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.

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.

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.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

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

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.

An accessible and practical tool for effective antenna design

Due to the rapid development of wireless communications, the modeling of radiation and scattering is becoming more important in the design of antennas. Consequently, it is increasingly important for antenna designers and students of antenna design to have a comprehensive simulation tool.

Sergey Makarov’s text utilizes the widely used Matlab software, which offers a more flexible and affordable alternative to other antenna and electromagnetic modeling tools currently available. After providing the basic background in electromagnetic theory necessary to utilize the software, the author describes the benefits and many practical uses of the Matlab package. The text demonstrates how Matlab solves basic radiation/scattering antenna problems in structures that range from simple dipoles to patch antennas and patch antenna arrays. Specialized antenna types like fractal antennas and frequency selective surfaces are considered as well. Finally, the text introduces Matlab applications to more advanced problems such as broadband and loaded antennas, UWB pulse antennas, and microstrip antenna arrays.

For students and professionals in the field of antenna design, Antenna and EM Modeling with Matlab:

• Strikes an important balance between text and programming manual
• Provides numerous examples on how to calculate important antenna/target parameters
• Provides means for modifying existing codes for various individual projects
• Includes a CD-ROM with Matlab codes and antenna geometry files

Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SAS® Software, Second Edition provides a unique approach to this topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information on

• mixed effects models
• applications of the MIXED procedure
• regression diagnostics with the correspoding IML procedure code
• covariance structures.

The authors' approach to the information aids professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding output accompany sample problems, and clear explanations of the various SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples. High-resolution graphs have been used in this new edition.

This introduction has been designed to teach Mathematica as a programming language to scientists, engineers, mathematicians and computer scientists. The text may be used in a first or second course on programming at the undergraduate level or in a Mathematica-related course in engineering, mathematics or the sciences. It is also intended for individual study by students and professionals. The text does not assume familiarity with Mathematica, nor does it require prior programming experience. The book and diskette contain over 200 exercises drawn from many areas of science, engineering, mathematics and computer science.

In statistics, fitting linear models to data is a general theme. This manual describes how GLIM 4--the popular software package--may be used for statistical analysis, including data manipulation and display, model fitting, and prediction. The manual has been divided into three distinct guides. The User Guide introduces and illustrates all the facilities in GLIM 4. Each chapter describes the directives relevant to a particular type of activity involved in the statistical modelling of data. The Modelling Guide presents a broad array of examples which comprise an effective introduction for new users. The Reference Guide contains a formal description of the syntax and semantics of the GLIM 4 language, of the data structures it handles, and of the directives provided, constituting a reference manual for the experienced user. This book is sure to be useful to research statisticians wherever GLIM is used.

Turtle Geometry presents an innovative program of mathematical discovery that demonstrates how the effective use of personal computers can profoundly change the nature of a student's contact with mathematics. Using this book and a few simple computer programs, students can explore the properties of space by following an imaginary turtle across the screen.The concept of turtle geometry grew out of the Logo Group at MIT. Directed by Seymour Papert, author of Mindstorms, this group has done extensive work with preschool children, high school students and university undergraduates. Harold Abelson is an associate professor in the Department of Electrical Engineering and Computer Science at MIT. Andrea diSessa is an associate professor in the Graduate School of Education, University of California, Berkeley.

For courses in Political and Social Statistics Using the popular SPSS software to de-mystify statistics Using SPSS for Windows and Macintosh guides students through basic SPSS techniques, using step-by-step descriptions and explaining in detail how to avoid common pitfalls in the study of statistics. Authors Samuel Green and Neil Salkind provide extensive substantive information about each statistical technique, including a brief discussion of the technique, examples of how the statistic is applied, a sample data set that can be analyzed with the statistic, a discussion of the analysis results, practice exercises, and more. The Eighth Edition has been updated for SPSS version 23 (Windows/Mac), and now offers added accessibility and increased problem solving. NOTE: This ISBN is for a Pearson Books a la Carte edition: a convenient, three-hole-punched, loose-leaf text. In addition to the flexibility offered by this format, Books a la Carte editions offer students great value, as they cost significantly less than a bound textbook.

Essentials of Programming in Mathematica (R) provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica (R) programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author's tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.

DESCRIPTION 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. R in Action, Second Edition is language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. RETAIL SELLING POINTS Step-by-step guide to practical data analysis Gives quick access to the R language Written by seasoned researcher Uses over 130 graphics AUDIENCE This book is for social scientists, medical and pharmaceutical researchers, professors and students in quantitative fields, data analysts, data mining specialists, and statistical programmers who have a sense of what they want to do, but not how to do it in 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

Algorithmen werden von Grundprinzipien bis Implementierungsdetails genau besprochen Verstandlichkeit und Anschaulichkeit mit Schritt-fur-Schritt-Anleitungen Praxisorientierte und verstandlichen Darstellung der Arbeitsweise von Algorithmen Umfangreiche praktische Beispiele aus dem Ingenieuralltag

Dieses Buch bietet eine kurze und verstandliche Einfuhrung in das Softwarepaket MATHEMATICA und zeigt dessen Anwendung auf Problemstellungen aus der Ingenieurmathematik. Zunachst werden der Aufbau, die Arbeitsweise und die Moeglichkeiten von MATHEMATICA naher beschrieben. Anschliessend wird dieses Grundwissen auf die Grundlagen der Ingenieurmathematik, z.B. Matrizen, Differential- und Integralrechnung, angewendet. Der letzte Teil des Buches widmet sich den fortgeschrittenen Themen der Ingenieurmathematik. Dabei werden Differentialgleichungen, Transformationen, Optimierung, Wahrscheinlichkeitsrechnung und Statistik behandelt.Die Berechnungen werden jeweils ausfuhrlich dargestellt und an zahlreichen Beispielen illustriert.

In diesem Buch werden in kompakter Form mithilfe zahlreicher Beispiele die ublichen Modelle und Methoden der angewandten Wahrscheinlichkeitstheorie und Statistik dargestellt. Es ist daher insbesondere fur angehende Wirtschaftswissenschaftler, Ingenieure und Informatiker geeignet, welchen auch das didaktische Konzept des Buchs entgegenkommt: Verstandnisfragen und Aufgaben in Form von "Meilensteinen" erleichtern das eigenstandige UEberprufen des Lernfortschritts. Ein ausfuhrlicher mathematischer Anhang "Mathematik kompakt" stellt die wichtigsten Ergebnisse aus Analysis und linearer Algebra zum effizienten Nachschlagen zur Verfugung. Ein Glossar mit den wichtigsten englischen Begriffen sowie Tabellen der statistischen Testverteilungen runden die Darstellung ab.

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

This textbook explores two distinct stochastic processes that evolve at random: weakly stationary processes and discrete parameter Markov processes. Building from simple examples, the authors focus on developing context and intuition before formalizing the theory of each topic. This inviting approach illuminates the key ideas and computations in the proofs, forming an ideal basis for further study. After recapping the essentials from Fourier analysis, the book begins with an introduction to the spectral representation of a stationary process. Topics in ergodic theory follow, including Birkhoff's Ergodic Theorem and an introduction to dynamical systems. From here, the Markov property is assumed and the theory of discrete parameter Markov processes is explored on a general state space. Chapters cover a variety of topics, including birth-death chains, hitting probabilities and absorption, the representation of Markov processes as iterates of random maps, and large deviation theory for Markov processes. A chapter on geometric rates of convergence to equilibrium includes a splitting condition that captures the recurrence structure of certain iterated maps in a novel way. A selection of special topics concludes the book, including applications of large deviation theory, the FKG inequalities, coupling methods, and the Kalman filter. Featuring many short chapters and a modular design, this textbook offers an in-depth study of stationary and discrete-time Markov processes. Students and instructors alike will appreciate the accessible, example-driven approach and engaging exercises throughout. A single, graduate-level course in probability is assumed.

Hear about student feedback on the character features that make this book special... With an exciting new look, new characters to meet, and its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities. What's brand new: A radical new design with original illustrations and even more colour A maths diagnostic tool to help students establish what areas they need to revise and improve on. A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills New sections on replication, open science and Bayesian thinking Now fully up to date with latest versions of IBM SPSS Statistics (c). All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment. For instructions on how to upload the resources you want, please visit the Instructors' page or alternatively, contact your local SAGE sales representative. Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics. More information on this version of the software's features can be found here.

Statistische Verfahren werden sowohl in der Wirtschaft als auch in den Natur- und Sozialwissenschaften eingesetzt. Die Statistik gilt trotzdem als schwierig. Um diese Hemmschwelle zu uberwinden, geben die Autoren eine didaktisch ausgefeilte, anwendungsbezogene Einfuhrung in die Methoden der deskriptiven Statistik und Datenanalyse. Anhand praxisnaher Beispiele werden die Ideen des Datenmanagements und der Datenauswertung unter Einsatz von SPSS und R beschrieben. Viele Ubungsaufgaben (mit Losungen) unterstutzen das (Selbst-) Studium der Leser. Das Buch deckt den Stoff Statistik I an deutschsprachigen Universitaten vollstandig ab. Neu in dieser Auflage ist eine Einfuhrung in die logistische Regression, deren Konzept auch anhand der statistischen Software SPSS und R erlautert wird. Des Weiteren wurden viele Beispiele und Ubungsaufgaben thematisch uberarbeitet."

The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.