DOCSLIB.ORG

SAS/STAT® Software

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
SAS/STAT® Software FACT SHEET SAS/STAT® Software What does SAS/STAT® do? From analysis of variance and linear regression to Bayesian inference and high-per- formance modeling tools for massive data, SAS/STAT software provides tools for both specialized and enterprisewide statistical needs. Why is SAS/STAT® important? Organizations of all types and sizes depend on statistical analysis to guide critical deci- sions. Modern statistical methods provide trustworthy evidence for creating effective treat- ments for disease, improving manufacturing processes, predicting customer behavior and making policy decisions. SAS/STAT provides a comprehensive set of up-to-date tools that can meet the data analysis needs of your entire organization. For whom is SAS/STAT® designed? It is designed for use by business analysts, statisticians, data scientists, researchers and engineers. Organizations in every field depend on data Benefits • Use proven and validated methods. and analysis to provide new insights, gain SAS has decades of experience devel- • Apply the latest statistical techniques. competitive advantage and make informed oping advanced statistical analysis With an accelerated release schedule, decisions. For many organizations, the software designed for superior, reliable SAS/STAT keeps up with new methods complexity and volume of their data has results. A rigorous software testing and emerging from the rapidly expanding outgrown the capabilities of other statistical quality assurance program means you field of statistics. software. can count on the quality of each release. • Analyze any kind and size of data. With SAS/STAT, you can produce code SAS/STAT software provides extensive statis- SAS/STAT includes exact techniques for that is documented and verified for tical capabilities that scale to meet the small data sets, high-performance statis- corporate and governmental compli- expanding requirements of specialized and tical modeling tools for large-data tasks ance requirements. enterprisewide analysis. and modern methods for analyzing data with missing values. Whether you want to perform a customer • Readily understand results with a wealth preference study, analyze the results of of graphs. SAS/STAT output provides clinical trials, predict credit card usage hundreds of built-in, customizable patterns, model air pollution patterns or graphs that are designed for a consistent design a health behavior survey, SAS/STAT look across analyses. is the software of choice. • Take advantage of our technical support and web user communities. Backed by And because SAS remains committed to its industry-leading statistical technical long tradition of constantly enriching its support, SAS/STAT software is the statistical offerings, you’ll continue to have complete answer to a broad spectrum the most up-to-date statistical techniques. of statistical needs. Overview Statistics is a rapidly expanding discipline, and SAS/STAT is keeping pace. An accelerated release schedule delivers more state-of-the- art statistical methods and high-performance computational tools, along with user- requested enhancements. SAS/STAT soft- ware provides the foundation for many SAS Analytic offerings. It delivers a complete, comprehensive set of statistical tools that can meet the data analysis requirements of your entire organization. Expansive library of ready-to-use statistical procedures With SAS/STAT, you get more than 90 prewritten procedures for statistical analysis. These procedures encapsulate and deliver significant functionality that can be executed with just a few simple commands, enabling programmers to be more efficient and Figure 1: Plot of component densities for a finite mixture model created with the productive. This wide range of robust statis- FMM procedure. tical methods can help you solve your most complicated business and scientific problems, such as uncovering new informa- tion for improving processes, driving devel- opment and revenues and retaining valued and satisfied customers. Highly interpretable statistical output Clarity and consistency of statistical output, including a wealth of built-in graphs, enable users to readily understand analysis results. Comprehensive documentation and training Extensive online documentation, including a rich set of introductory examples, allows users to get up and running with the software quickly and effectively. Free “how to” videos, tutorials and demos help you build your knowledge of statistical methods and learn how to apply SAS/STAT in your work. Cross-platform support and scalability Figure 2: A model-building progress plot created with the GLMSELECT procedure. SAS runs on all major computing platforms and can access nearly any data source. The technology easily integrates into any organi- zation’s computing environment and can scale as you face larger or more complex analytical problems. Key Features Analysis of Variance • Stepwise discriminant analysis. Missing Data Analysis • Balanced and unbalanced designs. • More discriminant analysis capabilities. • Multiple imputation. • Multivariate analysis of variance and • Weighted generalized estimating repeated measurements. equations. Distribution Analysis • Linear models. • Imputation for survey data. • Univariate and bivariate kernel • More analysis of variance capabilities. • More missing data analysis capabilities. density estimation. • More distribution analysis Bayesian Analysis capabilities. Mixed Models • Built-in Bayesian modeling and infer- • Linear and nonlinear mixed models. ence for generalized linear models, • Generalized linear mixed models. Exact Inference accelerated failure time models, Cox • Nested models. • Exact p-values and confidence regression models and finite mixture • More mixed models capabilities. intervals for many test statistics and models. measures based on one-way and • Wide range of Bayesian models n-way frequency tables. Model Selection available via general-purpose • Exact tests for the parameters of • Linear models. MCMC simulation procedure. a logistic regression model. • Generalized linear models. • Bayesian discrete choice modeling. • Exact tests for the parameters of a • Quantile regression models. • More Bayesian analysis capabilities. Poisson regression model. • More model selection capabilities. • More exact methods capabilities. Categorical Data Analysis Multivariate Analysis • Contingency tables and measures of Finite Mixture Models • Exploratory and confirmatory factor association. • Modeling of component distribu- analysis. • Bioassay analysis. tions and mixing probabilities. • Principal components analysis. • Generalized linear models. • Maximum likelihood and Bayesian • Canonical correlation and partial • More categorical data analysis methods. canonical correlation. capabilities. • More finite mixture capabilities. • More multivariate analysis capabilities. Causal Inference Group Sequential Design and Nonlinear Regression • Propensity score analysis. Analysis • Automatic derivatives. • Estimation of causal treatment effects. • Design of interim analyses. • Bootstrapped confidence intervals. • Perform interim analyses. • More nonlinear regression Cluster Analysis • More on group sequential design capabilities. • Hierarchical clustering of multivariate and analysis capabilities. data or distance data. Nonparametric Analysis • Disjoint clustering of large data sets. Longitudinal Data Analysis • Kruskal-Wallis, Wilcoxon-Mann- • Nonparametric clustering with • Marginal and mixed models. Whitney and Friedman tests. hypothesis tests for the number of • Continuous and categorical • Other rank tests for balanced or clusters. outcomes. unbalanced one-way or two-way • More cluster analysis capabilities. • More longitudinal data analysis designs. capabilities. • Exact probabilities for many Descriptive Statistics nonparametric statistics. • Box-and-whisker plots. • More nonparametric analysis Market Research • Compute directly and indirectly capabilities. • Simple and multiple correspondence standardized rates and risks for study analysis. populations. • Two-way and three-way metric and Nonparametric Regression • More descriptive statistics capabilities. nonmetric multidimensional scaling • Multivariate adaptive regression models. splines. Discriminant Analysis • Discrete choice models. • Generalized additive models. • Canonical discriminant analysis. • More market research capabilities. • Local regression. Key Features (continued) • Thin-plate smoothing splines. • Model selection for linear regression • Missing value imputation. • More nonparametric regression models. • More survey sampling and analysis capabilities. • More quantile regression capabilities. capabilities. Power and Sample Size Regression Survival Analysis • Computations for linear models • Least squares regression. • Nonparametric survival function including MANOVA repeated • Principal components regression. estimates. measurements. • Quadratic response surface models. • Competing-risk models. • Computations for many hypothesis • Accurate estimation for ill-conditioned • Accelerated failure time models. tests, equivalence tests and correla- data. • Proportional hazards models. tion analysis. • More regression capabilities. • Interval-censored data analysis. • Computations for binary logistic • More survival analysis capabilities. regression and survival analysis. • More power and sample size Robust Regression capabilities. • M estimation and high-breakdown High Performance methods. • 14 SAS/STAT procedures are • Outlier diagnostics. multithreaded. Post Processing
Load more

Recommended publications
  • Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
    UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done.
    [Show full text]
  • Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc
    PharmaSUG 2018 - Paper AD-12 Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc. ABSTRACT Day to day work can often require simple, yet repetitive tasks. All companies have tedious processes that may involve moving and renaming files, making redundant edits to code, and more. Often, one might also want to find or summarize information scattered amongst many different files as well. While SAS programmers are very familiar with the power of SAS, the power of some lesser known but readily available tools may go unnoticed. The programming language Python has many capabilities that can easily automate and facilitate these tasks. Additionally, many companies have adopted Linux and UNIX as their OS of choice. With a small amount of background, and an understanding of Linux/UNIX command line syntax, powerful tools can be developed to eliminate these tedious activities. Better yet, these tools can be written to “talk back” to the user, making them more user friendly for those averse to venturing from their SAS editor. This paper will explore the benefits of how Python can be adopted into a SAS programming world. INTRODUCTION SAS is a great language, and it does what it’s designed to do very well, but other languages are better catered to different tasks. One language in particular that can serve a number of different purposes is Python. This is for a number of reasons. First off, it’s easy. Python has a very spoken-word style of syntax that is both intuitive to use and easy to learn. This makes it a very attractive language for newcomers, but also an attractive language to someone like a SAS programmer.
    [Show full text]
  • Base SAS® Software Flexible and Extensible Fourth-Generation Programming Language Designed for Data Access, Transformation and Reporting
    FACT SHEET Base SAS® Software Flexible and extensible fourth-generation programming language designed for data access, transformation and reporting What does Base SAS® software do? Base SAS is a fourth-generation programming language (4GL) for data access, data transformation, analysis and reporting. It is included with the SAS Platform. Base SAS is designed for foundational data manipulation, information storage and retrieval, descrip- tive statistics and report writing. It also includes a powerful macro facility that reduces programming time and maintenance headaches. Why is Base SAS® software important? Base SAS runs on all major operating systems. It significantly reduces programming and maintenance time, while enabling your IT organization to produce the analyses and reports that decision makers need in the format they prefer. For whom is Base SAS® software designed? Base SAS is used by SAS programming experts and power users who prefer to code to manipulate data, produce and distribute ad hoc queries and reports, and/or interpret the results of descriptive data analysis. Many IT organizations struggle with functionality, including direct access to familiar Python interface using the SAS problems arising from complex and distrib- standardized data sources and advanced pipefitter package, fostering consis- uted data, spending excessive time synchro- statistical analysis. tency of code in the organization. nizing and reformatting data for various • Simplify reporting and delivery to applications. Producing accurate and visually Key Benefits mobile devices. Base SAS provides appealing reports often requires dispropor- maximum reporting flexibility. Easily • Integrate data across environments. tionate programming resources. Addition- create reports in formats such as RTF, Available with the SAS Platform, Base ally, IT often must manage a plethora of PDF, Microsoft PowerPoint, HTML and SAS integrates into any computing software packages that only support a e-books that can be read on many environment infrastructure, unifying specific demand.
    [Show full text]
  • Clinical Trial Laboratory Data Management Using the SAS ® System
    Clinical Trial Laboratory Data Management using the SAS® System Marianne Hack, Covance, Inc., Princeton NJ ABSTRACT In terms of volume, for many clinical trials laboratory process lab data, these standard agreements are data comprises a very large portion of all data collected. adhered to whenever possible on any studies that Add to that the fact that laboratory data is inherently utilize these laboratories. complex, and the management of the data acquisition (electronic and/or data entry), cleaning (synchronization Given that we know what to expect when receiving data of databases), and reporting (unit conversions, files from a particular laboratory data vendor, we can character to numeric conversions, result flagging, etc.) then build standard SAS modules to import and processes can be overwhelming. This paper discusses reformat the vendor data. The end result is that a SAS an approach to building and using an arsenal of data set (see example B) containing data from Vendor specialized SAS program modules in order to A will be very similar to a SAS data set containing data standardize the various processes involved in from Vendor B. These SAS data sets are then ready managing and reporting clinical laboratory data. for the data cleaning stage. DATA CLEANING INTRODUCTION Data review and cleaning of vendor data requires As a CRO (Contract Research Organization), we are additional steps over and above what one might required to be able to work with data from a multitude of ordinarily do to review data-entered laboratory data. sources. Clinical laboratory data is usually integrated Commonly called ‘header cleaning’, this involves into the clinical database via direct data entry or reconciling the patient identifiers from the vendor through importation of electronic data files.
    [Show full text]
  • Introduction to SAS
    1 Introduction to SAS How to Get SAS® The University has a license agreement with SAS® for individual use for $35 a year. You can buy and download the software package at: https://software.ucdavis.edu/index.cfm. If you can have the software installed on your laptop and bring it with you to the seminar, we can work through the examples and exercises together. SAS will run in Windows® Vista, 7, 8, and 10, and on UNIX. To run SAS on a Mac® you can run SAS University, which is a limited version that is free and runs virtually on OS X http://www.sas.com/en_us/software/university-edition/download-software.html#os-x or you will need to log in remotely to a Windows or UNIX system or use a desktop virtualization program such as Parallels®. Your department can purchase a license for Parallels® using the link above for a discount. You can get a free 14 day trial of Parallels at: http://trial.parallels.com/?lang=en&terr=us. What is SAS®? SAS is an integrated software system that enables you to perform a variety of tasks. For the purposes of this seminar series, we will be focusing on: 1) Data entry, retrieval, and management 2) Tables and graphics 3) Statistical analysis For this session held on Sept 14 and 21 we will mainly discuss (1) and (2) as a start. The future seminars will discuss (3) in detail in the context of statistical analysis methods covered. SAS stands for Statistical Analysis Software, but now we just use SAS as the registered trademark for the software package.
    [Show full text]
  • IBM SPSS Modeler Premium
    IBM Software IBM SPSS Modeler Premium IBM SPSS Modeler Premium Improve model accuracy with unstructured data IBM® SPSS® Modeler Premium helps you achieve superior outcomes Highlights by basing your business decisions on predictive intelligence. Predictive intelligence creates more effective strategies because it allows Solve business problems faster with organizations not only to evaluate trends and benchmarking results, analytical techniques that deliver deeper insight. plans and performance, but also to look into the future by evaluating likely outcomes and understanding how the interplay of factors affects • Easily access, prepare and integrate structured data and text, web and survey those outcomes. data. With SPSS Modeler Premium, you can solve any business problem • Quickly identify and extract sentiments from text in more than 30 languages. faster using powerful, proven analytical techniques that deliver deeper insight into your customers or constituents. However, because the • Extend access to text stored in legacy majority of data is trapped in unstructured or textual form – in systems, such as mainframes, through support for IBM Classic Federation Server comments, files or on the web – modeling with structured data alone and IBM® zDB2® data stores. may provide an incomplete view into your business processes and outcomes. • Easily perform in-database ­ analytics and mining for IBM® Netezza®*. SPSS Modeler Premium enables organizations to tap into the • Broaden visibility into the full customer predictive intelligence held in all forms of data. It goes beyond the view with the ability to export to IBM® Cognos® Business Intelligence directly analysis of structured numerical data and includes information from from the interface. unstructured data, such as web activity, blog content, customer feedback, emails, articles and more to discover relationships between • Add more deployment options through zLinux, SuSE Linux Enterprise Server and concepts and sentiments and create the most accurate predictive models inclusion in IBM Smart Analytics System possible.
    [Show full text]
  • Julia: a Modern Language for Modern ML
    Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement.
    [Show full text]
  • SWAT's It All About? SAS Viya for Python Users
    Paper 3610-2019 SWAT’s it all about? SAS Viya® for Python Users Carrie Foreman, Amadeus Software Limited ABSTRACT Python® is described within the community as the “second-best language for everything” (CALLAHAN, 2018). The strength of Python as a language stems from its huge range of uses including as a tool for data science. Python is taught on many university courses and as a result there are a vast number of coders with Python skills in the data science industry. Last year at Amadeus we explored the use of SASpy as an initial tool to bridge the gap between Python and SAS 9.4 (FOREMAN, 2018), this year we move on to look at integration with SAS Viya®. In July 2016, SAS® released Python SWAT (Scripting Wrapper for Analytics Transfer), a Python library. It allows connections to CAS (Cloud Analytic Services) and therefore opens up SAS Viya’s functionality to Python users. SWAT allows users who do not have a SAS background to perform their data manipulation and analytics using more familiar Pythonic syntax whilst still harnessing the power of CAS and SAS Viya. In this paper we will demonstrate the use of Python SWAT to first connect to a CAS session, load data from Python into CAS and use CAS action sets to analyze it. We will then make the data available to other applications such as SAS Visual Analytics to demonstrate some of the functionality this gives. INTRODUCTION Python is an integral part of the data science culture and is used within many organizations worldwide. Many universities teach Python as part of technology courses creating a steady stream of Python enabled programmers who then take those skills into industry.
    [Show full text]
  • Introduction This Document Is an Individual Chapter from SAS/STAT® 13.1 User’S Guide
    SAS/STAT® 13.1 User’s Guide Introduction This document is an individual chapter from SAS/STAT® 13.1 User’s Guide. The correct bibliographic citation for the complete manual is as follows: SAS Institute Inc. 2013. SAS/STAT® 13.1 User’s Guide. Cary, NC: SAS Institute Inc. Copyright © 2013, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a) and DFAR 227.7202-4 and, to the extent required under U.S.
    [Show full text]
  • Insight MFR By
    Manufacturers, Publishers and Suppliers by Product Category 11/6/2017 10/100 Hubs & Switches ASCEND COMMUNICATIONS CIS SECURE COMPUTING INC DIGIUM GEAR HEAD 1 TRIPPLITE ASUS Cisco Press D‐LINK SYSTEMS GEFEN 1VISION SOFTWARE ATEN TECHNOLOGY CISCO SYSTEMS DUALCOMM TECHNOLOGY, INC. GEIST 3COM ATLAS SOUND CLEAR CUBE DYCONN GEOVISION INC. 4XEM CORP. ATLONA CLEARSOUNDS DYNEX PRODUCTS GIGAFAST 8E6 TECHNOLOGIES ATTO TECHNOLOGY CNET TECHNOLOGY EATON GIGAMON SYSTEMS LLC AAXEON TECHNOLOGIES LLC. AUDIOCODES, INC. CODE GREEN NETWORKS E‐CORPORATEGIFTS.COM, INC. GLOBAL MARKETING ACCELL AUDIOVOX CODI INC EDGECORE GOLDENRAM ACCELLION AVAYA COMMAND COMMUNICATIONS EDITSHARE LLC GREAT BAY SOFTWARE INC. ACER AMERICA AVENVIEW CORP COMMUNICATION DEVICES INC. EMC GRIFFIN TECHNOLOGY ACTI CORPORATION AVOCENT COMNET ENDACE USA H3C Technology ADAPTEC AVOCENT‐EMERSON COMPELLENT ENGENIUS HALL RESEARCH ADC KENTROX AVTECH CORPORATION COMPREHENSIVE CABLE ENTERASYS NETWORKS HAVIS SHIELD ADC TELECOMMUNICATIONS AXIOM MEMORY COMPU‐CALL, INC EPIPHAN SYSTEMS HAWKING TECHNOLOGY ADDERTECHNOLOGY AXIS COMMUNICATIONS COMPUTER LAB EQUINOX SYSTEMS HERITAGE TRAVELWARE ADD‐ON COMPUTER PERIPHERALS AZIO CORPORATION COMPUTERLINKS ETHERNET DIRECT HEWLETT PACKARD ENTERPRISE ADDON STORE B & B ELECTRONICS COMTROL ETHERWAN HIKVISION DIGITAL TECHNOLOGY CO. LT ADESSO BELDEN CONNECTGEAR EVANS CONSOLES HITACHI ADTRAN BELKIN COMPONENTS CONNECTPRO EVGA.COM HITACHI DATA SYSTEMS ADVANTECH AUTOMATION CORP. BIDUL & CO CONSTANT TECHNOLOGIES INC Exablaze HOO TOO INC AEROHIVE NETWORKS BLACK BOX COOL GEAR EXACQ TECHNOLOGIES INC HP AJA VIDEO SYSTEMS BLACKMAGIC DESIGN USA CP TECHNOLOGIES EXFO INC HP INC ALCATEL BLADE NETWORK TECHNOLOGIES CPS EXTREME NETWORKS HUAWEI ALCATEL LUCENT BLONDER TONGUE LABORATORIES CREATIVE LABS EXTRON HUAWEI SYMANTEC TECHNOLOGIES ALLIED TELESIS BLUE COAT SYSTEMS CRESTRON ELECTRONICS F5 NETWORKS IBM ALLOY COMPUTER PRODUCTS LLC BOSCH SECURITY CTC UNION TECHNOLOGIES CO FELLOWES ICOMTECH INC ALTINEX, INC.
    [Show full text]
  • SAS/ASSIST ® 9.1 the Correct Bibliographic Citation for This Manual Is As Follows: SAS Institute Inc
    Getting Started with SAS/ASSIST ® 9.1 The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2004. Getting Started with SAS/ASSIST ® 9.1. Cary, NC: SAS Institute Inc. Getting Started with SAS/ASSIST® 9.1 Copyright © 2004, SAS Institute Inc., Cary, NC, USA ISBN 1–59047–205–5 All rights reserved. Produced in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227–19, Commercial Computer Software-Restricted Rights (June 1987). SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513. 1st printing, January 2004 SAS Publishing provides a complete selection of books and electronic products to help customers use SAS software to its fullest potential. For more information about our e-books, e-learning products, CDs, and hard-copy books, visit the SAS Publishing Web site at support.sas.com/pubs or call 1-800-727-3228. SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. IBM® and all other International Business Machines Corporation product or service names are registered trademarks or trademarks of International Business Machines Corporation in the USA and other countries.
    [Show full text]
  • They Have Very Good Docs At
    Intro to the Julia programming language Brendan O’Connor CMU, Dec 2013 They have very good docs at: http://julialang.org/ I’m borrowing some slides from: http://julialang.org/blog/2013/03/julia-tutorial-MIT/ 1 Tuesday, December 17, 13 Julia • A relatively new, open-source numeric programming language that’s both convenient and fast • Version 0.2. Still in flux, especially libraries. But the basics are very usable. • Lots of development momentum 2 Tuesday, December 17, 13 Why Julia? Dynamic languages are extremely popular for numerical work: ‣ Matlab, R, NumPy/SciPy, Mathematica, etc. ‣ very simple to learn and easy to do research in However, all have a “split language” approach: ‣ high-level dynamic language for scripting low-level operations ‣ C/C++/Fortran for implementing fast low-level operations Libraries in C — no productivity boost for library writers Forces vectorization — sometimes a scalar loop is just better slide from ?? 2012 3 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 “Gang of Forty” Matlab Maple Mathematica SciPy SciLab IDL R Octave S-PLUS SAS J APL Maxima Mathcad Axiom Sage Lush Ch LabView O-Matrix PV-WAVE Igor Pro OriginLab FreeMat Yorick GAUSS MuPad Genius SciRuby Ox Stata JLab Magma Euler Rlab Speakeasy GDL Nickle gretl ana Torch7 slide from March 2013 4 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 Numeric programming environments Core properties Dynamic Fast? and math-y? C/C++/ Fortran/Java − + Matlab + − Num/SciPy + − R + − Older table: http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/ Tuesday, December 17, 13 - Dynamic vs Fast: the usual tradeof - PL quality: more subjective.
    [Show full text]