issues ranging archetype to advanced.

Sanjay Matange and Dan Heath's "Statistical Graphics Procedures by Example: Effective Graphs Using SAS" shows the innumerable capabilities of SAS Statistical Graphics (SG) procedures. The authors begin with a general discussion of the principles of effective graphics, ODS Graphics, and the SG procedures. They then move on to show examples of the procedures' many features. The book is designed so that you can easily flip through it, find the graph you need, and view the code right next to the example.

Among the topics included are how to combine plot statements to create custom graphs; customizing graph axes, legends, and insets; advanced features, such as annotation and attribute maps; tips and tricks for creating the optimal graph for the intended usage; real-world examples from the health and life sciences domain; and ODS styles.

The procedures in "Statistical Graphics Procedures by Example" are specifically designed for the creation of analytical graphs. That makes this book a must-read for analysts and statisticians in the health care, clinical trials, financial, and insurance industries. However, you will find that the examples here apply to all fields.

Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software.

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Heat Maps in R: How-to is an easy to understand book that starts with a simple heat map and takes you all the way through to advanced heat maps with graphics and data manipulation. Heat Maps in R How-to is the book for you if you want to make use of this free and open source software to get the most out of your data analysis. You need to have at least some experience in using R and know how to run basic scripts from the command line. However, knowledge of other statistical scripting languages such as Octave, S-Plus, or MATLAB will suffice to follow along with the recipes. You need not be from a statistics background.

Il libro nasce dall esigenza di coniugare esperienze e capacita procedurali diverse provenienti da vari ambiti disciplinari, quali l informatica e la statistica, al fine di ricercare ed individuare percorsi e relazioni legate alla conoscenza. In un contesto di business, la conoscenza scoperta puo avere un valore strategico per le aziende perche consente di aumentare i profitti, riducendo i costi oppure aumentando le entrate con il conseguente aumento del ROI. Il volume e rivolto sia a studenti universitari e ricercatori, che a professionisti e manager aziendali che vogliano approfondire gli aspetti algoritmici delle tecniche di Data mining: lo studio degli algoritmi e delle principali tecniche e essenziale per conoscere meglio come la tecnologia possa essere applicata ai diversi tipi di dati e quindi anche diverse problematiche di business. Il testo pone volutamente l attenzione sugli aspetti procedurali e di calcolo della metodologia, differenziandosi dagli altri testi in italiano che inquadrano puramente il contesto statistico. Il materiale esposto puo essere utile a quanti vogliano completare la loro formazione scientifica in questa disciplina. "

This easy-to-understand guide makes SEM accessible to all users. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Six Sigma statistical methodology using Minitab

"Problem Solving and Data Analysis using Minitab "presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points.

"Key features: "Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15.Includes fully worked examples with graphics showing menu selections and Minitab outputs.Uses example based learning that the reader can work through at their pace.Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results.Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets.

Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.

Statisticians and researchers will find "Categorical Data Analysis Using SAS, Third Edition," by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, NPAR1WAY, and CATMOD procedures in a variety of analyses. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, loglinear models, generalized estimating equations, and bioassay analysis.

The third edition updates the use of SAS/STAT software to SAS/STAT 12.1 and incorporates ODS Graphics. Many additional SAS statements and options are employed, and graphs such as effect plots, odds ratio plots, regression diagnostic plots, and agreement plots are discussed. The material has also been revised and reorganized to reflect the evolution of categorical data analysis strategies. Additional techniques include such topics as exact Poisson regression, partial proportional odds models, Newcombe confidence intervals, incidence density ratios, and so on.

This is a beginner's guide with clear step-by-step instructions, explanations, and advice. Each concept is illustrated with a complete example that you can use as a starting point for your own work. If you are an engineer, scientist, mathematician, or student, this book is for you. To get the most from Sage by using the Python programming language, we'll give you the basics of the language to get you started. For this, it will be helpful if you have some experience with basic programming concepts.

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's "Data Quality for Analytics Using SAS" focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality.

"Applied Data Mining for Forecasting," by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large and identifies the correlation structure between selected candidate inputs and the forecast variable.

This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs.

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's "Logistic Regression Using SAS: Theory and Application, Second Edition," is for you Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing non-linear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).

For anyone who wants to be operating at a high level with the Excel Solver quickly, this is the book for you. Step-By-Step Optimization With Excel Solver is more than 200+ pages of simple yet thorough explanations on how to use the Excel Solver to solve today's most widely known optimization problems. Loaded with screen shots that are coupled with easy-to-follow instructions, this book will simplify many difficult optimization problems and make you a master of the Excel Solver almost immediately. Here are just some of the Solver optimization problems that are solved completely with simple-to-understand instructions and screen shots in this book: The famous "Traveling Salesman" problem using Solver's Alldifferent constraint and the Solver's Evolutionary method to find the shortest path to reach all customers. This also provides an advanced use of the Excel INDEX function. The well-known "Knapsack Problem" which shows how optimize the use of limited space while satisfying numerous other criteria. How to perform nonlinear regression and curve-fitting on the Solver using the Solver's GRG Nonlinear solving method. How to solve the "Cutting Stock Problem" faced by many manufacturing companies who are trying to determine the optimal way to cut sheets of material to minimize waste while satisfying customer orders. Portfolio optimization to maximize return or minimize risk. Venture capital investment selection using the Solver's Binary constraint to maximize Net Present Value of selected cash flows at year 0. Clever use of the If-Then-Else statements makes this a simple problem. How use Solver to minimize the total cost of purchasing and shipping goods from multiple suppliers to multiple locations. How to optimize the selection of different production machine to minimize cost while fulfilling an order. How to optimally allocate a marketing budget to generate the greatest reach and frequency or number of inbound leads at the lowest cost. Step-By-Step Optimization With Excel Solver has complete instructions and numerous tips on every aspect of operating the Excel Solver. You'll fully understand the reports and know exactly how to tweek all of the Solver's settings for total custom use. The book also provides lots of inside advice and guidance on setting up the model in Excel so that it will be as simple and intuitive as possible to work with. All of the optimization problems in this book are solved step-by-step using a 6-step process that works every time. In addition to detailed screen shots and easy-to-follow explanations on how to solve every optimization problem in the book, a link is provided to download an Excel workbook that has all problems completed exactly as they are in this book. Step-By-Step Optimization With Excel Solver is exactly the book you need if you want to be optimizing at an advanced level with the Excel Solver quickly.

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 are correlation, inferential statistics, descriptive statistics, categorical data analysis, simple linear regression, comparing means, multiple regression, logistic regression, non-parametric tests, 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.

"Carpenter's Guide to Innovative SAS Techniques" offers advanced SAS programmers an all-in-one programming reference that includes advanced topics not easily found outside the depths of SAS documentation or more advanced training classes. Art Carpenter has written fifteen chapters of advanced tips and techniques, including topics on data summary, data analysis, and data reporting. Special emphasis is placed on DATA step techniques that solve complex data problems. There are numerous examples that illustrate advanced techniques that take advantage of formats, interface with the macro language, and utilize the Output Delivery System. Additional topics include operating system interfaces, table lookup techniques, and the creation of customized reports.

Vielfach genutzt fur die Verarbeitung von Daten in Tabellenform, war Excel bisher fur statistische Analysen weniger geeignet. Seit 2009 kann mit dem Add-In RExcel die StatistiksoftwareR eingebunden werden. Der Band bietet die erste Einfuhrung auf Deutsch zur Benutzung der RExcel-Oberflache. Anhand eines Beispieldatensatzes aus der Herz-Kreislaufforschung werden Deskriptive Statistik, Korrelation und Regression, statistische Tests, Uberlebenszeitanalyse sowie Fallzahlplanung nachvollziehbar dargestellt. Mit Schritt-fur Schritt-Anleitungen und Tipps.

A dashboard is a collection of data visualization tools that provide the means to quickly get an overview of how an organization or a section of an organization is performing. Industries such as sales and manufacturing use dashboards extensively, but dashboards are quickly being adapted across all types of profit and non-profit organizations. THE DESIGN OF INFORMATION DASHBOARDS USING SAS is a nuts and bolts guide to building information dashboards using SAS software. The primary audience for this book is SAS programmers charged with developing dashboards for their organization. This audience would include data managers, report writers, and business analysts. A secondary audience includes business mangers and non-programmers who are just hoping to learn a little more about the potential of the technology. The first four chapters provide background on the science of dashboards and related concepts. The remaining chapters cover coding and design of dashboard elements using SAS software. By providing clear, well-structured examples, the volume shows the reader how to quickly and easily construct basic dashboards that are suitable to their unique needs and environment. SAS users familiar with the basics of SAS and the fundamentals of SAS/GRAPH software will be able to make small changes to the sample code contained in the book to design simple dashboards. Advanced users with more extensive knowledge of SAS/GRAPH and the annotate facility will be able to more fully customize the sample code to fit a variety of needs. CHAPTER DESCRIPTIONS Chapter I. AN INTRODUCTION TO DASHBOARDS The first chapter defines precisely what dashboards are and their common characteristics. Following a brief history of information dashboards, the chapter discusses their value, as well as some negatives, and describes current use and trends. Finally, the value that SAS contributes to producing the medium is introduced. Chapter II. SEVEN STEPS TO CREATING A DASHBOARD The development of a dashboard often requires a substantial investment of time and money, so designers should do it thoughtfully. The goal of this chapter is to guide the reader through the dashboard development process. The chapter provides an overview of the major steps involved, including preparation, design, construction, and maintenance of dashboards. Chapter III. ESSENTIAL ELEMENTS OF A DASHBOARD When you create your dashboard, several essential elements should be present on the interface to make the dashboard maximally effective. The third chapter covers these essential components of a dashboard. Chapter IV. BEST PRACTICES IN DASHBOARD VISUAL DESIGN This chapter covers the foundations of good dashboard design and addresses the contributions of Edward Tufte and Stephen Few to the area. The chapter delves into the science of visual perception and how to apply them to good dashboard design. Chapter V. CREATING DASHBOARD KEY PERFORMANCE INDICATORS USING SAS The fifth chapter presents a library of effective dashboard display media and discusses how to produce them using SAS coding. Programmers will be able to pick and choose those chart types that are most appropriate for their particular dashboard. Strengths and weaknesses of the various chart types are discussed. This chapter will also introduces new SAS procedures such as PROC GKPI. Chapter VI. ASSEMBLING AND DISTRIBUTING SAS DASHBOARDS This chapter describes how to bring all the visual components together to produce a single dashboard display. PROC GREPLAY, ODSLAYOUT, and ODS TAGSETS are described as the methods of choice. Methods of distributing this output are described. Chapter VII. DESIGING DASHBOARDS USING SAS BI DASHBOARDS The final chapter briefly describes the design of dashboards using SAS BI Dashboards business intelligence software. For a limited time use the following code for 10% off your purchase on this site: F46FRNCS This title is also available for purchase on Amazon.com.

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); and, m-files (available Aug 1st). This title 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. It offers in-depth coverage of file input/output, a topic essential for many engineering applications. It features systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning. It includes sections on 'common pitfalls' and 'programming guidelines' that direct students towards best practice. The following are new to this edition: more engineering applications that help the reader learn Matlab in the context of solving technical problems; new and revised end of chapter problems; and, stronger coverage of loops and vectorizing in a new chapter, chapter 5. It is updated to reflect current features and functions of the current release of Matlab.

Provides cutting-edge methods, specialized macros, and proven "best bet" procedures. The specialized macros and dozens of real-world examples illustrate solutions for a broad variety of problems that call for multiple inferences. The book also discusses the pitfalls and advantages of various methods, thereby helping you decide which is the most appropriate for your purposes.

This volume contains several contributions on the general theme of dependence for several classes of stochastic processes, andits implicationson asymptoticproperties of various statistics and on statistical inference issues in statistics and econometrics. The chapter by Berkes, Horvath and Schauer is a survey on their recent results on bootstrap and permutation statistics when the negligibility condition of classical central limit theory is not satis ed. These results are of interest for describing the asymptotic properties of bootstrap and permutation statistics in case of in nite va- ances, and for applications to statistical inference, e.g., the change-point problem. The paper by Stoev reviews some recent results by the author on ergodicity of max-stable processes. Max-stable processes play a central role in the modeling of extreme value phenomena and appear as limits of component-wise maxima. At the presenttime, arathercompleteandinterestingpictureofthedependencestructureof max-stable processes has emerged, involvingspectral functions, extremalstochastic integrals, mixed moving maxima, and other analytic and probabilistic tools. For statistical applications, the problem of ergodicity or non-ergodicity is of primary importance.

A user guide that helps you gain a better understanding of the new NVivo 9. Step-by-step instructions are combined with helpful comments to explain the terms used within qualitative analysis. NVivo 9 is a further development of Nvivo 7 and 8 and consistent with the Windows standard. NVivo 9 can handle all languages and alphabets supported by Windows. User interfaces in English, German, French, Portuguese, Spanish, Japanese and Mandarine. NVivo can import, code, and link Word files, PDFs, audio-, video- and picture files. Several categories of nodes can be created: Hierachical nodes, Relationships, and Matrices. The query methods are clearly explained and how to use the saved results. You can also create Graphical Models, Charts, Tree Maps, Word Trees and other visualizations like Cluster analysis. This book can be used as course literature or for self-teaching. A comprehensive yet clear list of contents, glossary, and index ensures the ease of finding the solution to problems that may occur.

SAS/IML software is a powerful tool for data analysts because it enables implementation of statistical algorithms that are not available in any SAS procedure. Rick Wicklin's Statistical Programming with SAS/IML Software is the first book to provide a comprehensive description of the software and how to use it. He presents tips and techniques that enable you to use the IML procedure and the SAS/IML Studio application efficiently. In addition to providing a comprehensive introduction to the software, the book also shows how to create and modify statistical graphs, call SAS procedures and R functions from a SAS/IML program, and implement such modern statistical techniques as simulations and bootstrap methods in the SAS/IML language. Written for data analysts working in all industries, graduate students, and consultants, Statistical Programming with SAS/IML Software includes numerous code snippets and more than 100 graphs.

New and updated for SAS Enterprise Guide 4.2 In this pragmatic, example-driven book, author Neil Constable demonstrates how you can use SAS code to enhance the capabilities of SAS Enterprise Guide. Designed to help you gain extra value from the products you already have, SAS Programming for Enterprise Guide Users contains tips and techniques that show you a variety of features that cannot be accessed directly through the task interfaces. In all cases, techniques are shown with examples that you can try and test, plus additional exercises are included to give you more practice. The end result is more efficient and resilient use of SAS Enterprise Guide in a wider variety of business areas. Included is a discussion of the following subject areas: the Output Delivery System advanced formatting macro variables and macros advanced reporting using PROC REPORT highlighting in reports hyperlinking between reports and graphs data manipulation using SQL data manipulation using the DATA step extended graphics By adding small amounts of code in key areas, SAS Enterprise Guide users can get more out of the product than the tasks reveal. Users should be familiar with the SAS Enterprise Guide user interface and tasks. No programming experience is necessary.