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BUGSSCSUG 1992 Papers - Index
34 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Scoring System for Sales Tax Audit Selection | Thomas J. Winn Jr. | regression analysis, proc step, dataset, proc reg, multiple regression | 10 | ||
| 2 | A Talk on SQL | Paul Kent | This paper provides an overview of Suuctured Query Language (SQL). and its implementation in Version 6 of the SAS System. The History of SQl The' 'Relational Data Model", proposed by Codd (CODD70) represents data in tables. The SAS data set concept blends very nicely with the concept of a table in the relational data model. Both have columns(variables) and rows(observations). SAS data sets are a little more liberal than true relational model tables - they allow duplicate rows, and have an inherent ordering. Nevertheless, there are strong enough parallels between the two to make SQL a useful language for accessing SAS data sets. The terms database table and SAS data set are interchangeable in the context of this paper. SQL is a language for accessing and manipulating data stored in tables. SQL is an acronym for "Structured Query Language" and is pronounced "ess-que-ell" or "sequel". There are many conunercial products that support SQL | proc sql, proc step, dataset, where statement, order by, select, proc means, create view, sample | 12 | |
| 3 | Adaptive Algorithmic Surface Response Estimation | Thomas W. Sager, M. W. Hemphill |
By response surface estimation is commonly meant the fitting of a quadratic function of two variables to a third and the graphical display of the fit by a perspective plot. PROC RSREG of SAS implements the calculation of such a fit, and PROC G3D can display the re sults. This paper considers a more general and more flexible procedure. Fitting quadratic re sponse surfaces can distort the data, suppressing important real structures and creating non-existent ones, because of the limited types of surfaces that the quadratic form permits. In this paper we introduce a nonparametric response surface method that adapts to the data, rather than forcing the data to adapt to the limited quadratic model. The algorithm is imple mented in SAS code that we have written |
proc step, proc princomp, dataset, regression analysis, macro variable, macro definition | 15 | |
| 4 | Adding Convenience to the SAS System: One Site's Implementation System | Valerie D. Hilton | When Tandy Information Services became a SAS site, the SAS System was new to most of the 200 plus members of the programming staff and the end user community had very little mainframe experience. The system needed to be implemented in such a way that it was convenient to use and minimized the learning curve associated with MVSrrsO. The system also needed to be standardized to lessen the burden on the support staff | production run, proc step, sas library, macro definition, sample | 12 | |
| 5 | Analyzing Mainframe Resources Using Starting and Ending Time Stamps | Richard Banks | One approach to analyzing resource usage is to plot the number of concurrent tasks, such as the number of active TSO sessions, over some time period. This paper shows how to use starting and ending time stamps to produce such plots, showing the number of concurrent processes versus the time-of-day. This particular study used data collected both from the MXGl and MICS2 computer performance database products. These data, consisting of the starting and ending time stamps for several types of IBM mainframe tasks, were "counted" in SAS DATA steps using SAS arrays. Then, the summarized data were analyzed using PROC GPLOT. SAS macros were used to generalize the SAS programs used for the data reduction and the analysis. Computer Peifonnance Evaluation The computer performance analyst often is concerned with the amount of computer activity over some period of time |
proc step, proc gplot, batch job, macro variable, proc freq, dataset, scatter plot, macro definition | 17 | |
| 6 | Analyzing Multiple Response Questions: Facilitating an Existing Method and Presenting an Alternative | Paul E. Pope, Darrell Fannin |
This paper addresses the difficulties associated with analyzing multiple response data with SASe software. After an examination of the existing | proc freq, proc step, if-then-else | 6 | |
| 7 | Bodysize Measurement Comparison Using SAS 6.06 | O. M. Little, Daniel J. Rahilly |
pharmaceutical industry, proc step, correlation, phase i | 5 | ||
| 8 | Building Data-Driven Object-Oriented Applications: Selected Techniques | SAS Institute (a company), Michael Gilman, Donald J. Henderson, Martha F. Johnson, Merry G. Rabb, David S. Septoff, Norman Shusterman, Gregory A. Smith, Robina G. Thornton, Philip H. Van Dusen, C. Jessica Yuan |
This tutorial describes a system's architecture in which an applica tion is built using reusable modules or classes of objects. The appli cation can be built, controlled, maintained, and enhanced through changes to the underlying data and parameter files. An application designed in this way allows for more flexibility and lower mainte nance than one where the code is written to perform specific func tions. Generalized modules or ctasses of objects, the actions of which are dictated by parameters, lend themselves to the inevitable changes that are made to the system. Many modifications can be accompfished by changing values in parameter files instead of changing the source code. Features of this approach include ° adding new reports ° changing or reordering menu items ° controlfing user access and security | sample, proc step, proc format, proc sql | 7 | |
| 9 | Calculating the Odds Ratio and Goodness of Fit Statistics for Logistic Regression Models Using PROC LOGISTIC | Chuan-Chuan C. Wun | logistic distribution, logistic regression, regression analysis, odds ratio, likelihood function, proc logistic, proc step, response variable, predictor variable, confidence interval, proc reg, goodness of fit, maximum likelihood estimation, degrees of freedom, proc freq, proc means | 19 | ||
| 10 | Converting SAS Data Sets with Label Information to ASCII Files | Glenn Millard | Occasionally the need arises to convert a SAS data set to an ASCII file. In my case I needed to convert SAS files for use with SPSS. It might even be a last resort for moving SAS files between platforms if nothing else works. This particular procedure is generic in nature and is designed to extract all variables with their variable and value formats from the catalog files. In most cases, you need not know anything about the variables in the files. This procedure will not be what everyone might need, but it can be easily adjusted to handle most any data set. 202 The idea of trying to come up with a SAS data set to ASCII file conversion program that is generic enough to work with most data sets proved more difficult than it might seem on the surface. This is because of questions about precision and the way SAS stores numeric data. Normally SAS will store a variable as an 8 byte real number | numeric variable, dataset, catalog, label statement, proc step, character variable, proc contents, flat file | 10 | |
| 11 | Database Wars: Part I -- Memoirs of a SAS Technical Support Analyst | Kathe Richards | proc sql, data merging | 6 | ||
| 12 | Developing a SAS Based Decision Support System for Database Cleanup | Yi-Sheng Hwang | macro variable, proc freq | 15 | ||
| 13 | Discovering the FUN in Functions | Neil H. Howard | put statement, proc step, input statement, missing value, lookup, character functions, numeric functions | 5 | ||
| 14 | Effectively Utilizing the POWER of the SAS Display Manager | Rich Priem | The SAS System can be invoked in a variety of different modes. One of the most powerful and efficient tools available is the Interactive Display Manager. With the appropriate techniques, applications can be developed, debugged, and maintained with great ease using the Dialog Manager. This paper will present a quick overview of the SAS Display Manager, followed by an outline of effective procedures and "tricks" to maximize your productivity. Attention to these guidelines should lead to fast and efficient development of neat, clean, and orderly programs. The programs will follow a logical top-down flow and contaiIl. reasonable documentation - two of the major factors in producing low-maintenance code. Finally, similar techniques can be adapted for debugging existing software. These procedures have been proven in their success | proc step, proc print | 11 | |
| 15 | Graphical Analysis of Clinical Canceer Data Using SAS Macros and SAS/GRAPH | Karen L. Knorr, David G. Mascorro, Greg S. Shaw, Susan G. Hilsenbeck, John J. Hough, Gary M. Clark |
phase i, macro definition, raw data, quality control, relational database, outlier | 10 | ||
| 16 | How to Implement a Graphical User Interface Using SAS 6.06 Under MVS/TSO, and the Reasons for Doing So (... Without Confusing Your Present Users) | James D. Hefner | sample, dataset | 30 | ||
| 17 | Macro Techniques for Generating Customized Reports | Jimmy A. DeFoor | macro variable, &variable, call symput, variable, proc sort, put statement, if-then-else, %if-%then-%else, proc step, quality assurance | 7 | ||
| 18 | Mining For SAS Gold? | Tom Mannigel | Mining for SAS gold? Tom Mannigel Multitex Software Finding SAS gold or an ingenious SAS software application is important both to the private and public sector. This presentation will divulge how to identify, evaluate and program potential SAS gold mine applications. Locating those ingenious SAS applicacions can be difficult because its hard to know where to look. This paper will begin by helping you locate those golden SAS opportunities by revealing where the most SAS valuable applications are. To make sure an application is worth doing and not fool's gold, several techniques of evaluate the project will be unfurled in the presentation. And finally to help you mine that gold once you found it, several programming tricks of the trade will be describe in the presentation. Biography: Tom Mannigel President of Multitex Software a software company that specializes in SAS based information systems. Tom has twenty years of program development experience and 15 years of SAS experience | cpu time, quality control | 4 | |
| 19 | Productivity Tool Created for Tax System Conversion Using BASE SAS Software 's MACRO Facility | Clarence W. Jackson | This paper describes the conditions and SAS coding used in the evaluation, conversion, and auditing of a recent project we undertook at the City of Dallas in changing to a different TAX collections system. For each computer program written, a section of code is needed to define the input file, and describe the fields of data on the file. In SAS, this is done in a DATA STEP using "INFILE" and "INPUT" statements. If this is done using COBOL record descriptions, then there needs to be a translation to the logical field positions if you would like to use SAS in its most effient INPUT method of column pOinting. This effort should not be wasted without documenting the field layout of the file for later programming. It is possible to used the document as an input tool for SAS jobs using BASE SAS software's MACRO facility, as I have done. Among the things that needed to be done to convert our data to the format was to document the files of both systems |
macro facility, sas base, input statement, sample | 10 | |
| 20 | SAS/AF - Overview | Sheri George | libname statement, catalog | 12 | ||
| 21 | SAS/AF Application in Determining Sample Size | Zhongmin Li | sample, sample size | 7 | ||
| 22 | Side-by-Side Dot Box Plots in SAS | Alan Davis, Mary M. Lane, Andy Cucchiara, Don Parker |
For visual comparison of groups of data, Side-by-side Dot Box Plots are an important tool in exploratory data analysis. This paper shows how they can be constructed in SASR , and some examples, including their advantage over standard displays such as PROC CHART and PROC UNIVARIATE |
box plot, proc step, proc univariate, class variable, proc gplot | 14 | |
| 23 | Software Improvement at SAS Institute | Kay C. Bydalek, Sue McGrath |
quality assurance | 4 | ||
| 24 | Stupid SCL Tricks: Techniques for Manipulating Screens and Views and Other Cute Stuff | Betty Smith | This paper describes a number of SCL coding techniques one can do to get an application to perform a number of tasks such as fetching (information retrieval, fishing (table lookup), swimming (navigation), creating and raising kids (creating/updating/appending SAS data sets in SCL), and other stuff like the mysterious METHOD routine and SUBMIT blocks. The introduction of the SCREEN CONTROL LANGUAGE to VERSION 6 of SAS has empowered data entry and interactive applications with almost unimaginable abilities. For example, from within an FSEDIT display you have the ability to access other data sets and external files, and display them in any form you desire. You can perform calculations and tasks on specific fields in your display. Suppose while in an FSEDIT session you would need to copy a phone field to other phone fields in the observation | where statement, macro variable, proc step | 40 | |
| 25 | Techniques for Simultaneously Processing Multiple Data Sets with Different File Layouts Using SAS Flexcode | Huixing Tang, Matthew Grover, Kerril S. Bauerly |
call symput, put statement, macro call, macro variable, data step, cpu time, dataset, macro definition | 18 | ||
| 26 | Testing the Additivity Assumption in Repeated Measures | Robert J. Zambarano | repeated measures anova, repeated measures, interaction effect, main effect, anova, degrees of freedom, proc step, fixed effects, proc means, response variable, numeric functions, proc genmod | 11 | ||
| 27 | The SAS System Supervisor - A Version 6 Update | Merry G. Rabb, Donald J. Henderson, Jeffrey A. Polzin |
This tutorial updates a talk given at previous SUGls that discussed the functions 01 the SAS· System supervisor during the compilation and execution 01 a SAS DATA STEP program. Under Version 6 of the SAS System, the SAS System supervisor provides interface ser vices at a higher level than before, and its primary activity in this context Is to determine whether a DATA or PROC STEP is being invoked. Once invoked, the DATA STEP controls its own environ ment during both compile and execution phaSes. This tutorial pres ents a logical model describing both the compile and execution time actions of the DATA STEP processor |
set statement, by group processing, dataset, merge statement, missing value, where statement, keep statement, output, update statement, sample, do loop, drop statement, sas programmer | 8 | |
| 28 | The Use of PROC ARIMA to Perform Time-Series Intervention Analysis of the Impact of the Texas 1989 Motorcycle Helmet Law on Total and Head-Related Fatalities | Neil S. Fleming | proc arima, correlation, moving average, response variable, proc step, proc calis, maximum likelihood estimation, trend analysis, likelihood function, standard deviation, regression analysis | 21 | ||
| 29 | User-friendly Testing Analysis with SAS Software Products | Mark L. Schneider | The Ouality Assurance (OA) department at SAS Institute Inc. is responsible for testing the numerous features of the SAS System on a variety of hardware platforms. Much of the testing is conducted in an automated fashion, thus producing reams of output that must be scrutinized by OA analysts to weed out unexpected, inconsistent, or incorrect behavior. In response to this challenge, the OA tooling group has developed a centralized analytical system by which bugs can be quickly identified. The goal of the system is to reduce the time spent on problem identification, in effect maximiZing time available for interactive testing and new testware development. The system was developed using SAS software products for two reasons: the ease with which user-friendly applications can be developed. and the opportunity for evaluating SAS software products in a real-world application | proc step, audit trail, proc freq, where statement, trend analysis, regression analysis | 6 | |
| 30 | Users Get It Their Way | Twila Faye Turpen | health insurance, macro variable, insurer | 7 | ||
| 31 | Using of SAS Software to Manage Chemical Plant Laboratory Results | Conrad Reynolds | A large petrochemical plant uses SAS software to manage laboratory sample results. This SAS system includes reports, graphics, long-tenn storage, and tools to combine the results with process data from non-SAS systems. The paper discusses the system from the programmer's point of view; it covers design, decisions, coding techniques, and system maintenance | sample, dataset, create table, laboratory data, proc step, autocall macro, macro facility, proc sql | 4 | |
| 32 | Using PROC PRINTTO | Thomas J. Winn Jr. | proc step, time series analysis, anova, proc printto, proc anova | 8 | ||
| 33 | Using SAS to Manage Diverse Data Types Needed to Develop a Gas Field | Mark K. MacLeod | At Chevron, we use SAS to manage large quantities of engineering, geologic and geophysical data from a Gulf Coast gas field. Easy access to these data allows us to make better, more timely decisions on where to drill new wells. Using SAS, we piece together these interrelated but diverse data types in ways not envisioned or available from in-house or third-party engineering, geologic or geophysical software. Developing a gas freld entails deciding where to drill the next well within the limits of the field. Three dimensional (3D) seismic data provides a technological breakthrough in this complex geologic area such that picking the actual drill location is less geologically risky than with two-dimensional seismic data. Given that the 3D seismic data removes most geologic risk, we need to know that the well will recover enough gas to be an economic success | format statement, proc step | 8 | |
| 34 | Using the FILEVAR=Option to Read Large Numbers of Data Files in One Data Step | Glenn Millard | The FILEV AR option on the INFILE statement is a powerful tool for reading multiple data files, especially if the exact filenames may not be known by the SAS programmer. With this option you can potentially read hundreds of files in one DATA STEP. SAS was used to process snapshots of VMS system statistics without using large amounts of system resources in the process. A set of three standard VMS commands captured the desired information and was executed once each hour. Each command created a separate file each time it was run, resulting in hundreds of files to be read in just a few days. Reading these files into one data set proved to be no problem for SAS. However, careful use of command options is necessary to take full advantage of interrelated features of SAS commands used in the procedure. 212 The goal of this project was to find an easy way to get shapshots of VMS system usage |
dataset, data step, batch job | 5 |
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