Sas Insight User S Guide
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Sas Insight User S Guide
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Author : SAS Institute
language : en
Publisher:
Release Date : 1990
Sas Insight User S Guide written by SAS Institute and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Sas Insight User S Guide Version 7 1
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Author : SAS Publishing
language : en
Publisher:
Release Date : 1999-04-01
Sas Insight User S Guide Version 7 1 written by SAS Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-04-01 with categories.
Using Sas For Data Management Statistical Analysis And Graphics
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Author : Ken Kleinman
language : en
Publisher: CRC Press
Release Date : 2010-07-28
Using Sas For Data Management Statistical Analysis And Graphics written by Ken Kleinman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-28 with Mathematics categories.
Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsA unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate thro
Sas Insight User S Guide
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Author : SAS Institute
language : en
Publisher:
Release Date : 1995
Sas Insight User S Guide written by SAS Institute and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Mathematical statistics categories.
Sas System For Regression
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Author : Rudolf Freund
language : en
Publisher: John Wiley & Sons
Release Date : 2000-12-29
Sas System For Regression written by Rudolf Freund and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-12-29 with Mathematics categories.
SAS® System for Regression Learn to perform a wide variety of regression analyses using SAS® software with this example-driven revised favorite from SAS Publishing. With this Third Edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics covered include performing linear regression analyses using PROC REG diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. New for this edition The Third Edition includes revisions, updated material, and new material. You’ll find new information on using SAS/INSIGHT® software regression with a binary response with emphasis on PROC LOGISTIC nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, using the OUTEST option to produce a data set, and using PROC SCORE to predict another data set.
Sas Insight User S Guide
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Author : SAS Institute
language : en
Publisher:
Release Date : 1995
Sas Insight User S Guide written by SAS Institute and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Data Mining Using Sas Enterprise Miner
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Author : Randall Matignon
language : en
Publisher: John Wiley & Sons
Release Date : 2007-08-13
Data Mining Using Sas Enterprise Miner written by Randall Matignon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-13 with Mathematics categories.
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.
Unlocking Insights A Comprehensive Guide To Big Data Analytics
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Author : Mothiram Rajasekaran
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-04-26
Unlocking Insights A Comprehensive Guide To Big Data Analytics written by Mothiram Rajasekaran and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Computers categories.
Mothiram Rajasekaran, Senior Solution Consultant, Cloudera, USA.
Sas Insight 9 1 User S Guide
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Author : SAS Institute
language : en
Publisher:
Release Date : 2005*
Sas Insight 9 1 User S Guide written by SAS Institute and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005* with Mathematical statistics categories.
Applied Multivariate Analysis
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Author : Neil H. Timm
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-21
Applied Multivariate Analysis written by Neil H. Timm and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-21 with Mathematics categories.
Univariate statistical analysis is concerned with techniques for the analysis of a single random variable. This book is about applied multivariate analysis. It was written to p- vide students and researchers with an introduction to statistical techniques for the ana- sis of continuous quantitative measurements on several random variables simultaneously. While quantitative measurements may be obtained from any population, the material in this text is primarily concerned with techniques useful for the analysis of continuous obser- tions from multivariate normal populations with linear structure. While several multivariate methods are extensions of univariate procedures, a unique feature of multivariate data an- ysis techniques is their ability to control experimental error at an exact nominal level and to provide information on the covariance structure of the data. These features tend to enhance statistical inference, making multivariate data analysis superior to univariate analysis. While in a previous edition of my textbook on multivariate analysis, I tried to precede a multivariate method with a corresponding univariate procedure when applicable, I have not taken this approach here. Instead, it is assumed that the reader has taken basic courses in multiple linear regression, analysis of variance, and experimental design. While students may be familiar with vector spaces and matrices, important results essential to multivariate analysis are reviewed in Chapter 2. I have avoided the use of calculus in this text.