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BUGSMWSUG 1993 Papers - Index
53 papers. Source: MWSUG (Midwest SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Data Driven Report Generator | John G. Blodgett, Bee-Yauw Soo |
STF3PRO is a SAS AF application which generates data products based on the 1990 census Summary Tape File 3. It currently supports four basic kinds of reports which it can generate for a broad range of geographic universes and levels of geographic summarization. The key to making this application work was to be able to present the user with custom selection menus which could be dynamically built in response to the user's replies to earlier questions. A SAS data set was created containing the basic information required to build these custom selection lists, based upon our current file holdings. Because this data set contained over 1500 observations we were concerned that it would be very slow if we had to dynamically query it in order to build the selection lists, but ~our experience has shown that this is not a problem. Issuing a SQL call to do SELECT UNIQUE/ WHERE queries and display a selection list based on the | sample, dataset, proc sql | 7 | |
| 2 | Advanced SQL and Database Connectivity | C. Michael Whitney | This paper will discuss SAS 6.07's SOL Pass-Through Facility and how it eliminates the need for access and view descriptors when retrieving from and updating DB2 databases. Tech niques described can be applied to other relational databases. Several of SAS SOL's other fea tures will also be covered, including SOL and macro variables, summary functions, the case ex pression, and multi-table selects | create table, macro variable, proc step, proc sql, relational database, dataset | 7 | |
| 3 | Advanced Usage Techniques of PROC FSEDIT | Andrew P. Ford | This paper presents an advanced application of FSEDIT under version 6. This paper shows how to: Link customized help screens, built with PROC BUILD, to an FSEDIT screen for both overall general help information as well as for individual entry field help. create customized commands. -'' Link a selectiolil menu built with--PRGG-- PMENU with a FSEDIT screen. Test for entry errors and display appropriate error messages. Link together two FSEDIT screens to provide a tool for previewing output before printing a hard copy. Link customized PF key settings for both FSEDIT and OUTPUT windows. II!TRODUC!riOii A focus of the Electric Engineering Department is coordinating Capital Budget Projects. The tools needed to coordinate over 1100 projects include data entry, data tracking and gathering, and printing customized reports on an as-needed and monthly basis. SAS FSEDIT, version 6, was the tool chosen because it could provide all of the capabilities needed |
proc step, sas output | 6 | |
| 4 | An Extraordinary Reporting System for the Pharmaceutical Industry - the Macro Way | Judy L. Gilbert | Automaled Clinical Data Ou1put, ACDO, is an Eli Ully and Company project 1hal has been dedicatad to developing flexible. sophisticallld SAS~ macros which are used to create output across all dsease classes. SAS macros are developed in a modular desi!JI so that multiple reports may use IT1Illl}'!Jf the same_llltXkJII!I!I. T'heJl!Lrrm focus of the reports has been demographic, adverse event, and laboratory data, but soma of the modules also analyze or summarize drug efficacy data. Although the listing programs will hanc:le any clinical trial design, including crossovers, the summaries are generally applicable only for parallel desigls. A fundamental and powerful attribllf& of this reporting system is flexibility. The SAS macro facility is fully uttiZed. All reports contain mUtiple nes1sc1 macros, SAS procedures, and functions. Customer-specified parameters number from ~ in the simplest report to over 200 in a more complex report | proc step, proc glm, clinical trial, macro definition, proc genmod, data cleaning, adverse events, anova, proc rank | 5 | |
| 5 | Analysis of Incomplete Categorical Repeated Measures | Charles S. Davis | repeated measures, proc step, missing value, sample, data science, longitudinal data, response variable, proc freq | 6 | ||
| 6 | Basic and Advanced Users of SAS Dates | Kenneth L. Goodwin | if-then-else, proc step | 5 | ||
| 7 | Breaking Those Monthly GPLOT Chains | Terry Lessmann | sysdate9, macro variable, date variable, proc step, sample, mdy, proc gplot, macro loop | 10 | ||
| 8 | Building and Using Corporate-Wide Libraries for Macros and Formats | Dennis McBride | options statement, proc format, format statement, proc step, macro definition | 9 | ||
| 9 | Building Programs with the SAS Macro Facility | Philip M. Pochon | The SAs· macro facility allows a programmer analyst to create object-like utility flUICtions that may be used to consttuct task specific modules within a main program. The macro interpreter resides between the source code and the base SAS intelpreter. Macro language statements and functions can thus be used to control the flow of data values and lines of code to the base interpreter. This turorial flrst reviews the principles of constructing SAS macro slatemenrs and functions and then discusses how macro code is processed by, and can control the SAS interpreters. Several example utility functions are then examined. A f1nal section shows how utility functions are assembled into a generalized program that will generate CONTRAST statements for statistical testing in the GLM procedure. SAS Macro Overview The macro language is a facility within the base SAS system that allows the programmer to perfonn certain !aSks outside the base SAS interpreter |
sas base, macro function, macro variable, %if-%then-%else, macro facility, proc genmod, proc step, macro loop, macro definition, proc sql, if-then-else | 6 | |
| 10 | Chart Smart: Designing Graphs and Tables for Effective Communications | LeRoy Bessler | bar chart, proc step, horizontal bar chart | 9 | ||
| 11 | Client/Server Computing: The Opportunties and Challenges | Leonard M. Greski | In a discipline where change is occurring at an ever increasing rate, it is a challenge for practitioners to keep up with the latest advances in technology. To many SAS" users, the need to access data that resides on a variety of machine platforms is a familiar one. However, those who have tried to deliver client/ server applications to coworkers and clients have discovered shortcomings in the current technology, and faced daunting challenges during implementation of the technology. This paper will present a definition of client/server, an overview of the business need for distributed data access, the opportunities for our organizations that can be gained through successful implementation of the technology, and the challenges we must overcome if we are to successfully build client/ server applications | 4 | ||
| 12 | Client/Server Made Easy: Using SAS/AF and SAS/Connect Software Together | Tammy L. Gagliano | This paper will discuss only one aspect of client/server computing: making a request to a remote platform (like MVS) and receiving data at the local platform (like OS/2 or Windows). Since early 1987, SAS Institute has offered tools which enable you to offload work from a remote system to a local machine tor further processing, thus saving valuable and often costly host resources. Downloading data trom one platform to another can be accomplished in a variety of ways with a vast set of tools that exist in the marketplace, so why use SAS-software? The SAS System otters an integrated set of tools which enable an end-user to make data requests with little or no system knowledge. Using SAS/AF software, a menu-driven interface can be built to surround the functionality of SAS/CONNECT software and guide a user through a specific task |
dictionary tables, libname statement, sas base, options statement, sample, dataset | 10 | |
| 13 | Common Industrial Applications of Mixed Models | Bob Obenchain | The two types of linear models with random coefficients that I use most frequently in pharmaceutical applications are (i) models for within and between batch variation and (ii) random slopes regression models Although very simple conceptually, these basic models illustrate some fimdamental distinctions and computational difficulties that have fueled conttoversies over fixed versus random coefficient models for at least the last 25 years. 1. HISTORICAL PERSPECTIVE. The following are a number of quotations from Frank Yates' June 1968 presidential address to the Royal Statistical Society [Yates(l968), Section 2, Irrelevance of Much Present-Day Statistical Literature]: ''Only when radically new situations arise are extensions of theory necessary." "A publishes a paper which is irrelevant, or based upon unrealistic premises. This stimulates B and C to further thoughts on the same lines. And in a few years a body of literature is built up." "In case you think I exaggerate | regression analysis, proc mixed, pharmaceutical industry, fixed effects, correlation, proc reg, proc calis, sample, random slope, likelihood function, restricted maximum likelihood | 10 | |
| 14 | Data Ansalysis Semicolon-Free: Using SAS without the Syntax | Julia Brooks | regression analysis, data science, predictor variable, estimate, response variable, scatter plot, anova | 3 | ||
| 15 | Data Entry Using SAS/AF, SCL, and Extended Tables | Allan M. Kominek | This paper discusses a system for entering the resul.ts of a survey. The information for this particul.ar survey rel.ates to the background of people in the CJ.eveland SAS users Group. Through the use of SAS/AF, Screen Control. Language {SCL}, and extended tabl.es, a system was developed that displayed the ~estion and al.J. possible choices, incl.uding blank lines for comments. The data entry person could then select the answers from the list and enter any comments. The entire system re~ired one screen and three SAS data sets |
6 | ||
| 16 | Data Exchange and Sharing between Mainframe and PC Hosts | Geoff Morse | proc step, proc copy, distributed processing, proc download | 3 | ||
| 17 | Data Integrity Validation - Proactive vs. Reactive | Douglas P. DeYoung | data cleaning, data science, data integrity, raw data | 6 | ||
| 18 | Designer Graphics - Anatomy of Some SAS Macros for Statistical Graphs | Michael Friendly | For statistical graphics-where the goal is to gain insight into the data-SAS/GRAPH procedures alone often do not go far enough in providing the tools for the most effective visual displays. In these cases, other SAS facilities, including the macro language, Annotate, PROC GREPLAY, and SAS.!JMI provide tbe hasjs for coostnJctiog nJstom graphic displays. This paper describes some useful graphic displays which cannot be produced by the basic SAS/GRAPH procedures, and shows how they can be constructed by general macro programs, applicable to any dataset Some examples are drawn from programs described in SAS System for Statistical Graphics, First Edition |
proc step, proc freq, proc gplot, proc greplay, macro facility, odds ratio, macro definition, proc sgplot, sample, main effect, %if-%then-%else, macro variable | 8 | |
| 19 | Designing Bank Warning System Based on Logit Model Fitted to Multi-Period Performance Data | Sameer Kumar, Sant R. Arora |
sample, type ii error, risk classification, likelihood function, type i error, capital adequacy, predictor variable, hazard function, past due, days past due, proc freq, response variable | 10 | ||
| 20 | Developing a User Interface to Customize SAS Output | Ty Lee | SAS is well recognized as a development tool for infonnation systems. It is not uncommon that a system should have a flexible interface to not only facilitate the request for output but also remotely control the output device without changing the default setup. SAS/AF and SCL are extremely helpful in building user interface. Based on author's experience on DREAMS (Data REporting And Management System) developed using SAS* on OS/2 with HP LaserJet mas peripheral output device, this paper provides readers with the techniques to develop such a flexible output interface. Following the procedure step by step, one can actually build a framework for a fairly sophisticated output interface. Due to the hardware independence since version 6.06, the |
proc step, sas output, proc printto, macro variable | 7 | |
| 21 | Efficent Use of Data Storage with SAS | Eric Webster | proc step, dataset, work library, call symput, character variable, input statement, proc contents, proc freq, set statement, numeric variable | 9 | ||
| 22 | Efficient Programming with PROC SQL | Kim L. Kolbe Ritzow | PROC SQL is a powerful new Base SAS" PROC which combines the functionality of the DATA STEP and many of the Base SAS PROCs such as SUMMARY, MEANS, and SORT into a single PROC STEP. This paper is designed for those users with little or no prior SQL knowledge and who are interested in learning SQL as a means to more efficient and effective SAS programming. This paper will not discuss the use of SQL as an interface language to other database products, but will concentrate on familiarizing you with SOL's syntax and the efficiencies gained by using PROC SQL versus other SAS PROCs to access SAS data sets |
proc step, select, proc sql, group by, order by, proc print, sas base, create table, by group processing, proc sort, where statement, create view, dataset | 4 | |
| 23 | Enhanced Support Services from SAS Institute's Technical Support Division | Joy Reel, Sally J. Painter |
At SUGI 17, the Technical Support Division announced three new support services available to SAse software installations. These three new features, in addition to the existing Online Customer Support Facility, offer a full range of electronic support services. The electronic support services available are: * Electronic Mail Interface to Technical Support | proc print, sas log | 5 | |
| 24 | Fun with the SQL Procedure - An Advanced Tutorial | Lewis Church Jr., Paul Kent |
The SQL procedure is an implementation of ANSI Standard StrUctured Query Language (SQL) that facilitates extracting data from multiple sources with simple and concise queries. SQL is a versatile language for expressing these queries but there is often more lhtllleRe ~~ 16 pose the same qllCstion- some of d1ese ways may be less than intuitive for someone used to a procedural language. This paper assumes some basic knowledge of the SQL SELECT Statement, and the concepts of joins and set operators that are central to SQL. The paper is a tutorial on using the more advanced SQL constructs to solve problems in unique and concise ways. Where more than one way to address a problem exists, we analyse the alternatives with an eye towards selecting the more efficient solution |
proc sql, proc step, where statement, dataset, group by, in=, modify statement | 6 | |
| 25 | Generalized Least Squares Estimation in Heteroscedastic Linear Models | George W. Pasdirtz | monte carlo, outlier, sample, proc step, regression analysis, proc nlin, proc calis, normal distribution, proc reg, anova | 7 | ||
| 26 | Graphical Methods for Categorical Data | Michael Friendly | Statistical methods for categorical data. such as loglinear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for 'ontinuous response variables. However. while graphical display techniques are common adjuncts to analysis of ~~-- variance and regression. methods for plotting contingency table data are not as widely used. This paper provides a brief introduction to graphical | proc freq, proc corresp, proc step, logistic regression, data visualization, odds ratio, likelihood function, sample, regression analysis, response variable | 7 | |
| 27 | Graphical Methods for the Interpretation of Linear Regression Models | Simin Kavoossi, Timothy E. Cooley |
The SAS output provided by REG procedure supplies a great deal of information in the form of summary statistics and estimates. Not only is this information difficult to communicate to non-statisticians it also can obscure important aspects of the data itself. This paper will demonstrate the importance of visualization methods (graphical statistics) in the interpretation and evaluation of linear regression models. Special emphasis will be place on methods which can reveal the unexpected or which can expose patterns in the data which may not be readily obtained from the standard regression output |
regression analysis, proc reg, predictor variable, proc step, macro definition, outlier, response variable, correlation | 7 | |
| 28 | Graphs Which Almost Knock Your Socks Off: Using Visualization Principles to Convey the Message | Kathy Shelley | data step, bar chart | 7 | ||
| 29 | How to Make Data Found in a Print File Available to a Later Step of the Same Program | Hugh Geary | proc freq, proc step, input statement | 5 | ||
| 30 | Learning SAS by Diagrams and Examples | Joanne Peng, Shu-Yeng Wong |
This paper outlines an altvnatiw me1hod of teaching SAS to computer-inexperienced and stllltistics-phobic students in social sciences.. SpecifiCally we are intveSII!d in demonstrating how the SAS system and its programming capabilil'( can be easily understood through diagrams and examples. We believe diagrams are so effectiw visually 1hat 1hey IIS5ist students in conceptualizing the structure and logic inherent in SAS programming language. Hence, they help to reduce logical erron often made by beginning-lew! users who lade training in any formal programming language. Examples of short SAS programs, on the other hand, can be used as templates by students. These ll!mplates ll!ach students to *speak" the SAS language correctly, 1hus, minimizing the likelihood of making syntaX erron. This altvnatiw me1hod af \!!aching has been tried in an introduc:tcry SAS course at Indiana Universil'( for four years | proc step, dataset, data science, do over, error message, proc means | 7 | |
| 31 | Modualar Programming for a Screen Control Language Application | Linda M. Quinn, Allan M. Kominek, Tina M. Sluga |
This paper discusses the development of a system for loading, checking, and correcting the data from a clinical study. The data was collected using a set of five Case Report Forms {CRFl. The objective of the project was to develop a system that would allow display and editing of data on screens that resembled the pages of the CRF. Error processing was similar, if not identical, for many of ·;:he CRFs. Common modules could be employed for validation routines among screens and programs. Although the system consists of over 20 screens, the actual time of system developmem was greatly shortened through the use of common modules | index, macro variable, data cleaning | 6 | |
| 32 | MOVE=(+0, +5): Making SAS/GRAPH Work For You | Deb Cassidy | proc step, proc gchart, run, bar chart | 10 | ||
| 33 | Moving from a Mainframe to a Server | Roger Konyndyk | Because of the low cost and increasing power of workstations, many companies are turning to them for their computing needs instead of mainframes, where performance gains have been much slower. The decision whether to move from a mainframe to a server, however, must be based on much more than just the relative cost of computing on the two machines. Many time-consuming technical difficulties must be dealt with, people must learn to function in a new computer environment, and the new system must be able to share data with the mainframe. Much effort and expertise is required to set up a new system on a server. If the hurdles can be overcome, moving to server will provide users with much more control over their computing, faster response time, faster job turnaround, and less bureaucracy | batch job, data wrangling | 6 | |
| 34 | PC Data Conversion: Avoiding the Pitfalls | Daniel R. Bretheim | dataset, proc step, date variable, numeric variable, sysdate9, filename statement, missing value, format statement, character variable | 6 | ||
| 35 | Programmarium and Atlas of Statistical Cartography: How to Find/Show Your Data with PROC GMAP | LeRoy Bessler | proc step, proc gmap, response variable | 10 | ||
| 36 | Providing User Friendly Access and Subsetting Capabilities to Non-SAS Data Across Operating Platforms | Jim Anderson | macro variable, macro facility, sas base, dataset, options statement, macro definition, &variable, macro call | 10 | ||
| 37 | Random Sampling for the Non-Statistician | Diane E. Brown | sample, random number functions | 10 | ||
| 38 | Relational Processing of Tape Databases | Howard Levine | This paper covers the following topics: ~xjdanation ofRelatiooalProJ:essing _ Simple Relational Processing Why Use Tapes? Setting Up the Files Parallel Processing General Joins with More than 2 Files Limitations ofTapes | by group processing, parallel processing, data integrity, data merging, inner join, proc sql, proc step | 9 | |
| 39 | SAS Documentation: My Achy Breaky Back | Deborah Blank, Johnny Williams |
This paper discusses the ongoing commitment by SAS Institute to address users' concerns about the volume and accessibility of SAS* documentation. In this paper, we showcase current and planned activities to help users of the system find the information they need quickly and easily | sas base, macro facility | 5 | |
| 40 | SAS Presentation Tools: Indiana University's Library Management Information System | Mark Feddersen | Indiana University maintains one of the largest research and academic collections in the world, holding over 6 million volumes in 45 libraries afrQSS the statE!. H.J b~ automated the ur:d m ~ catalog and many of the back-room business functions of a large library. Library staff and administrators need access to information about the usage and management of the collection generated by the automated system. IU and other large academic libraries have standardized on SAS for simple statistical reporting, but SAS has more to offer. At IU, we are developing a "library management information system," using a variety of SAS data presentation tools in a UNIX/AIX environment to provide statistical reports, ad-hoc queries, and high-resolution graphics. Front-end interfaces are tailored for occasional as well as "hardy" users and delivered statewide. Examples of point-and-click menus, and color graphics displayed with X-windows graphic terminal emulation software, are presented and discussed | proc sql, proc step | 7 | |
| 41 | SAS Software on the IBM RISC System/6000 Workstation - The Wave of the Future for Information Delivery | Darius S. Baer | After years of using the SAS System for a wide variety of applications on both MVS and VM mainframe platforms, many SAS users may feel somewhat intimidated by moving to the IBM RISC Systemi6QOO (RS/6000) workstation environment. Actually, once the users become somewhat familiar with the AIX operating system (IBM's UNIX offering), they will feel rather comfortable. We discovered that running the SAS System on the workstation was not only similar in many ways to running on a mainframe, but it was ~ctually easier. The availability of the graphical Interface and locally attached terminal made the SAS experience both more interesting and enjoyable as well as faster in most cases. The AIX environment offers unique opportunities and challenges for an organization heavily dependent upon the mainframe environment. Historically, the Burlington Northern Railroad has developed SAS applications solely on the mainframe | data wrangling, dataset, batch job, batch processing | 6 | |
| 42 | Statistical Graphs in Environmental Regulatory Studies | Larry R. Holden | Agricultural companies are required to conduct large numbers of environmental studies to evaluate the potential impact of their products on the environment. The | sample, sample size, proc calis, data visualization, histogram, treatment effect | 8 | |
| 43 | Supporting SAS in an Academic Environment | Nancy Hill | sas consultant, sas library | 5 | ||
| 44 | Techniques for Distributed Application Development | Cheryl A. Garner | proc step, traffic data, numeric variable, macro loop, character variable, macro definition, macro variable, where statement, libname statement, proc download, %if-%then-%else | 10 | ||
| 45 | Techniques to Improve Efficiency in SAS Programs | David Sommer | decimal, numeric variable | 5 | ||
| 46 | The SET Statement and Beyond: Uses and Abuses of the SET Statement | S. David Riba | The SET statement is one of the most frequently used statements in the $AS- System. It is also probably one of the more misused SAS statements because the SET statement typically wm~ar- without much prograrnrner input. Proper use of the SET statement is one of the key techniques to improving the efficiency of SAS programs |
set statement, by group processing, where statement, dataset, _eof_, numeric variable, point= option, proc step | 7 | |
| 47 | The View Review: PROC SQL Views, SAS/ACCESS Views, and DATA Step Views | Andrew A. Norton | proc sql, where statement, sql pass-through, proc step, create view, dataset, missing value, flat file | 6 | ||
| 48 | Tuning Tips for Applications That Use SAS/SHARE Software | Bill Brideson, Steve Beatrous, Daniel J. Squillace, Jan Squillace |
The SAS System has many tuning options, most of which are left at their default values. When an application accesses data through a SAS server, sometimes the default values provide adequate performance and sometimes they do not. The SAS System Is delivered to you property tuned for a ''typical" application that uses SASISHARE software. SAS Institute makes some assumptions about the klnd of processing that Is going to take place In a ''typical" application. Recognizing that your application may not be ''typicaf', the SAS System supplies tuning options that you can use to override default behavior. This paper discusses programming techniques and option value adjustments that you can use to Improve the performance of your applications that access data through SAS servers. The lnlormetlon in this paper IS the result of tuning several large applications that are In use at SAS Institute | dataset, where statement, proc step, catalog entry, cpu time, sas library, sysdate9, proc sql, catalog, lookup, proc freq | 10 | |
| 49 | Using SAS to Create Statistical CANDA (SCANDA) Datasets | Rocco L. Brunelle, Debbie Romjue-Bailey, Bill Huster, Michelle McNabb, Sharon Symanowski, Ted Shaw, Linda Bergin, Kathy Koskowicz |
The Food and Drug AdminiS1ration (FDA) has stated that all New Drug Applications (NDA's) will have a computerized review by 1995. Consequerrtly, all pharmaceutical companies are preparing computer assisted NDA's (CANDA's). This computerized submission aids FDA reviewers and often shortens the time of the review. Similarly, the Statistical Evaluation and Research Branch of the FDA often requests a Statistical CANDA (SCANDA) which includes the data and the computer code used to perform the statistical analysis. SCANDA's are typically an afterthought, constructed after the NDA is complete and validated with the results in the NDA. Because of this, the SCANDA's are often inadequate. The FDA's CANDA Guidance Manual (1992) stresses the need for a priori attention to the structure of clinical databases and the accessibi6ty of suitable data subsets. This paper reviews our experience in defining and creating a SCANDA database as SAs datasets | clinical trial, clinical data, dataset, crf, sas library, proc step, adverse events | 5 | |
| 50 | Using SAS/AF and SCL Extended Tables to Build a Custom-Designed Data Editor | Darius S. Baer | Although widely acknowledged as an industry standard for managing data and information, the SAS System has come into its own as a sophisticated, easy-to-use application geRerator. Custom-designed data editors are in great demand. With the SAS Application Facility (AF) and Screen Control Language (SCL). the developer can quickly prototype enduser requirements. As the enduser previews the application and recommends changes and enhancements, the developer can rapidly add and implement the additional code. The SAS System provides an environment in which the application requirements do not have to be totally formalized before development is begun. In fact, all that is needed is a general understanding of the business needs and the environment in which the application will be used. General editors differ greatly from Application Specific Editors (ASEs). In both, the user should be able to move freely around the screen scrolling up and down and left and right, entering data as is necessary | dataset, proc step | 10 | |
| 51 | Using SAS/AF Software and SAS Screen Control Language | Nancy Carley | dataset, catalog | 4 | ||
| 52 | Why We Replaced DB2 Software with SAS Software as a Relational DBMS for a 30-Gigabyte User Information System | Stephen C. Scott | Every company has a need for a user information system where data can be presented for analysis. This system was originally designed as a normaliZed relational DB2 system to contain data from various databases. The users for this system are 100 analysts from departments throughout the corporation. However, in tests comparing access and CPU time, SAS software so outperfonned the DB2 tables that the company elected to change from a relational database management system (DBMS) using DB2 software to one that uses SAS software. When tested against DB2 In accessing millions of rows of data, SAS software provided an 80 percent reduction In CPU time. This presentation describes the test plan and results of the comparison between DB2. software and SAS software as a relational DBMS. In addition, this presentation shows how we designed a user model DBMS with SAS software that contains one percent of production and, thus, allows quick testing of complex SAS programs | cpu time, dataset, proc step, relational database, sample, proc sql | 10 | |
| 53 | Working Smart: SAS Graphics vs. Harvard Graphics | Gregory Kovich | This paper details the use of version 6. 07 Base SAS Language, SAS Macro Language, and SAS!Graph using annotation, template, and replay in an IBM 3090 MVS batch environment. The Problem Each month Harvard Graphics, a user-friendly PC based graphics package, was employed to create and print 72 graphs (24 pages}. This packet of graphs, which represents the perfonnance of the External Processing & Distribution Department, was distributed to the officers of the Inland Steel Flat Products Company. Harvard Graphics was the software of choice due to the high quality output it provides, the ability to mix bars and symbols on the same chart, the relative ease of annotating text and values, the ability to display multiple graphs per page, and the small skill set needed to execute this task | macro variable, sas log, proc greplay, proc step, macro facility, macro definition, %if-%then-%else | 10 |
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