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Statistics Using Sas Enterprise Guide

Author: James B. Davis, Ph.D.
Publisher: SAS Institute
ISBN: 1599947242
Size: 40.45 MB
Format: PDF, Kindle
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Discover how easy it is to perform statistical analysis using the power of SAS Enterprise Guide. Suitable for students new to statistics and to SAS, as well as for experienced professionals, James Davis's Statistics Using SAS Enterprise Guide provides an introduction to the basics of SAS Enterprise Guide and to statistics. Early chapters in this easy-to-follow book address topics such as how to work with data sets (including SAS data sets, data sets in Microsoft Excel, and other formats) and how to perform queries. A separate chapter on descriptive statistics offers a wide range of techniques and multiple presentation options. Later chapters provide detailed discussions (without calculus) of statistical theory and step-by-step examples that illustrate how to apply the appropriate SAS Enterprise Guide tasks, including both complete output and thorough analyses of the results. These chapters present examples of one-sample inference, two-sample inference, analysis of variance, correlation and regression, and table analysis

Data Analysis Using Sas Enterprise Guide

Author: Lawrence S. Meyers
Publisher: Cambridge University Press
ISBN: 0521112680
Size: 44.18 MB
Format: PDF, Kindle
View: 7449
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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.

Basic Statistics Using Sas Enterprise Guide

Author: Geoff Der
Publisher: SAS Institute
ISBN: 1599947153
Size: 51.27 MB
Format: PDF, ePub
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This example-rich guide shows you how to conduct a wide range of statistical analyses with no SAS programming required. For each analysis, one or more real data sets, a brief introduction of the technique, and a clear explanation of the SAS Enterprise Guide output are provided.

Datenanalyse Mit Sas

Author: Walter Krämer
Publisher: Springer-Verlag
ISBN: 354026776X
Size: 56.11 MB
Format: PDF, ePub
View: 1972
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Das Programmpaket SAS hat sich im Lauf der Jahre als ein Standardwerkzeug zur statistischen Datenanalyse durchgesetzt. Der souveräne Umgang mit statistischen Methoden und deren praktischer Umsetzung in SAS bietet somit einen unschätzbaren Vorteil für die tägliche Arbeit aller Anwender, die in Studium oder Beruf mit der Analyse von Daten zu tun haben. Das vorliegende Buch erläutert alle gängigen statistischen Verfahren und deren Umsetzung als SAS-Programm. Es ist als Einführung in SAS geeignet, aber auch als Nachschlagewerk für Fortgeschrittene, mit deutlicher Betonung der grafischen Aspekte der statistischen Datenanalyse. Anleitungen zur Programmierung mit IML und Makros sowie hilfreiche Assistenten in SAS runden die Darstellung ab.

Data Mining Using Sas Enterprise Miner

Author: Randall Matignon
Publisher: John Wiley & Sons
ISBN: 0470149019
Size: 80.60 MB
Format: PDF, ePub, Docs
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The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include: The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.

Exploratory Data Analysis An Introduction To Data Analysis Using Sas

Author: Patricia Cerrito
Publisher: Lulu.com
ISBN: 1435705424
Size: 33.72 MB
Format: PDF, Docs
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This is an introductory text on how to investigate datasets. It is intended to be a practical text for those who need to research large datasets. Therefore, it does not follow the standard contents for more typical introductory statistics textbooks. When you complete the material, you will be able to work with your data using data visualization and regression in order to make sense of it, and to use your findings to make decisions. The book makes use of the statistical software, SAS, and its menu system SAS Enterprise Guide. This can be used as a stand alone text, or as a supplementary text to a more standard course. There are some datasets to accompany this text. ID# 1640751, Data for Exploratory Data Analysis.

Neural Network Modeling Using Sas Enterprise Miner

Author: Randall Matignon
Publisher: AuthorHouse
ISBN: 1418423416
Size: 75.16 MB
Format: PDF, ePub, Docs
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No Childhood is a collection of incidents, written from a child's point of view. It describes the feelings of a little girl, who never really was a child. Some of her questions and lots of her constant fears are brought to the surface. It gives insight into the mixed emotions of the little girl and the adults around her. The little girl was one of the lucky ones who survived the holocaust. But there was suffering too. And yet, among all the evil of the times, there were the good people. The family, the teacher, the nuns, the dentist, who all had a part in saving the little girl's life.

Business Analytics Using Sas Enterprise Guide And Sas Enterprise Miner

Author: Olivia Parr-Rud
Publisher: SAS Institute
ISBN: 1629593281
Size: 64.75 MB
Format: PDF, ePub, Docs
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This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. Today’s businesses increasingly use data to drive decisions that keep them competitive. Especially with the influx of big data, the importance of data analysis to improve every dimension of business cannot be overstated. Data analysts are therefore in demand; however, many hires and prospective hires, although talented with respect to business and statistics, lack the know-how to perform business analytics with advanced statistical software. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner is a beginner’s guide with clear, illustrated, step-by-step instructions that will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. This book is part of the SAS Press program.

Statistical Data Analysis Using Sas

Author: Mervyn G. Marasinghe
Publisher: Springer
ISBN: 3319692399
Size: 72.82 MB
Format: PDF, ePub
View: 6148
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The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: • Covers SAS v9.2 and incorporates new commands • Uses SAS ODS (output delivery system) for reproduction of tables and graphics output • Presents new commands needed to produce ODS output • All chapters rewritten for clarity • New and updated examples throughout • All SAS outputs are new and updated, including graphics • More exercises and problems • Completely new chapter on analysis of nonlinear and generalized linear models • Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

Predictive Modeling With Sas Enterprise Miner

Author: Kattamuri S. Sarma
Publisher: SAS Institute
ISBN: 1635260388
Size: 79.79 MB
Format: PDF, ePub, Mobi
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A step-by-step guide to predictive modeling! Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner!