SCSUG 1996 Papers - Index

30 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Beginner's Guide to Using Random File Access With the SAS System	Glenn Millard	A Beginner's Guide to Using Random File Access with the SAS System Glenn Millard Stephen F. Austin State University One of the SAS System's strengths is its fast sequential file access. However, in many cases, this method does not give the progranuner the type of file access he needs. SAS provides various forms of random file access to enable programmers to accomplish their tasks. This type of access has been enhanced greatly in the latest releases of the SAS system. This has left the documentation on this subject scattered over multiple manuals and updates. This paper will present all forms of SAS random file access to provide a beginner with a concise summary on the subject. Various examples of how random file access can be used will be shown as a further aid to someone just beginning to use this SAS feature	dataset, modify statement, index, automatic variable, by group processing, point= option, key=, master data management, data step, index creation, sysrc, proc copy, transaction data, proc step, proc contents, proc sql	11
2	A SAS Graph is Worth a Thousand PROC TABULATEs	Rick Moore, Steve Johnson		proc step, proc gchart, standard deviation, scatter plot, proc tabulate, proc greplay, bar chart, regression analysis, class variable	15
3	Developing a User Friendly Data Entry and Access Application Using SAS/FSP	Gordon Holley, Leslie Dale	The College of Forestry at Stephen F. Austin State University, maintains a data base of climatological information gathered from a weather station that has been maintained on a daily basis since 1976. Using data collected, key pieces of information were hand calculated and then data were entered into a Honeywell CP6@ using the line editor. This resulted in a large file, full of errors, requiring an experienced programmer to extract useful data. This paper presents an FSEDIT application developed to streamline data entry and eliminate errors associated with hand calculations while creating a more useful and attractive interface. This application was developed in `SAS/FSP` using FSEDIT and Screen Control Language	dataset, numeric variable	12
4	Engineering Efficiency into SAS Applications	Kirk Paul Lafler	With the ever increasing popularity of SAS Software among programmers and users, guidelines for its efficient use become increasingly important. This paper presents a collection of techniques for gaining efficiency when using SAS Software. Areas deserving special consideration include program execution, JJO, disk space as well as an often overlooked item known as program maintenance. Sample code is presented to illustrate numerous practical techniques as to why some	proc sql, cpu time, dataset, drop statement	6
5	Fishing for Exactness	Ted Pedersen	Statistical methods for automatically identifying de pendent word pairs (i.e. dependent bigrams) in a cor pus of natural language text have traditionally been performed using asymptotic tests of significance. This paper suggests that Fisher's exact test is a more ap propriate test due to the skewed and sparse data sam ples typical of this problem. Both theoretical and experimental comparisons between Fisher's exact test and a variety of asymptotic tests (the t-test, Pearson's chi-square test, and Likelihood-ratio chi-square test) are presented. These comparisons show that Fisher's exact test is more reliable in identifying dependent word pairs. The usefulness of Fisher's exact test ex tends to other problems in statistical natural language processing as skewed and sparse data appears to be the rule in natural language. The experiment presented in this paper was performed using `PROC FREQ` of the SAS System	sample, fisher exact test, exact test, sample size, proc freq, proc step, likelihood function, null hypothesis, goodness of fit, data sampling, hypothesis testing, frequency analysis	13
6	Formats: An Advanced Tutorials	Richard D. Langston	This 1UtorIaI discusses several advanced features of format processing that can be of interest even to knowledgable users of the SAS System·. Items covered include: using formats as labels, endout and entlin data sets, gaining efficiency with the the NOTSORTED opdon, using a format as an alternative to sorting large data sets, and comparing V5 and V6 formats	proc format, proc step	4
7	Getting Started with PROC LOGISTIC	Andrew H. Karp		logistic distribution, logistic regression, predictor variable, regression analysis, proc step, proc logistic, response variable, odds ratio, proc reg, estimate, confidence interval, null hypothesis, data science, proc genmod	5
8	Getting Started With SAS/EIS Software	Destiny Corporation (a company)		dataset, character variable	9
9	Getting Started With SAS/INSIGHT	Destiny Corporation (a company)		bar chart, histogram	11
10	Introduction to FSREPORT	Destiny Corporation (a company)		dataset, proc report	11
11	Introduction to PROC SQL	Thomas J. Winn Jr.		proc sql, proc step, order by, where statement, select, full join, group by, relational database, inner join, proc sort, create table, create view, sql pass-through, right join, dataset, proc print, view	16
12	Introduction to SAS/ACCESS Software	Destiny Corporation (a company)			2
13	Porting Data Collected With Custom Internet Forms to SAS	Robert S. Workman, David Workman	Robert Workman Computer Science Department Southern Connecticut State University David Workman Brazil Center, Institute of Latin American Studies University of Texas at Austin This presentation discusses the use of the Perl programming language to transform data collected from Internet HTML forms into SAS readable format. Detailed examples of the techniques used are taken from work done for the Brazil Center at the University of Texas	proc append, error message	11
14	Redesigning a Legacy: Techniques of a Quality Partner	S. David Riba		data science, sas base	7
15	Repeated Measures Analysis with Discrete Data Using the SAS System	Gordon Johnston, Maura E. Stokes	The analysis of correlated data arising from repeated measurements when the measurements are assumed to be multivariate normal has been studied exten sively. In many practical problems, however, the normality assumption is not reasonable. When the responses are discrete and correlated, for example, different methodology must be used in the analysis of the data. Generalized Estimating Equations (GEEs) provide a practical method with reasonable statistical efficiency to analyze such data. This paper provides an overview of the use of GEEs In the analysis of correlated data using the SAS System. Emphasis is placed on discrete correlated data, since this is an area of great practical interest		6
16	Reponding to Change: Providing Data Warehouse Services as the Sources of the Data Evolve	Kathe Richards	Change is the rule in these days of evolving architectures, strategies, and tools. Consider what would happen if a significant portion of the application that supports the business processes that supply your data changes from traditional mainframe based to some flavor of client/server. Or what would happen if the equipment on the desk of your primary data users changes from a dumb terminal to a box that has significant processing power of its own. These are common scenarios these days. Continuing to provide the service levels expected by users of data warehouse services can be a challenge. What follows is a discussion of some potential pitfalls in this situation and "real life" stories about dealing with them. Who We Are and Where We Came From The State Comptroller's office has three constitutionally mandated roles: keeping the books, collecting the taxes, and telling the Legislature how much money they have to spend	data science, proc sql	7
17	Running a Fantasy Baseball League Using SAS Software	Keith Cranford	Fantasy baseballlea.gues have been around since the late 1970's and have increased greatly in popularity in last several years. Actual major league players' statistics are used to determine the results in the fantasy league. These leagues give owners a chance to own and manage their own major league baseball teams without having to put together a $150 million investment group. This paper presents a SASIAF application that helps manage and run a fantasy baseball league. This application utilizes SASIFSP software to enter data from each day's major league box scores and then playa schedule ofhead-to-head 'games' between teams in the league. SAS software is used to keep track of game results and players' statistics and to produce weekly reports for the owners in the league. Reports include those produced byPROC REPORT as well as custom reports using the `DATA STEP`	dataset, conditional do loop	11
18	Sampling Techniques Using SAS Software	Keith Cranford, Don Boudreaux	In many applications SAS users are faced with very large data sets. Sampling can be used in these situations to provide smaller, more manageable data sets. This paper presents a SASI AF Frame application for producing samples and estimates of the population mean for three common sampling techniques: simple random sampling, stratified random sampling, and cluster sampling. For each of these sampling techniques, a sample data set is created by using the appropriate technique on an existing, population data set. Also, the application can provide an estimate of the population mean of a variable of interest with its associated standard error. These standard errors differ from those provided by SAS procedures such as `PROC MEANS` in that they assume a finite population and use the appropriate correction factor. This application was developed in SAS 6.11 using several of the new objects available in this release such as the Data Table and the External File Viewer	sample, sample size, random sampling, cluster sampling, proc calis, dataset, macro definition	10
19	SAS and the Year 2000 Problem	Clarence W. Jackson	The purpose of this paper is to review the Year 2000 Problem, and some tools available within the SAS System. SAS System tools such as the YEARCUTOFF system option, various date time informats, formats, and functions wJ1l be reviewed. The significance of the Year 2000 problem on SAS programs and other computer systems vary depending on your point of view, but it will affect each of us in some fashion. The root of and reason for the problem is very simple to understand: no century digits in date fields. As anyone will teU you who was programming back in the early days, two digits was worth a lot of resources. When everylhing was expensive (memory and storage space), or 6mited (memory, storage space, Ale program sizes, paper `CARDS`), saving space was saving money. Two digits multiplied by the total records could save a lot of resources and money depending of the size of your data and process	options statement, raw data	10
20	SAS Graphics: A Tutorial	Glenn Millard	To beginners, SAS Graphics can seem confusing and difficult to use. Because of this they might abandon it and try to use a graphics program to accomplish the task. However, `SAS/GRAPH` is much easier to use than you might ftrst believe and has some advantages over graphics programs. This paper will present SAS Graphics in a way beginners can understand so they can begin to use this powerful tool. Its relationship to other SAS products will also be shown. Making a Decision You've got your data, now you want to present it in a concise way to someone else. A graphic of the data is the best way to get the message across. But what kind of graphic and what to use to prepare it. Nowdays the choices are staggering to the user. There are so many graphics and presentation software packages available today that these decisions become much harder to make	footnote statement, proc sgplot	6
21	SAS System Programming Tips and Efficiencies: A Tutorial	Andrew H. Karp		dataset, data step, if-then-else, proc step, output, raw data, where statement, sas programming	8
22	SAS/GIS and SAS/SPECTRAVIEW for Visualization-Driven Analysis: An Application Supporting Ambient Ozone Control Policy	Thomas W. Sager, M. W. Hemphill	The national standard for ground-level ozone pollution is currently topical. The current EPA standard for ozone is set in tenns of the maximum of hourly levels throughout a day. A revision of the standard to a moving average of hourly levels is under consideration. Neither standard nor revision specifically considers population exposures or the effect of weather and other mitigating/aggravating factors. `SAS/GIS` and `SAS/SPECTRAVIEW` pennit the cflSplay of relevant spatial-temporal maps of ground-level ozone concentrations that can inform the judgment of policy-makers, with the support of SAS analytical tools avanable for deeper probing. The integration of visualization tools with powerful statistical analysis. makes possible a new approach to analysis of spatiallyltemporally distnbuted variables that we have elsewhere tenned "visualization-driven analysis." We illustrate the application of this approach to modeling ambient ozone pollution		12
23	Self-Modifying SAS Programs: A DATA Step Interface	S. David Riba	There are many situations where it is useful to have a SAge program modify itself during execution. Some examples include: oonditlonally execute a macro based on the data in a SAS dataset oonditionally execute a procedure when there is no data in a SAS dataset generate in-line SAS program code based on the contents in a SAS dataset write SQl statements for the SQl Pass-Through FaCility "on-the-fly" This paper explores a little-known routine in the SAS System that allows a SAS program to write SAS rode "on-the-fly" during execution. Since CALL EXECUTE is a `DATA STEP` CALL routine, the full capabilities of the `DATA STEP` can be applied to oonditionally generate SAS program statements while the program is executing. If you have ever written program code to a temporary file and then %INCLUDEd it back into your program, you need to take a closer look at CALL EXECUTE. The paper will discuss the uses of CALL EXECUTE with specific examples based on real world situations	call execute, dataset, proc step, proc sql, macro facility, proc print, call symput, macro variable, sample, if-then-else, excel, macro definition	8
24	The Basics of AF/Frame Entry and Screen Control Language	Dana Rafiee		dataset, sas base	8
25	The SAS System in a Data Warehouse Environment	T. Randall Betancourt	The purpose of this paper is to provide the reader a general overview of the strategies employed in implementing a data warehouse and the role the SAS System plays in these various steps. While this is not an in-depth methodology, it is an attempt to outline the various steps one would normally go through to implement a data warehouse. In order to make clear all of the terms and acronyms used in this paper, they will be underscored and defined in the glossary at the end of this paper	dimension table, proc sql, data warehouse, fact table, relational database, star schema, data standard	12
26	The Tank Battle of the 21st Century	Judith Ann Rosten		data step, proc step	14
27	Using the PIPE and FILEVAR= Options	Richard D. Langston		infile statement, conditional do loop, data step	3
28	Using the SAS System to Produce HTML-Based Text	Richard D. Langston	The author has previously produced a poster session regarding the presentation of genealogical data. The next step in the process of producing a useful document of research is to use emerging HTML technology in conjunction with the World Wide Web. The author has used the SAS· System to produce a set of HTML files consisting of text and image data. The resultant files can be examined by common Web browsers and can also be converted into a serial form suitable for publication	txt, sample	4
29	Who Says Study Effect Doesn't Matter?	O. M. Little		anova, treatment effect	11
30	Why Knowing About Our Customers Helps Our Business	Mark Brown		data mining, data warehouse, data science, insurance claims	4

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