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SAS/ETS User's Guide

SAS/ETS User's Guide PDF Author:
Publisher:
ISBN:
Category : Accounting
Languages : en
Pages : 432

Book Description


SAS/ETS User's Guide

SAS/ETS User's Guide PDF Author:
Publisher:
ISBN:
Category : Accounting
Languages : en
Pages : 432

Book Description


The Little SAS Book

The Little SAS Book PDF Author: Lora D. Delwiche
Publisher: SAS Institute
ISBN: 1642953431
Category : Computers
Languages : en
Pages : 512

Book Description
A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming. Lora Delwiche and Susan Slaughter offer a user-friendly approach so that readers can quickly and easily learn the most commonly used features of the SAS language. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. Nearly every section has been revised to ensure that the sixth edition is fully up-to-date. This edition is also interface-independent, written for all SAS programmers whether they use SAS Studio, SAS Enterprise Guide, or the SAS windowing environment. New sections have been added covering PROC SQL, iterative DO loops, DO WHILE and DO UNTIL statements, %DO statements, using variable names with special characters, the ODS EXCEL destination, and the XLSX LIBNAME engine. This title belongs on every SAS programmer's bookshelf. It's a resource not just to get you started, but one you will return to as you continue to improve your programming skills. Learn more about the updates to The Little SAS Book, Sixth Edition here. Reviews for The Little SAS Book, Sixth Edition can be read here.

The Essential Guide to SAS Dates and Times

The Essential Guide to SAS Dates and Times PDF Author: Derek P. Morgan
Publisher:
ISBN: 9781629590660
Category : SAS (Computer file)
Languages : en
Pages : 0

Book Description
Why does SAS use January 1, 1960 as its arbitrary reference date? How do you convert a value such as 27 January 2003 into a SAS date? How do you put a date into a filename, or label an Excel worksheet with the date? You'll find the answers to these questions and much more in Derek Morgan's Essential Guide to SAS Dates and Times, Second Edition, which makes it easy to understand how to use and manipulate dates, times, and datetimes in SAS. Updated for SAS 9.4, with additional functions, formats, and capabilities, the Second Edition has a new chapter dedicated to the ISO 8601 standard and the formats and functions that are new to SAS, including how SAS works with Universal Coordinated Time (UTC). Novice users will appreciate the new "Troubleshooting" appendix, which discusses questions common to newer SAS users in a conversational way and provides clear examples of simple solutions to these questions. Both novice and intermediate users will find the clear, task-based examples on how to accomplish date-related tasks and the detailed explanations of standard formats and functions invaluable. Users working with intervals will appreciate the expanded discussion of the topic, which details the new custom interval capability, among other enhancements to intervals. Users working with international dates and times will benefit from the detailed discussion of the NLS facility as it relates to dates and times. Included are bonus "Quick Reference Guides" that list both the standard date and time formats and the NLS date and time formats with examples. These guides illustrate how each format displays the same date, time, or datetime, so you can find the format you want to use at a glance. The Essential Guide to SAS Dates and Times, Second Edition is the most complete and up-to-date collection of examples on how to write complex programs involving dates, times, or datetime values. This book is part of the SAS Press Program.

SAS for Forecasting Time Series

SAS for Forecasting Time Series PDF Author: John C. Brocklebank
Publisher: John Wiley & Sons
ISBN: 9780471395669
Category : Mathematics
Languages : en
Pages : 424

Book Description
Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS/390, OS/2, UNIX, and Windows.

SAS for Forecasting Time Series, Third Edition

SAS for Forecasting Time Series, Third Edition PDF Author: John C. Brocklebank, Ph.D.
Publisher: SAS Institute
ISBN: 1629605441
Category : Computers
Languages : en
Pages : 616

Book Description
To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.

Advances in Soft Computing Applications

Advances in Soft Computing Applications PDF Author: Shristi Kharola
Publisher: CRC Press
ISBN: 1000922065
Category : Computers
Languages : en
Pages : 305

Book Description
The proclivity of today’s technology to think like humans may be seen in new developing disciplines such as neural computing, fuzzy logic, evolutionary computation, machine learning, and probabilistic reasoning. These strategies are grouped together into one main technique known as "soft computing." This book discusses the most recent soft computing and fuzzy logic-based applications and innovations in industrial advancements, supply chain and logistics, system optimization, decision-making, artificial intelligence, smart systems, and other rapidly evolving technologies. In today’s competitive world, the book provides soft computing solutions to help companies overcome the obstacles posed by sophisticated decision-making systems.

Applying Data Science

Applying Data Science PDF Author: Gerhard Svolba
Publisher: SAS Institute
ISBN: 1635260566
Category : Computers
Languages : en
Pages : 490

Book Description
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.

The Book of R

The Book of R PDF Author: Tilman M. Davies
Publisher: No Starch Press
ISBN: 1593276516
Category : Computers
Languages : en
Pages : 833

Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

PIRLS 2011 International Results in Reading

PIRLS 2011 International Results in Reading PDF Author: Ina V. S. Mullis
Publisher:
ISBN: 9789079549160
Category :
Languages : en
Pages : 361

Book Description


Data Quality for Analytics Using SAS

Data Quality for Analytics Using SAS PDF Author: Gerhard Svolba
Publisher: SAS Institute
ISBN: 162959802X
Category : Computers
Languages : en
Pages : 429

Book Description
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.