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.

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.

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.

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.

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.

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.

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data.

Fully updated for SAS 9.2, Ron Cody's "SAS Functions by Example, Second Edition," is a must-have reference for anyone who programs in Base SAS. With the addition of functions new to SAS 9.2, this comprehensive reference manual now includes more than 200 functions, including new character, date and time, distance, probability, sort, and special functions. This new edition also contains more examples for existing functions and more details concerning optional arguments.

Like the first edition, the new edition also includes a list of SAS programs, an alphabetic list of all the functions in the book, and a comprehensive index of functions and tasks. Beginning and experienced SAS users will benefit from this useful reference guide to SAS functions.

Bridging the gap between statistics texts and SAS documentation, Elementary Statistics Using SAS is written for those who want to perform analyses to solve problems. The first section of the book explains the basics of SAS data sets and shows how to use SAS for descriptive statistics and graphs. The second section discusses fundamental statistical concepts, including normality and hypothesis testing. The remaining sections of the book show analyses for comparing two groups, comparing multiple groups, fitting regression equations, and exploring contingency tables. For each analysis, author Sandra Schlotzhauer explains assumptions, statistical approach, and SAS methods and syntax, and makes conclusions from the results. Statistical methods covered include two-sample t-tests, paired-difference t-tests, analysis of variance, multiple comparison techniques, regression, regression diagnostics, and chi-square tests. Elementary Statistics Using SAS is a thoroughly revised and updated edition of Ramon Littell and Sandra Schlotzhauer's SAS System for Elementary Statistical Analysis.

Where do I start? How do I know if I'm asking the right questions? How do I analyze the data once I have it? How do I report the results? When will I ever understand the process? If you are new to using the Stata software, and concerned about applying it to a project, help is at hand. David Pevalin and Karen Robson offer you a step by step introduction to the basics of the software, before gently helping you develop a more sophisticated understanding of Stata and its capabilities. The book will guide you through the research process offering further reading where more complex decisions need to be made and giving 'real world' examples from a wide range of disciplines and anecdotes that clarify issues for readers. The book will help with: manipulating and organizing data; generating statistics; interpreting results; and, presenting outputs. "The Stata Survival Manual" is a lifesaver for both students and professionals who are using the Stata software!

This is a tutorial explaining how to use the free and open source mathematical software package Sage (version 6.1.1). Sage and this can be downloaded free from the website: http: //www.sagemath.org/. Copyright: (c) 2014 Creative Commons Attribution-ShareAlike 3.0. Royalties go directly to the Sage Foundation.

In PROC SQL by Example: Using SQL within SAS, author Howard Schreier illustrates the use of PROC SQL in the context of the SAS DATA step and other SAS procedures (such as SORT, FREQ, MEANS, SUMMARY, APPEND, DATASETS, and TRANSPOSE) whose functionality overlaps and complements that of SQL.

Using a side-by-side approach, this concise reference guide includes many extensively explained examples showing equivalent DATA step and SQL code, enabling SAS users to take advantage of existing SAS skills and knowledge while learning about SQL. Discussions cover the differences between SQL and the DATA step as well as situations where SQL and the DATA step are used together to benefit from the strengths of each.

Topics addressed include working with joins and merges; using subqueries; understanding set operators; using the Macro Facility with PROC SQL; maintaining tables; working with views; using PROC SQL as a report generator; and more.

This text is ideal for SAS programmers seeking to add PROC SQL to their SAS toolkits as well as SQL programmers striving to better integrate the SAS DATA step and SQL.

This book provides the optimal introduction to MATLAB and Simulink, the primary tools in engineering, science, and industry for simulating dynamic systems. Using the latest versions of the software, the book also has 20 hands-on projects that provide a practical mastery of the subject areas including the code and executable files. Apart from a basic knowledge of mathematics and physics, no further specialist knowledge is necessary. There are also over 80, in-text, exercises where readers themselves can check their mastery of the material. A CD-ROM with source code accompanies the book.

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including statistics, pattern classification, machine learning, data mining, biology, econometrics, and experimental psychology, as well as philosophers interested in the foundations of statistics. The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern. This extensive, step-by-step introduction to the MDL Principle provides a comprehensive reference (with an emphasis on conceptual issues) that is accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection, including biology, econometrics, and experimental psychology. Part I provides a basic introduction to MDL and an overview of the concepts in statistics and information theory needed to understand MDL. Part II treats universal coding, the information-theoretic notion on which MDL is built, and part III gives a formal treatment of MDL theory as a theory of inductive inference based on universal coding. Part IV provides a comprehensive overview of the statistical theory of exponential families with an emphasis on their information-theoretic properties. The text includes a number of summaries, paragraphs offering the reader a "fast track" through the material, and boxes highlighting the most important concepts.

Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!

Offering extensive coverage of cutting-edge biostatistical methodology used in drug development, this essential reference explores the practical problems facing today's drug developers. It is written by well-known experts in the pharmaceutical industry and provides relevant tutorial material and SAS examples.

Art Carpenter demystifies the powerful REPORT procedure and shows you how to incorporate this highly flexible and customizable procedure into your SAS reporting programs. Combining his years of SAS experience with a talent for instruction, Art offers clear and comprehensive coverage that demonstrates how valuable this procedure is for both summarizing and displaying data. Illustrated with more than two hundred examples and sample exercises to reinforce your learning, "Carpenter's Complete Guide to the SAS REPORT Procedure" provides you with information that you can put to immediate use.

The text is divided into three distinct sections. Part 1 introduces you to PROC REPORT, showing you how it works and "thinks." This section is designed to be read linearly by users who are unfamiliar with the procedure. Part 2 is a collection of increasingly more complex examples that feature advanced options and capabilities. It also introduces the relationship between PROC REPORT and the Output Delivery System (ODS). Part 3 incorporates the options and statements described in Parts 1 and 2 into a series of examples that highlight many of the extended capabilities of PROC REPORT. Included in this section is a discussion of a few ODS statements and options that might be useful to a PROC REPORT programmer, plus an in-depth look at the PROC REPORT process itself, especially as it relates to the execution of compute blocks.

Art's author page at support.sas.com/carpenter includes the following bonus material: example SAS data sets, example results, and a compilation of nearly 100 related conference papers.

Navigate the world of the powerful SQL procedure with Katherine Prairie's Essential PROC SQL Handbook for SAS Users. Written in an easy-to-use, logical format, this comprehensive reference focuses on the functionality of the procedure, as well as the accomplishment of common tasks using PROC SQL, enabling readers to quickly develop and enhance their SQL skills. Features include more than 300 examples of PROC SQL code, plus queries and diagrams showing how the statements are processed, tips and techniques highlighting "need-to-know" concepts, and an appendix designed specifically for SQL Pass-Through Facility and SAS/ACCESS users. This practical guide is written for SAS users of all levels who want to learn how to integrate the SQL procedure into their Base SAS and/or SAS/ACCESS programs as well as SQL programmers who want to adapt their current skills to SAS.

Proven bestseller: almost 6000 copies sold in the U.S. in two editions

New edition updated to cover S-PLUS 6.0

Can be used as an introduction to R, as well as S-PLUS

New exercises have been added; Includes a comparison of S-PLUS and R

Well-suited for self-study

Updated for SAS 9, A Step-by-Step Approach to Using SAS for Univariate and Multivariate Statistics, Second Edition, is an easy-to-understand introduction to SAS as well as to univariate and multivariate statistics. Clear explanations and simple language guide you through the research terminology, data input, data manipulation, and types of statistical analysis that are most commonly used in the social and behavioral sciences. Providing practice data inspired by actual studies, this book teaches you how to choose the right statistic, understand the assumptions underlying the procedure, prepare the SAS program for the analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association. Step by step, authors Norm O'Rourke, Larry Hatcher, and Edward Stepanski demonstrate how to perform the following types of analysis: simple descriptive statistics, measures of bivariate association, t tests for independent samples and paired samples, ANOVA and MANOVA, multiple regression, principal component analysis, and assessing scale reliability with coefficient alpha. This text is ideally suited to students who are beginning their study of data analysis, and to professors and researchers who want a handy reference on their bookshelf.

Do you need to conduct data analysis with SPSS but are unfamiliar with the software? This user-friendly book-a SAGE bestseller-helps you become proficient in SPSS by teaching you the fundamentals of SPSS. An Introductory Guide to SPSS (R) for Windows (R), Second Edition develops SPSS skills using sample programs illustrating how to conduct the analyses typically covered in an introductory statistics course. Throughout the book, data are analyzed and SPSS output are interpreted in the context of research questions. Boldface text is used to indicate operations or choices the reader will need to make when running SPSS. Exercises are also included, with solutions provided in the appendix. "I want to commend the author for including a chapter on using the SPSS manuals. I would not have thought of this addition, and the information helps the reader go on to learn the more complex components of SPSS." -Laura Myers, University of Georgia This Second Edition has been updated to SPSS Version 12.0, although its approach makes it useful for readers running other versions. Each chapter in this updated text includes a statement of its purpose and goal, as well as a chapter glossary. The updated text includes new SPSS features, including how to recode data using the Visual Bander and how to read text data using the Text Import Wizard. Author Eric L. Einspruch thoroughly covers critical basic skills: * How to create data sets by defining and coding data, using a codebook, and entering data * How to run SPSS and work with different SPSS files * How to manipulate data by recoding values, computing values, and selecting subsets of cases to include in an analysis * How to manage data files by reading data that have been entered using other software, appending files, and merging files * How to analyze data using SPSS pull-down menus * How to analyze data using programs written in SPSS syntax This outstanding book concludes with a discussion of how to get help in SPSS, suggestions on how to make the most of SPSS manuals, and directions for taking the next steps toward software mastery.

The information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations.

The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented.

All exercises involve only “real” data, collected in the course of scientific research. The book is divided into sections covering the following topics:

• Basic Theory
• Multiple Regression
• Model Building and Evaluation
• ANOVA
• ANCOVA

This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.