SCSUG 1997 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Generalization of the Stuart-Maxwell Test	Stephen I. Black, Mithat Gonen		covariance, proc step, proc freq, degrees of freedom, macro definition, proc iml, nominal variable, macro conditional	14
2	A Macro to Account for Missing Data in a Table	Margie Bell, Stephen Black, Jennifer K. Fulton	Margie Bell, Stephen Black, and Jennifer Fulton Applied Logic Associates, Inc. Houston, TX The purpose of this macro is to provide supplementary infonnation to a table, which can explain why the table does not contain all of the data that the user expects to observe. The macro takes a user-specified data set that contains the variable to be tabulated and combines it with another user-specified data set that contains a record for EVERY item, which could potentially have data in the table. It then creates a set of counts to be printed beneath the table, which account for discrepancies between the total counts in the table and the counts that would appear if there were no missing data issues. The categories of counts include "No Record in Table Data", "Missing Value in Table Data", "Number in Table", "True Total", and a listing identifying those individual items that have missing data related to the table	missing value, standard deviation, proc sort, proc step, vital signs, dataset, %if-%then-%else, macro loop	10
3	A Macro to Generate the Sensitivity, Specificity, Positiv Predictive Value, Negative Predictive Value and Their 95% CI's	Qingling Zhang		confidence interval, type ii error	8
4	A Not-So-Rapidly Developed FSEDIT Application --or-- What a Non SAS Programmer Can Do With SAS	Charlotte R. Wenger		proc step, proc sql	7
5	A Tip for Improving DB2 Performance	Ann Stephan		create table, proc sql, proc step	5
6	An Application of Cycle Regression Analysis Algorithm to Predict Electric Utility Demand	Mayur R. Mehta, Walter E. Johnston		regression analysis, proc reg, cycle, series plot, time series analysis, sample	7
7	Applications of Intelligent Technologies for Efficient Businesses	D. Thomas Taylor, Ron Salazar		neural network, data mining, regression analysis	6
8	Batch Export of ROSCOE Members to PDS Files Using SAS to Build RDF	Clarence W. Jackson		dataset, flat file	6
9	Contruction of Inptu for Environmental Simulation Models Using Multivariate Analysis	Joaquin Sanabria, Don Goss		proc step, factor analysis, proc cluster, cluster analysis, proc tree, proc score, proc factor, sample, proc standard, macro definition	18
10	Data Warehouse Implementation with the SAS System	Tony Brown	The successful implementation of a data ware house can be an extremely complex process, requiring significant planning, patience, and persistence. The Professional Services Division of SAS Institute Inc. has found that a good understanding of data warehousing concepts, coupled with a solid development methodology, and a technical infrastructure built around the SAS System can enable practitioners to achieve their goals of effective data warehousing. This paper addresses many of the challenges and issues that The Professional Services Division has confronted during the development of data warehouses. It explores the definition and	data warehouse, star schema, fact table, dimension table, data cleaning	11
11	Data-driven HTML	Keith Cranford, Mary Keinarth		proc step, macro definition	6
12	Having a Ball with Strings	Ronald P. Cody		substr function, character variable, proc print, proc step, compress function, input statement, numeric variable, translate, put statement, character functions, string functions, upcase function	8
13	Hodges-Lehmann Confidence Intervals for a Shift in Location Using SAS Procedure SQL	Ann Olmstead	An inefficient but elegant way to compute Hodges-Lehmann nonparametric confidence intervals for a shift in location using SAS `PROCEDURE SQL` is described and illustrated. SAS `PROCEDURE TIMEPLOT` is used to display the results, and NP ARI WAY is used to obtain exact confidence levels. Similar code can be used to compute other nonparametric point and interval estimates, such as the corresponding Hodges Lehmann CI for the median of a symmetric population	confidence level, proc step, sample, proc sql, confidence interval, create table, order by	6
14	Implementing a Dimensional Data Warehouse with the SAS System	Gregory S. Barnes Nelson	This paper will discuss the implementation of a dimensional data warehouse. A dimensional warehouse is a design or modeling teChnique that was developed by Ralph Kimball (Kimball. 1996) to help us develop our data models in a structured. visual way. Here we will discuss a strategy for the design, development and imlllementation of this structure using tools available with the SASe System. In addition, we will talk about several modeling techniques, implementation of logical and physical schemas, transformation and aggregation strategies, and the loading and unloading of data to the warehouse. Scope of this Paper Data warehousing can mean a lot of things to a lot of people. This paper will provide a broad level perspective on the implementation of a simple data warehouse, or perhaps more accurately, a data mart. Occasionally, we will dip down to a technical level on specific issues and then resume our broad perspective		10
15	Intermediate PROC SQL	Thomas J. Winn Jr.	This tutorial presentation will provide a practical explanation of some advanced features of the SAS Sal Procedure, including the use of: summary functions, subqueries, complex joins, in-line views, indexes, macro variables, and the Sal Pass-Through Facility. Also included will be a	order by, proc step, create view, proc sql, create table, group by, macro variable, view, dataset	7
16	Internet Breast Cancer Education: Application of JMP Software in the Analysis	Jeanette Rasche, Shane Story, Jeremy Wise, James Zadinsky	In this information and technology age, the enormous amount of information that health care consumers must evaluate has forced the development of new approaches to patient education. For example, a new advance in technology, illustrated by the emergence of the Internet as an application development platform, has accelerated dramatic changes in our	data mining, error message	9
17	Inventory Optimization and SAS	D. Thomas Taylor		inventory optimization, integer programming	7
18	Phonetic Matching of Character Data	S. David Riba	There are many occasions when character values must be compared on non-precise. or ''fuzzy'', criteria. For example, how do you find all the values In your data for a city such as Raleigh, NC. when the values could be spelled (or mis-spelled) as: Raleigh, Raliegh, Ralehg, or even Roleg Traditionally, SAS programming looks at the TEXT of the character values and attempts to define all of the possible text variatiOns. There Is another way. Most people, when they do not know how to spell something, will phoneticlze it and spell it based on how it sounds. You can compare text the same way, based on how the text value SOUNDS - phonetically. There are several tools in Base SAge software to compare character data based on phonetiC values. This paper will review these SAS tools, and present several techniques for comparing and summarizing data based on phonetic, instead of text, values. These techniques can be useful for all levels of SAS programming expertise	where statement, proc step, proc sql	4
19	Reliability Data Analysis in the SAS System	Gordon Johnston		confidence interval, regression analysis, right censoring, proc calis, interval censoring, likelihood function, predictor variable, maximum likelihood estimation, weibull distribution, data science, estimate, proc step, sample, censoring, binomial distribution, logistic distribution	10
20	SAS Client-Server Technology Used for Parallel Processing of Statistical Process Control Charts	Randy T. Rist	Eight hundred (800) Statistical Process Control (SPC) X-Charts are being produced on a Compaq Proliant 586 server by a Client-Server parallel processing application written in SAS v6.12. The Compaq server has four 100 Mhz CPUs and a Compaq Raid Array. Eight (8) clients are being executed in parallel under an NT 3.51 operating system. Clients and server communicate using named pipes. The amount of work distributed by the server to each client is determined by an exponential smoothing algorithm. The algorithm equalizes the elapse time of each client's work. Thus, starting at the same time, all clients end at the same time. With all eight clients operating and negligible contention for resources by other applications, the throughput is 80 graphs per minute. The total number of SPC charts produced at anyone time is just under 800. The total time to completion is about 12 to 13 minutes	elapsed time, parallel processing, dataset, data step, infile statement, proc sgplot	22
21	SAS Programming Tips for Finding Variables in the DATA Step: A Tutorial	Mark MacMullen	This tutorial is based on my own successful experiences in finding variables defined in the `DATA STEP` in an MVS environment, running in batch on a mainframe. Many users who work with external files have pre-defined columns from which to retrieve the variables needed. Most of the time I run batch interface commands from the vendor software to create the reports supplied from the vendor. I route the output from the batch commands to a file. This fue becomes my "external" file for my SAS routine. Using the SAS language I selectively pull only the necessary recOrds that meet the specific criteria. In most cases these repons have more information than I need and the variables are located within a vast output of data within the file. I will show three methods that I have found to be the most helpful. This paper is not intended to be an exhaustive treatment of the topic but rather a spring board from which to exercise your own inventiveness in the use of the SAS language	input statement, sample	9
22	SAS-a-Sketch: Using SAS Annotate Facility for Creating Custom Graphs	Patrick M. McGown	This paper presents an example of using the SAS Annotate Facility to generate reports containing horizontal bar charts. The Annotate Facility provides the flexibility to create detailed custom graphs using the annotate macros provided in SAS Graph. Summary data sets containing the infomnation to be plotted are first created. Annotate data sets are then created containing the plotting infomnation for the bars, item text, title infomnation, group labels and other data. The program then uses `PROC GSLIDE` to create the pages of the report and send them to the output device	sample, proc step, macro definition, retain	6
23	Self-Modifying SAS Programs: A DATA Step Interface	S. David Riba	There are many situations where it is useful to have a SASe program modify itself during execution. Some examples include: conditionally execute a macro based on the data in a SAS dataset conditionally 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 code "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 conditionally 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, sample, if-then-else, call symput, macro variable, proc sort, excel, macro definition	8
24	Starting, Leading, and Growing a Successful SAS User Group Program	Dina G. Urquhart	The number of SAS user groups in the United States and Canada continues to grow each year. If you are interested in starting a group in your area, or if you are looking for some new ideas for an existing group, this presentation is for you. Get advice and recommendations from users who have been involved in successful SAS user groups. Hear new ideas on programs, membership drives, finances, leadership, and more. The discussion will include local, in-house, special-interest, and regional SAS user groups		5
25	Statistical Enhancements in Release 6.12 of the SAS System	Maura E. Stokes	Release 6.12 of the SAS System brings several key enhancements to SASISTA"f software. Generalized Estimating Equations (GEE) methodology, a popu lar technique for the modeling of correlated response data, has been incorporated into the GENMOD proce dure. Another key update is the computation of exact p-values for a number of statistical tests provided by the `FREQ` procedure. In addition, this paper provides an overview of enhancements to the MUL TTEST, MIXED, GLM, and NPAR1WAY procedures. GEE Methods in the `GENMOD` Procedure GEEs were introduced by Liang and Zeger (1986) as a method of dealing with correlated data when, except for the correlation among responses, the data can be modeled as a generalized linear model. Correlated data can arise from situations such as * longitudinal studies, in which multiple measure ments are taken on the same subject at dif ferent points in time. A clinical trial for a new treatment of psoriasis may involve repeated measurements of symptom severity	proc freq, proc genmod, clinical trial	7
26	Survival Strategies and Solutions for the New Millennium - Year 2000	Kirk Paul Lafler, Sunil Kumar Gupta, Charles E. Shipp	As the turn of the century approaches, critical challenges face organizations world-wide. Will mission-critical computer applications be equipped to handle the numerous calculations and comparisons required by businesses to successfully operate? And how can organizations smoothly transition into the next century without impacting current operations and staffing levels? If estimates are accurate, as much as 90 percent of alI business applications will be impacted in some way by the coming millennium. A successful strategy is needed to ensure cost-effective, timely, and lasting solutions	sysdate9, date variable, proc step	6
27	Ten Great Reasons to Learn SAS Software's SQL Procedure	Kirk Paul Lafler	The `SQL` Procedure has so many great features for both end-users and programmers. It's fun, easy to learn and use, and can often result in fewer and shorter lines of code than using conventional DATA and `PROC STEP`	proc sql, proc step, select, where statement, group by, order by, alter table, delete, sql pass-through, proc contents	6
28	The INPUT Statement: Where is it's @	Ronald P. Cody		input statement, infile statement, sas log, data step, proc print, column input, trailing @, dataset, proc step, missing value, numeric variable	10
29	The Use of PROC ARIMA to Test An Intervention Effect	Neil S. Fleming, Eric Gibson, David G. Fleming		proc calis, maximum likelihood estimation, moving average, regression analysis, proc arima, likelihood function, proc step	8
30	The Utter "Simplicity?" of the TABULATE Procedure - The Final Chapter?	Daniel E. Bruns		proc tabulate, proc step	6
31	The Utter Simplicity of the TABULATE Procedure	Daniel E. Bruns		proc step, proc tabulate, proc freq, label statement	7
32	The Year 2000: Preparing for the Inevitable	Richard D. Langston	The Year 2000 represents a challenge to application developers and system programmers throughout the computing industry who rely upon dates with years represented by two digits in their applications and programs. This paper discusses various approaches to preparing your SAS0 applications and programs to be Year 2000 "ready" and compliant	sysdate9, date variable, options statement, sas log	5
33	User Publishing with SAS Institue: A Valuable Exchange	Hanna Schoenrock	As the largest private software company in the world, SAS Institute Inc. is dedicated to providing quality documentation of its software to the millions of users around the globe. While we have in-house technical writers who produce manuals, CD-ROMs, and online documentation, SAS users also hold extremely valuable knowledge about using the software. SAS software users are the people who are working with the code and procedures and SAS language daily; these folks see first hand what the software is capable of producing and how successful a product can be when SAS software is involved. Through SAS Institute's User Publishing Program, SAS software users have the opportunity to publish their work using the Institute as a resource for expert technical support, production support, and marketing support. In return, the Institute is able to offer to our users a unique approach to using our software - a user's approach	sample, cluster analysis	4
34	Word-Wrapping Text Output in SAS Corner	S. David Riba	How do you get your SAS output that normally prints text values in a very long line to wrap in smaller lines at word breaks just like a word processor typically does? The problem with printing long text values in SAS is that either a) your output gets truncated when the next field writes over it at a predefined column or b) you can split the values at a certain line length which may, or may not, be at a break between words This datastep routine parses long text values into smaller output lines based on the output line length that has been defined. Instead of merely splitting the line, it defines the closest gap between words and breaks at that point	if-then-else, conditional do loop	2
35	Your Friend the Colon: Some Efficiency Techniques For Comparing Or Selecting Character Data	S. David Riba	S. David Riba, JADE Tech, Inc., Clearwater, FL	colon modifier, substr function	2

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