Multiple Imputation of Missing Data Using SAS

Multiple Imputation of Missing Data Using SAS
Author: Patricia Berglund,Steven G. Heeringa
Publsiher: SAS Institute
Total Pages: 164
Release: 2014-07
ISBN: 1629592056
Category: Computers
Language: EN, FR, DE, ES & NL

Multiple Imputation of Missing Data Using SAS Book Excerpt:

Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. It provides both theoretical background and practical solutions for those working with incomplete data sets in an engaging example-driven format.

Multiple Imputation of Missing Data Using SAS Hardcover Edition

Multiple Imputation of Missing Data Using SAS  Hardcover Edition
Author: Patricia Berglund,Steven Heeringa
Publsiher: Unknown
Total Pages: 164
Release: 2014-07-16
ISBN: 9781642955217
Category: Computers
Language: EN, FR, DE, ES & NL

Multiple Imputation of Missing Data Using SAS Hardcover Edition Book Excerpt:

Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action.

Multiple Imputation of Missing Data Using SAS

Multiple Imputation of Missing Data Using SAS
Author: Patricia Berglund,Steven Heeringa
Publsiher: Sas Inst
Total Pages: 164
Release: 2014-07-01
ISBN: 9781612904528
Category: Computers
Language: EN, FR, DE, ES & NL

Multiple Imputation of Missing Data Using SAS Book Excerpt:

Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. It provides both theoretical background and practical solutions for those working with incomplete data sets in an engaging example-driven format.

Multiple Imputation of Missing Data in Practice

Multiple Imputation of Missing Data in Practice
Author: Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu
Publsiher: CRC Press
Total Pages: 419
Release: 2021-11-20
ISBN: 0429530978
Category: Mathematics
Language: EN, FR, DE, ES & NL

Multiple Imputation of Missing Data in Practice Book Excerpt:

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

Data Mining Using SAS Enterprise Miner

Data Mining Using SAS Enterprise Miner
Author: Randall Matignon
Publsiher: John Wiley & Sons
Total Pages: 576
Release: 2007-07-27
ISBN: 0470171421
Category: Mathematics
Language: EN, FR, DE, ES & NL

Data Mining Using SAS Enterprise Miner Book Excerpt:

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.

Multiple Imputation in Practice

Multiple Imputation in Practice
Author: Trivellore Raghunathan,Patricia A. Berglund,Peter W. Solenberger
Publsiher: CRC Press
Total Pages: 246
Release: 2018-07-20
ISBN: 1351650319
Category: Mathematics
Language: EN, FR, DE, ES & NL

Multiple Imputation in Practice Book Excerpt:

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.

Data Quality for Analytics Using SAS

Data Quality for Analytics Using SAS
Author: Gerhard Svolba
Publsiher: SAS Institute
Total Pages: 356
Release: 2012-04-01
ISBN: 1612902278
Category: COMPUTERS
Language: EN, FR, DE, ES & NL

Data Quality for Analytics Using SAS Book Excerpt:

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. This book is part of the SAS Press program.

Handbook of Statistical Analysis and Data Mining Applications

Handbook of Statistical Analysis and Data Mining Applications
Author: Robert Nisbet,Gary Miner,Ken Yale
Publsiher: Elsevier
Total Pages: 822
Release: 2017-11-09
ISBN: 0124166458
Category: Mathematics
Language: EN, FR, DE, ES & NL

Handbook of Statistical Analysis and Data Mining Applications Book Excerpt:

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Applied Medical Statistics Using SAS

Applied Medical Statistics Using SAS
Author: Geoff Der,Brian S. Everitt
Publsiher: CRC Press
Total Pages: 559
Release: 2012-10-01
ISBN: 1439867984
Category: Mathematics
Language: EN, FR, DE, ES & NL

Applied Medical Statistics Using SAS Book Excerpt:

Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudi

Advances in Computing and Data Sciences

Advances in Computing and Data Sciences
Author: Mayank Singh,P. K. Gupta,Vipin Tyagi,Jan Flusser,Tuncer Ören
Publsiher: Springer
Total Pages: 578
Release: 2018-10-25
ISBN: 9811318131
Category: Computers
Language: EN, FR, DE, ES & NL

Advances in Computing and Data Sciences Book Excerpt:

This two-volume set (CCIS 905 and CCIS 906) constitutes the refereed proceedings of the Second International Conference on Advances in Computing and Data Sciences, ICACDS 2018, held in Dehradun, India, in April 2018. The 110 full papers were carefully reviewed and selected from 598 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations.

Customer Segmentation and Clustering Using SAS Enterprise Miner Third Edition

Customer Segmentation and Clustering Using SAS Enterprise Miner Third Edition
Author: Randall S. Collica
Publsiher: SAS Institute
Total Pages: 356
Release: 2017-03-23
ISBN: 1629605271
Category: Computers
Language: EN, FR, DE, ES & NL

Customer Segmentation and Clustering Using SAS Enterprise Miner Third Edition Book Excerpt:

Understanding your customers is the key to your company’s success! Segmentation is one of the first and most basic machine learning methods. It can be used by companies to understand their customers better, boost relevance of marketing messaging, and increase efficacy of predictive models. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition, Randy Collica explains, in step-by-step fashion, the most commonly available techniques for segmentation using the powerful data mining software SAS Enterprise Miner. A working guide that uses real-world data, this new edition will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. Step-by-step examples and exercises, using a number of machine learning and data mining techniques, clearly illustrate the concepts of segmentation and clustering in the context of customer relationship management. The book includes four parts, each of which increases in complexity. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics, such as when and how to update your models. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner. Finally, part 4 takes segmentation to a new level with advanced techniques, such as clustering of product associations, developing segmentation-scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions. New to the third edition is a chapter that focuses on predictive models within microsegments and combined segments, and a new parallel process technique is introduced using SAS Factory Miner. In addition, all examples have been updated to the latest version of SAS Enterprise Miner.

Machine Learning Optimization and Big Data

Machine Learning  Optimization  and Big Data
Author: Giuseppe Nicosia,Panos Pardalos,Giovanni Giuffrida,Renato Umeton
Publsiher: Springer
Total Pages: 600
Release: 2017-12-19
ISBN: 3319729268
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning Optimization and Big Data Book Excerpt:

This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Advances in Intelligent IT

Advances in Intelligent IT
Author: Yuefeng Li,Mark Looi,Ning Zhong
Publsiher: IOS Press
Total Pages: 473
Release: 2006
ISBN: 1586036157
Category: Computers
Language: EN, FR, DE, ES & NL

Advances in Intelligent IT Book Excerpt:

" In the great digital era, we are witnessing many rapid scientific and technological developments in human-centered, seamless computing environments, interfaces, devices and systems with applications ranging from business and communication to entertainment and learning. These developments are collectively best characterized as Active Media Technology (AMT), a new area of intelligent information technology and computer science that emphasizes the proactive, seamless roles of interfaces and systems as well as new media in all aspects of digital life. An AMT based computer system offers services that enable the rapid design, implementation, deploying and support of customized solutions. This book brings together papers from researchers from diverse areas, such as Web intelligence, data mining, intelligent agents, smart information use, networking and intelligent interface. The book includes papers on the following topics: Active Computer Systems and Intelligent Interfaces; Adaptive Web Systems and Information Foraging Agents; Web mining, Wisdom Web and Web Intelligence; E-Commerce and Web Services; Data Mining, Ontology Mining and Data Reasoning; Network, Mobile and Wireless Security; Entertainment and Social Applications of Active Media; Agent-Based Software Engineering and Multi-Agent Systems; Digital City and Digital Interactivity; Machine Learning and Human-Centered Robotics; Multi-Modal Processing, Detection, Recognition, and Expression Analysis; Personalized, Pervasive, and Ubiquitous Systems and their Interfaces; Smart Digital Media; and Evaluation of Active Media and AMT Based Systems. "

Advances in Intelligent IT

Advances in Intelligent IT
Author: Y. Li,M. Looi,N. Zhong
Publsiher: IOS Press
Total Pages: 472
Release: 2006-05-09
ISBN: 1607501724
Category: Computers
Language: EN, FR, DE, ES & NL

Advances in Intelligent IT Book Excerpt:

In the great digital era, we are witnessing many rapid scientific and technological developments in human-centered, seamless computing environments, interfaces, devices and systems with applications ranging from business and communication to entertainment and learning. These developments are collectively best characterized as Active Media Technology (AMT), a new area of intelligent information technology and computer science that emphasizes the proactive, seamless roles of interfaces and systems as well as new media in all aspects of digital life. An AMT based computer system offers services that enable the rapid design, implementation, deploying and support of customized solutions. This book brings together papers from researchers from diverse areas, such as Web intelligence, data mining, intelligent agents, smart information use, networking and intelligent interface. The book includes papers on the following topics: Active Computer Systems and Intelligent Interfaces; Adaptive Web Systems and Information Foraging Agents; Web mining, Wisdom Web and Web Intelligence; E-Commerce and Web Services; Data Mining, Ontology Mining and Data Reasoning; Network, Mobile and Wireless Security; Entertainment and Social Applications of Active Media; Agent-Based Software Engineering and Multi-Agent Systems; Digital City and Digital Interactivity; Machine Learning and Human-Centered Robotics; Multi-Modal Processing, Detection, Recognition, and Expression Analysis; Personalized, Pervasive, and Ubiquitous Systems and their Interfaces; Smart Digital Media; and Evaluation of Active Media and AMT Based Systems.

Applying Data Science

Applying Data Science
Author: Gerhard Svolba
Publsiher: SAS Institute
Total Pages: 490
Release: 2017-03-29
ISBN: 1635260566
Category: Computers
Language: EN, FR, DE, ES & NL

Applying Data Science Book Excerpt:

See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.

Analyzing Longitudinal Clinical Trial Data

Analyzing Longitudinal Clinical Trial Data
Author: Craig Mallinckrodt,Ilya Lipkovich
Publsiher: CRC Press
Total Pages: 302
Release: 2016-12-12
ISBN: 1351737694
Category: Mathematics
Language: EN, FR, DE, ES & NL

Analyzing Longitudinal Clinical Trial Data Book Excerpt:

Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.

Multiple Imputation and its Application

Multiple Imputation and its Application
Author: James Carpenter,Michael Kenward
Publsiher: John Wiley & Sons
Total Pages: 368
Release: 2012-12-21
ISBN: 1119942276
Category: Medical
Language: EN, FR, DE, ES & NL

Multiple Imputation and its Application Book Excerpt:

A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.

Flexible Imputation of Missing Data

Flexible Imputation of Missing Data
Author: Stef van Buuren
Publsiher: CRC Press
Total Pages: 344
Release: 2012-03-29
ISBN: 1439868247
Category: Mathematics
Language: EN, FR, DE, ES & NL

Flexible Imputation of Missing Data Book Excerpt:

Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science—multiple imputation—fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise. Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author’s package MICE is included throughout the book. Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences: (Bio)statisticians, epidemiologists, and methodologists in the social and health sciences Substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.

Big Data Analytics with SAS

Big Data Analytics with SAS
Author: David Pope
Publsiher: Packt Publishing Ltd
Total Pages: 266
Release: 2017-11-23
ISBN: 1788294319
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Analytics with SAS Book Excerpt:

Leverage the capabilities of SAS to process and analyze Big Data About This Book Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SAS Who This Book Is For SAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory. What You Will Learn Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting. Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis. Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages. Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems. In Detail SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one's career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data. The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS's architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R. By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS. Style and approach The book starts off by introducing the reader to SAS and the SAS programming language which provides data management, analytical, and reporting capabilities. Most chapters include hands on examples which highlights how SAS provides The Power to Know©. The reader will learn that if they are looking to perform large-scale data analysis that SAS provides an open platform engineered and designed to scale both up and out which allows the power of SAS to combine with open source offerings such as Hadoop, Python, and R.

A Beginner s Guide to Structural Equation Modeling

A Beginner s Guide to Structural Equation Modeling
Author: Tiffany A. Whittaker,Randall E. Schumacker
Publsiher: Routledge
Total Pages: 418
Release: 2022-04-27
ISBN: 1000569748
Category: Psychology
Language: EN, FR, DE, ES & NL

A Beginner s Guide to Structural Equation Modeling Book Excerpt:

A Beginner’s Guide to Structural Equation Modeling, fifth edition, has been redesigned with consideration of a true beginner in structural equation modeling (SEM) in mind. The book covers introductory through intermediate topics in SEM in more detail than in any previous edition. All of the chapters that introduce models in SEM have been expanded to include easy-to-follow, step-by-step guidelines that readers can use when conducting their own SEM analyses. These chapters also include examples of tables to include in results sections that readers may use as templates when writing up the findings from their SEM analyses. The models that are illustrated in the text will allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models, up to testing higher order models as well as multiple group modeling techniques. Updated information about methodological research in relevant areas will help students and researchers be more informed readers of SEM research. The checklist of SEM considerations when conducting and reporting SEM analyses is a collective set of requirements that will help improve the rigor of SEM analyses. This book is intended for true beginners in SEM and is designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and healthcare sciences. This book also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.