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BUGSSUGI 1979 Papers - Index
83 papers. Source: SAS Users Group International conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Case Management System for the New Jersey Public Employment Relations Commission | Nancy Begin, Barbara E. Kemmerer |
case management, data governance | source | 3 | |
| 2 | A Class of Invertible Functions for Analysis of Categorical Response Using Linear Models | Gloria Cappy, James E. Dunn |
sample, logistic distribution | source | 6 | |
| 3 | A Data Dictionary Driven Data Analysis System | Richard A. Enz, Doron Steger |
Because of the diversity and volume of data encountered in clinical research, we developed a system allowing standardization for input and flexibility for data management. The two main components Of our overall system are SAS and a Data Dictionary. The Data Dictionary defines the format and location of data for each study. SAS allows flexibility to manage and manipulate data. The key to the system is an interface program (ODSAS), which links the Data Dictionary and SAS. Through this program, an entire master file of a clinical study is read into a SAS data base as separate data sets containing vari ous types of data, e.g. admission records, lab oratory values, etc., allowing access to the· data through SAS. Many of our in-house programs have been inter faced with the Data Dictionary and with SAS. Several utility programs have been written for the management of SAS disk files | data dictionary, dataset, format statement, missing value | source | 5 |
| 4 | A Multivariate Approach to the Analysis of a Repeated Measures Experiment Using SAS | Rocco L. Brunelle, David W. Johnson |
Experiments with repeated measures over time are common in agricultural experiments, educational testing, and clinical trials. The usual design involves the comparison of two or more treatments with a series of measurements taken over time on each experimental unit. The object of this type of experiment is to compare the effects of the treatments over time. In clinical trials, comparisons between two treatments are often made using a parallel repeated measures design. Here, patients are randomly assigned to one of two groups and each group receives one of the two treatments for a given period of time. In its simplest form no other factors are involved in a parallel design. | sample, response variable, multivariate analysis, missing value | source | 5 |
| 5 | A Recode Procedure for SAS | Douglas D. Cockrell, Ione H. Cockrell |
missing value, proc step, character variable | source | 2 | |
| 6 | A SAS Macro for a Distribution-free Test for the Parallelism of Two Regression Lines | Sandra Novinger, Robert Rogers |
regression analysis, sample, residual, nonparametric test | source | 6 | |
| 7 | A SAS Macro for Plotting "Pair Chart" and Obtaining Two Sample Mann-Whitney, Wilcoxon, and Kolmogorov-Smirnov Statistics | Kenneth A. Hardy, Again N. Sinha |
The "pair chartl! is a useful. graphical rep resentation of ordered data for two_sample prob lems. In addition, it provides a conyenient for mat for computing common two-sample, inferential statistics such as the-Wilcoxon, Mann-Whitn~y and Kolmogorov-Smirnoy. The present paper presents a SAS macro for plotting pair charts on the pen plotter at the UNC Computation Center (or the screen of a Tektronix video terminal) and also calculating ,the above two-sample test statist-ics | sample, macro definition | source | 12 |
| 8 | A SAS Macro for Ridge Analysis of Multivariate General Linear. Models | Kenneth A. Hardy, Again N. Sinha |
Ordinary le~at Bquar~~ (OLS) fails to p~Qvide satisfactory 'estimates for General Linear ~del parameters when the prediction vectors are not orthogonal. i.e., when the data are ill-condi tioned. Th~ technique of ridge regreSSion pro vides better minimum variance estimates in such situations. This paper reviews the general theory of ridge regression analysis for the multivariate general linear model and presents a SAS macro which enables the user to ubtain both 015 and ridge estimates 'fo"r purp()ses of comparing the two techniques and visualizing the effects of non~ orothogonality. INTRODUCTlON The ordinary least squares (OLS) estimation procedure fails to provide satisfactory estimates of the parameters of the mult~ple regression ~d el when the data are ~ll-conditioned due to multicollinearity-of the regressors. MUlticol linearity Ar~ses ~henever the~e ~~sts·linear de pendency a~ng the predictor variabies which ia indicated by singularity in the matrix 6f their _correlations | correlation, predictor variable, macro definition, covariance, response variable, regression analysis | source | 5 |
| 9 | A SAS Macro for Robust Multiple Regression | Charles McElrea | An SAS macro is described .. hich performs robust multiple regression. The technique is ·implemented as a macro in order to provide the user with maxi~ mum access and flexibility of program execution. The macro is constructed in a manner 'such that the user has the option of supplying both the appropriate weighting function and the algorithm control information. Addi tionallY, -the macro is designed such that multinle datasets may be processed through a single macro call; thereby enabling the user to assess the performance of particular weighting functions. Finally, the macro is demonstrated in an example by Drapper and Smith (1966) in order to compare each function's treatment of the outliers | regression analysis, multiple regression, proc robustreg, proc reg, outlier | source | 4 |
| 10 | A SAS Macro for Selecting a Random Sample of Data Points from a SAS Database | W. S. Cash, K. Martin |
A macro lS presented which enables the user to easily select a random sample (without replacement) of size n from a SAS database con taining N data points which can be either nu meric or character. The program lists the original database with interspersed records which quickly identify the data points in the random sample~ The procedure is specifically adapted to SAS databases created through an Inquire database. An example of its use on an Inquire database is given | sample, sample size | source | 4 |
| 11 | A SAS Procedure for After Tax Investment Analysis | Steven R. Borbash, Arup K. Mallik |
proc step, interest rate | source | 3 | |
| 12 | A SAS Program to Graphically Schedule the Tasks Assigned to Each Employee | Jeffrey W. Berno, Walter J. Guthrie |
source | 5 | ||
| 13 | A SAS-AUTOGRP Interface for the Analysis of Hospital Discharge Data | Michael B. Bracken, Robert F. Elia, Daniel H. Freeman Jr., Jean L. Freeman |
sample, likelihood function, data science | source | 7 | |
| 14 | A SAS-VSPC Interface for Handling Library Functions | Sandra T. Cowen, David J. Cowen |
This paper describes the use of SAS c prpced:u:res for the creation, mai,~tenance ~:D.d ;t"ec,ording of materials -contained in a research or.ient_ed library in the· Bureau·~f Gover.amental -Research ~nd_ S~rvice' at the University of South Carolina. SAS used in conjunction with VSFC .. thrqugh a CRT terminal ,located ·,in the ~ibrary has aided in the cr~at1.on· of a h1.ghly u,ser-o-r1ented- and flexible bibl;f.ographieal, syst-em~ ,The- 'system handles a multitude Qf'lnputting~ 'editing. 8ort;ing,l. se lect-iug ~d repQrting .procedures that; are common to the. operation of any library. SAS INPUT, SORT, FORMS and. IF pro'cedurM il1, v.a.rious c~b.ina.·t1.ona provide .extensive. capabilitJes to g~ner.,ate. specialized searches, and. output products. More specifically~ FROC FORMS was used to create a card .ca-talqgue fo~.a pub.lic adm;[,nistratiOll library that wa~: based on an .alpha~etical. !=!laSSi: fication scheme developed by the International City Management Association | sas output, sample | source | 3 |
| 15 | A SEER Compatible Tumor Registry under SAS | M. Clinton Miller III, Edmund Murphy, Paul B. Underwood Jr. |
source | 3 | ||
| 16 | A Short Note on Discrete Event Simulation in SAS | Douglas L. Anderton, Audie R. Brewton |
In performing several full-scale simulation analyses, the State of Utah haa chosen tG use SAS rather than traditional simulation languages. The authQrs present a basic; structur,e. a brief diecussion of the comparative advantages of simulation using SAS, and simplified examples from a faculty tenure Bimulation~ | sample, cpu time | source | 2 |
| 17 | A Tumor Registry Information System Application | William Ingram III | proc step, dataset | source | 5 | |
| 18 | An Evaluation of Nonlinear Regression Programs | C. M. Metzler | Comparison of computer programs is a very complex task. as there are many criteria which can be used and comparison under standard con ditions may be difficult to achieve. Any eval uation of statistical computer programs must consider three aspects: (I) definition of the statistical problem, (2) possible numerical algorithms for computing the solution to the statistical problem, and (3) implementation of the chosen algorithm{s) on a computer. Clearly all of these are reflected in the per formance of a statistical program. Nonlinear regression or parameter estimation is a subset of the much larger mathematical area of function optimization. Having picked the objective function to be optimized and the numerical algo rithm{s) to compute the optimum, then the imple mentation must be considered and the performance evaluated against one or more criteria. The criteria may include statistical and mathema tical validity, ease of use, capabilities and flexibility, robustness and cost | proc step, regression analysis, proc nlin, predictor variable, estimate | source | 9 |
| 19 | Analysis of Covariance of Incomplete Data in Experiments with Repeated Measurements in Clinical Trials | Harji I. Patel | The purpose of this paper is to show how SAS can be llsed to perform stepwise procedures for analyzing-repeat~d measurement experiments. These multivariate methods can be used to deter mine treatment differences when a single response is observed at successive equally/unequally spaced fixed time-points. Using orthogonal poly nomials, the response can be modeled in terms of suitable degree polynomial scores which adequately characterize the curves. This proce dure not only compares the overall treatment means but also compares the selected degree of polynomials in a stepwise manner. We consider one-way classification models with and without covariate(s), the null hypoth esis being that the observations are a random sample from MN (~'t). When the number of vari ables (K) are less than the numb~r of observations (N)~ the· data can be analyzed in a stepwise manner by considering the original data in time. When K ~ N, then we cannot apply Wilks criterion for analyzing treatment differences | treatment effect, null hypothesis | source | 5 |
| 20 | Analyzing and Reporting Employee Attitude Survey Findings in a Large, Multidivisional Company | Janet L. Curley, Douglas Garber |
In a large company" employee surveys C30n-· earning individual ·atti tudes toward work, super vision, compensation, and other perso-nnel issues are needed to 1) evaluate· personnel polioies and programs, and 2) facilitate an uJl'o'!ll'd flow of communications through the organization. Prac 'tical application of survey findings requires corporate trend analyses for top management. This must be followed by rapid feedback to smaller components so that local aotion planning can be initiated. SAS data managemant and report-writing capabilities are discussed in connection with a recent slll"V"ey,...feedback e:.r.rQrt in a large, mu1 tidi triaional company. The reports generated by SAS helped managers inter pret component results, greatly facilitatirig the action planning process | proc step, trend analysis | source | 5 |
| 21 | Analyzing Large Data Sets Using PROC SSCP and GLMMS | Ronald W. Helms, James D. Hosking |
proc step, proc corr, proc genmod, response variable | source | 5 | |
| 22 | Application of Stepwise MANOVA and MANCOV Using SAS | Chandu M. Patel | PATEL, H. I., 1979. Analysis of covariance of incomplete data in experiments with repeated measurements in clinical trials pp. In Littell, Ray (ed.), Proe., Fourth Annual Conference of the SAS Users Group, Internat~onal. SAS Institute, Inc., Raleigh, North Carolina. pp. In clinical drug trials, we often have to deal with incomplete data as SOme of the patients under therapy drop out before completing the scheduled treatment period due t~ several reasons. In this situation, the multivariate normal density for the distribution of the vector of observations obtained at success~ve time pOinte is assumed and th~ likelihood ratio test is derived for the com parison of treatment mean vectors in a one-way analys~s of covariance model. The likelihood ratio test; for testing the hypotheses of equal variance-covariance matrices and equal regression slopes are also der1~ed | likelihood function, missing value, covariance, proc genmod, ancova, regression analysis, null hypothesis, clinical trial, homoscedasticity, maximum likelihood estimation, proc step | source | 9 |
| 23 | Applications of SAS at Educational Institutions | Emilio A. Icaza, Kenneth L. Koonce |
This paper reviews the Impact of SAS at universities and colleges In the areas of research, teach fng and admt"lstration~ A brief revIew of the association of University Statisticians of Southern Experiment Stations with SAS Is given. Results of a survey of educational installations of SAS are given. At present most of the uses of SAS appear In research and teaching. Increasingly; educatIonal InstitutIons are applying SAS to adm1nistratlve problems, including data bases for student records and record and file maintenance for comp .... te ... centers. An examp1e of a student records system-l,Jtlllzll"lg SAS at LSU I. given | proc step, data science | source | 5 |
| 24 | Automatic Job Scheduling Using SAS | Robert J. Bronstein | The SAS Automatic Job Schedu1er is a pr~gram geared to those SAS users who must run jobs at specific time Lntervals without the he1p of a coordinator~ The basic flow of the system is to read a file of DD statements that have scheduling information added to them~ If run conditions are met, the job is automatically submitted to HASP for execution. Same of the more advantageous qualities of this proqram are its abilities to handle any program ming language, as well as to select job runs for particular days of th~ week | job scheduling, sysdate9 | source | 3 |
| 25 | Building a Research Data Base through Clerical Staff Using SAS | Tina Brezenoff | A generalized data base system design has been developed based on IBM generation ~ata sets for automatic file backup and a,n SAS76 skeleton program and macrOs to implement lay user controlled building of a multi-form, multi-record data base. A unified error message Teport is produced, easing the problem of having the data carr-ections made, 'by clerical staff. SAS76 was found to be 'sufficiently flexible and powerful to handle the considerable range of -functions required to design a system controlled exclusively by the lay user. The types of errOrS screened for include: 1) input records with invalid key fields, 2) full duplicate records in the input stream, 3) semi-duplicate records in the input stream~ 4) key field inconsistencies between input records and old master records, 5) inp~t records with invalid single data fields 1 and 6) data or data and key field inconsistencies. The program is designed to generate appropri ate actions according to the nature of,the error | error message, data step | source | 6 |
| 26 | Coding Subjects for Analyzing Unbalanced Repeated Measures Designs Using SAS | Jerome R. Busemeyer, James E. Laughlin |
repeated measures, proc genmod | source | 2 | |
| 27 | Correcting and Updating EBCDIC Files with SAS76 | Marilyn Bodow | For an environment with no advanced file edit facilities and where files are stored in EBCDIC format, SAS76 was chosen as the most convenient and flexible language to corr~ct and update an ongoing data base. Advanced -coding features were used to accomplish these objec~ives with minimum effort to the user and with the easiest possible portability of code among- similar a.pplications. The corrections program~ controlled ,by a pro grammerj requires that each T~cord be assigned a unique key field for record identification. One of t~o options occurs: 1. Undesirable records are deleted with IF statements. 2. Records with specific column errors are overwritten with suspended PUT statements. The update program is user controlled. The uSer submits updates of a specific subset of fields. The progr~ checks to insure that the proper number of fields appear and that all characters are numeric. CARDS failing this screening are listed and deleted |
if-then-else, unique key | source | 5 |
| 28 | Cost Accounting an RJE Station with SAS | Rodney H. Strand | proc freq, data science | source | 5 | |
| 29 | DESIGN, a Macro for Constructing Optimum Experimental Designs | Ed R. Jones | A Statistical Analysis System (SAS) macro for constructing optimum experi mental designs for full-rank linear models is described. Basically, the macro selects an optimum set of runs from a list of candidate runs specified by the user. The user has two choices of optimality criteria, D-optimality and A -opt imali ty . The versatility of this macro makes it very useful in practical applications. Since the user can describe the design region by a list of candidate runs, the macro can be applied to any design region. Furthermore, not 'only can the macro be used to construct an initial experimental design, it can also be used to augment a specified design with the flbest tt set of experimental runs. In this way, poorly designed experiments can be "shored up;" and experiments which require certain experimental runs be made can be augmented with an optimum set of experimental runs. Applications of this macro for constructing experiments involving gasoline mixtures are described | predictor variable, sample size | source | 6 |
| 30 | Development of a SAS Data Management System for the Germplasm Collection at the Asian Vegetable Research and Development Center (AVRDC) | John N. Hubbell Jr. | data governance, proc step | source | 4 | |
| 31 | Environmental Data: Management and Analysis Considerations | Rodney H. Strand | Environmental data are a resource of considerable value for evaluating trends and patterns in the environment in which we live. Since environmental data are such a valuable and irreplaceable resource, it is important that these data be organized and accounted for in a systematic manner that retains the representativeness of the sample and allows for the proper application of the data to problem solving. This paper presents considerations involved in the management of environmental data for analysis and evaluation | data governance, dataset, data wrangling | source | 6 |
| 32 | Estimating Passenger Demands from Truncated Samples | Franklin S. Young | standard deviation, sample, monte carlo, proc freq, outlier, likelihood function, regression analysis | source | 5 | |
| 33 | Estimating the Parameters of Biological Models Using SAS | Thomas P. Capizzi, Karen Kral, Robert D. Small |
A biological model is usually repre sented by a system of linear differential equations whose coefficients are to be estimated from observed data. Capizzi and Small have developed a technique which can be applied to virtually all differential models. The parameter esti mates are obtained directly from the unintegrated form through spline regres sion and differentiation followed by linear least squares. A measure of pre~ cis ion which is uncommon for most other | response variable, regression analysis, predictor variable, confidence interval | source | 7 |
| 34 | Exploring Biased Regression with'SAS | Samuel G. Carmer, Wen T. Hsieh |
regression analysis, predictor variable, proc princomp, degrees of freedom, ridge regression, response variable, correlation | source | 6 | |
| 35 | File Definition "Macros "for Census Public Use Samples | Jean B. Holzer | sample, macro definition | source | 5 | |
| 36 | GLMM5 as a Tool for Simultaneous Study of the Structure of the Covariance Matrix and Experimental Design Effects | Ronald W. Helms, James D. Hosking, Keith E. Muller |
covariance, proc step, correlation, proc factor, proc princomp, proc cancorr, proc score, residual analysis, regression analysis | source | 4 | |
| 37 | GLMM5,Analysis of Growth and Dose-Response Curves | Ronald W. Helms, Paul W. Stewart |
The Growth and Dose-Respon~e analyses proposea by Grizzle and Allen (Biometrics, Vol+25, No.2, Jun~ 1969, pp. 357-381) have not been widely applied, in part because appropriate software was not available. This paper shows how such analyses can be performed with GLMM5~ One of the GLMMS macros, GCMPROC, speci~lizes as a flexible growth-curve MODEL procedure. GCMPROC 1 inks to other GLMM5 modules ~s Subroutines to perform standard tasks and provide well documented printout with error messages when necessary |
proc step, covariance | source | 6 |
| 38 | Graphic, Calendar, and U. S. Map Reports for Various Business Applications | William R. Gjertsen | SAS is widely used in business for computer performallce tunlng, market research, proJu...:1:. planning, operations research and MIS reporting as well as for standard statistical applications. Three examples are presented. The second example uses the new ARRAY and DO *** END capabilities of | macro definition, put statement | source | 12 |
| 39 | Graphical Report Generation Using PROC VIVIPLOT | Michael P. Farrell, A. Dale Magoun |
proc step, dataset | source | 6 | |
| 40 | Implementing Relational Database Management Techniques in SAS | William Ingram III | proc step, relational database, normalization, dataset | source | 6 | |
| 41 | Interfacing SAS with Mark IV | Edward L. Spitznagel | input statement, data wrangling | source | 4 | |
| 42 | Management of Evolving Ecological Data Sets with SAS: An Open-ended Management Approach | Karen L. Daniels, Michael P. Farrell, A. Dale Magoun |
Often in ecological pilot field studies t d~ cisions as to data base mariagement, structure t analysEs and report generatiori. can only be specu lative ,since the data base requirements often have to be developed in this phase of the study. In these cases, the lack of appropriate informa tion prior to the study cannot justify detailed and extensive pre-planning of research data base m~naseruent strategies. ~he potential liability of open-minded management of data bases bas to be minimized against the n~ed to be able tQ modify eontent and structure of large data bases. For many complex and large scale ecological field studies being conducted at the Waterways Experi ment Station SAS 76 under ISO provides the frame work for making open-minded research data base management systems practical and cost effective | dataset, quality control, character variable, proc step | source | 8 |
| 43 | Modular Report Writing in-a Clinical-Environment-Using SAS | Robert J. Bronstein | When handling clinical dat~r similarities in report structure can usuaLly be found from clinical project to clinical project. When a detailed analysis is performed l we real~ze that the basic changes required from report to report are necessitated by chanqea in head ing content, and sometimes slight modifications in appear_ance. By utilizing one of the newer features of SAS "PROC l'Oru1AT", as -well as many of the standard features of ShS~ a truly modular report format canT in factI be developed. A typical report request mdght be the following: DATA, SST < FILE NAME > REPORTl Modular report writing is a method of generat ing a maximal amount of computer output from a minimal amount of user specified information. My general- philosophy of a modular report writ ing system is: "Keep it simple; make it functional"-. The modular report writing s~5tem desi9ned for this presentation is exactly that. With as fQw ae two SAS statements |
sample, proc step | source | 5 |
| 44 | Multivariate MultipleComparisons Using SAS | A. Lawrence Gould | main effect, response variable | source | 8 | |
| 45 | New Features in GLM and VARCOMP | James H. Goodnight | proc genmod, fixed effects, likelihood function, numeric functions, proc mixed, proc glm | source | 10 | |
| 46 | Nonlinear Simultaneous Equation Models in Discrete Time | John P. Sall | proc step, proc model, regression analysis | source | 3 | |
| 47 | On-line System Report Generator | Susan E. Bengtson, Byron C. Lewis, Richard Nelson |
this paper was developed with two purposes in mind. First, as a pedagogical tool, and second, as a much needed management tool for the Academic Computing Support group at Clemson University. In addition to teaching regularly scheduled short courses, staff members of Academic Computing Support are often invited to give presentations to various classe.s an topics ranging from statistical analysis to fundamentals of data processing. As a language, SAS is useful for many of these applications. The data manipulation functions of SAS make it possible, for example, to teach the concepts of file updating and and to have students write programs to perform such tasks without the necessity of teaching them COBOL or PL/I. This has been found to be particularly useful in manageme.nt curricula where it is desirable to give the potential manager an idea of the procedures and difficulties involved in data processing without an extensive formal educatipn in the subject | proc freq, data wrangling | source | 3 |
| 48 | PAMS: A Project Activity Management System Implemented in SAS | William Ingram III | dataset, bar chart | source | 4 | |
| 49 | Plotting General Linear Model Parameters and Confidence Bounds with GLMM5 | David H. Christiansen, Ronald W. Helms |
response variable, proc step | source | 7 | |
| 50 | PROC REPTIFMD - A SAS Procedure for Repeated Measurements Experiments with Missing Data | William G. Jackson Jr., Richard C. McNee |
proc genmod, main effect, anova | source | 6 | |
| 51 | Procedures for Analysis of Survival Data | Terry M. Therneau | predictor variable, proc rank, log-rank test, proc step, regression analysis | source | 5 | |
| 52 | Project Control System | Susan E. Bengtson, Byron C. Lewis, Richard Nelson |
proc format, raw data | source | 3 | |
| 53 | Providing Data Processing Support with SAS | William M. Parker | Thecopobillti"s of the Statistical Analysis Sy<tem ($AS) ere much greater than simply providit"l,Q statistical anal ysis. At the North Cent",1 Te"as Council of Govern menls (NCTCOG), SAS i, u",d primarily for Its data management and programming copobilities. The use of SAS has significantly reduced the prOlgramming time 10 respond to ad 'hoc re~uests and has consequently chc:mged the Wr:JY in which data processin',Q support is provided at NCTCOG. This poper will outline the process OIf select ing SAS, provide a comparison of SAS versus other pro gramming language, from the slondpoints of program development cost ond processing e-fficiency, and discuss the cast-sa.vings OIf using $AS | data governance, proc step | source | 6 |
| 54 | Randomization Schedules via SAS | John L. Stedl | proc step, randomization | source | 3 | |
| 55 | Relationships between the Estimable Functions of SAS GLM Output for Unbalanced Data and the Hypotheses Tested by Traditional-style F-statistics | Shayle R. Searle | The Statistical Analysis System (SAS) computer routine entitled General Linear Model (GLM) includes in its output four types of estimable functions that have certain arbitrariness (repre sented by the letter L) in their coeff'iclents. This paper' shows how such arbitrary estimable functions are derived from the known, general expressions ror hypotheses tested by traditional style F~statisticQ in analysis of variance calculations that are often made ror unbalanced data (i.e., data having unequal numbers of observations in their subclasses) | sas output, proc glm | source | 12 |
| 56 | Responsibilities of a SAS User | Richard E. Cooper | sas programmer, view | source | 2 | |
| 57 | Retrieval of Value Labels from SPSS Files for SAS Use and the Creation and Maintenance of a SAS Format Library | David H. Culver, Richard D. Langston |
missing value, format statement, dataset | source | 2 | |
| 58 | SAS as a Management Tool for Course Registration and Grading | Edward L. Spitznagel | From ro~ters to the grading of examinations to assigning courae grad~s, SAS has been an in valuable tool in the management of our large enrollment cOurses. It enables us to post exam ination results the very n~xt day, complete with tables, histograms) and rank-in-class. At the end of the course, we simply m~~ge the individual scores_and compute letter grades by the appropri ate algorithm. Human effort is reduced, accuracy is improved) and the students obtain better feed back on their progres6 throughout the course | sample, proc print | source | 4 |
| 59 | SAS in a Course for Beginning Computer Users | Philip N. Gallagher Jr. | raw data, dataset | source | 3 | |
| 60 | SAS in the Analysis of Categorical Data | Shawki A. Salem | proc genmod, anova | source | 2 | |
| 61 | SAS Macros for Analyzing Growth Curves | Carlos A. Gonzalez | correlation, covariance | source | 4 | |
| 62 | SAS Procedures for Estimating the Parameters of the Makeham Model | R. Clifton Bailey, James P. Summe |
likelihood function, proc step, proc calis | source | 2 | |
| 63 | SAS Programming in an OS Environment | Peter Svendsen | The purpose of this paper simply is to make BAS users aware of the resources made available by the Operating Sys.tem and its associated utility package. ThUs,!- the pap_111arity of this -paper is dire_ctly related to the number of readers who, although quite versed in SAS, may not be aware of fts inter.face with the. Ope.ratic,,"g System. and its resources. Consider a simple file of data {Figure '1). C;:onslider also the problem associated w~th writi-ng OBS STUDY ACCT INV_l | proc step, output delivery system | source | 7 |
| 64 | SAS Used for Data Management and Mathematical Analysis of a Flow Microflourometry Histograms | Warren Curry, William Ingram III, Dennis C. Lezotte |
proc freq, proc step, histogram, data governance | source | 5 | |
| 65 | Statistical Applications of SAS at the 3M Company | B. F. McDonagh | clinical trial, macro definition | source | 3 | |
| 66 | Testing for Trends in Drug Research Data Using SAS | Chiao Yeh | anova, proc genmod, proc glm, proc step, regression analysis | source | 4 | |
| 67 | The "Critical Mass "Concept | John F. Duffy | data wrangling, data governance | source | 4 | |
| 68 | The "Statistical Analysis System "as Implemented at EMBRAPA | Dalton F. Andrade, Cicero A. da Silva, Enedino C. da Silva, Gustavo M. Pimentel |
data wrangling, data science | source | 4 | |
| 69 | The CLMEANS Procedure | Moacir Pedroso Jr. | proc step, degrees of freedom | source | 2 | |
| 70 | The Preparation and Publication of a Toxic Effects Document Using SAS and COM | R. M. Anderson, R. M. Cushman, S. G. Hildebrand, Rodney H. Strand |
proc freq, dataset, sample, proc step | source | ||
| 71 | The Procedure DURBIN | M. E. McBride, Robert P. Parks |
sample, exact test, significance level, proc step, by group processing | source | 6 | |
| 72 | The Role of Package Programs in Statistical Methods Courses | Thomas A. Bubolz, James E. Gentle |
correlation, regression analysis, covariance, data science, multivariate analysis, proc step, anova | source | 7 | |
| 73 | The Role of SAS in the Support of Clinical Studies in Medicine | J. Philip Miller | clinical trial, proc step, data governance, data cleaning | source | 6 | |
| 74 | The South Carolina Papmobile Program: A SAS Application | Bonnie Pleasants Dumas, M. Clinton Miller III, Janet M.' Riggs, Sue L. Stramm, Paul B. Underwood Jr. |
significance level, statistical software | source | 6 | |
| 75 | The Use of SAS in Administrative Computing at Both the University and Federal. Levels | Arnold W. Bragg Jr., Herbert J. Kirk |
dataset, proc step, macro definition, proc printto | source | ||
| 76 | Trimmed Means, Medians, and Robust Tests of Homogeneity of Variance | Donald J. Henderson | A general purpose macro which computes trimmed means is presented. The macro has II BY II statement capabilities, will do multiple vari ables, deletes observations only for the variables for which they are missing, and can handle any number of trims. Any amount of trim ming between 0 and .5, inclusive, is allowed. Another macro is presented that creates a data set which can be used as input to ANOVA(GLM) to obtain Brown and Forsythe's generalization of Levene's test of homogeneity of variance. I NTRODUCTl ON The results of past software ballots have indicated that SAS users place high priority on the addition of tests of homogeneity of variance to SAS. Such tests are available thru the interfaces to SAS 72 and BMDP. Bartlett's test and Levene's test are available in BMDP9D and BMDP7D respectively. Bartlett's, Cochran's, a jackknife test and the F-max test are available in the SAS 72 user contributed procedure HOMOG. All of these programs have limitations | by group processing, central tendency, trimming, anova, sample, proc step, response variable | source | 6 |
| 77 | Use of SAS-in the Development of a Management Information System in a Social Service.Agency | Michael C. Choban, Cynthia Deitz |
A growing ·demand by the general public for· accountability in social. programs is resulting in the rapid development of management information systems in social service setting-s~ However, a lack of understanding on the part of social age.ncy staff regarding the capabilities of automated information systems, as well as a relative .'ack of understanding by data processing personnel of the particular needs and constraints operating in social service settings. has resulted in a low level of satisfaction and only minimal utilization of many of the data systems that have been developed. SAS has a 11 owed a uni que approach to the development of a management information system in a community mental heal.th setting. SAS's flexi bi 1 ity provided an opportunHy for agency staff to sample a wide range of information displays and test their utility over a two year period before maki ng any costly software .i nvestments. The | management reporting, data wrangling | source | 4 |
| 78 | Uses of SAS for Corporate Operations Research | Robert L. Anderson | odds ratio, network optimization | source | 1 | |
| 79 | Using GLMM5 for General Linear Models Analysis - A Tutorial II. Three Examples | Imogene McCanless, Ronald W. Helms |
The purpose of this paper is not to prQvide the reader with the analysis of three datasets J but rather to exemplify the power and flexibility of the GLMMS macro system, to provide the Teader with a few examples of the output that is produced when using the system, and to demon strate the ease with which GL~ may be used to provide statistical analysis of various linear models problems. In this paper, three examples will be dis~ cussed. The first is the analysis of an experiment that is a 2x3 factorial in the design with two dependent variables. The second example illustrates how GLMMS may be used to analyze a multivariate regression problem with four dependent variables, three concomitant variables, and two sex groups, fitting sep~rate intercepts for the two sex groups. The third example involves the analysis of a messy uni variate data problem; the experiment is a 3x4 factorial (unbalanced) design with four missing cells. EXAMPLE 1: A 2x3x2 Factorial Experim~nt | response variable, regression analysis, estimate, main effect, covariance, proc step | source | 9 |
| 80 | Using GLMM5 for General Linear Models Analysis - A Tutorial-: I. A:Brief Overview of the System | Ronald W. Helms, Imogene McCanless |
proc step, correlation, regression analysis, covariance, output delivery system, dataset | source | 7 | |
| 81 | Using SAS for Soil Test Reporting | Jeannie M. Currin, Hoke S. Hill |
A soil test reporting system has been developed for use by the Extension Service of Clemson University. The program inputs soil test lab | sample, dataset | source | 4 |
| 82 | Using U-Model Techniques to Test Hypotheses in the Linear Model | Emilio A. Icaza, Kenneth L. Koonce |
treatment effect, anova | source | 5 | |
| 83 | Writing Simple SAS Procedures | Louis G. Partridge | Frequently it is necessary to write a SAS PROC simply to interface previously written routines to existing procedures. By waiving special features found in more complex SAS PROC's simple versions can be written using the tools available to any beginning programmer. These tools include a high level language such as FORTRAN or PL/I, existing JCL procedures and SAS macro's. The ease of developing such a proce dure will be demonstrated by using a FORTRAN subroutine implementing the Sande Tukey radix-2 fast Fourier transform. The points to be discussed inClude: writing the FORTRAN interface, making the necessary additions to both the FORTRAN JCL PROC and the SAS JCL PROC, passing the data in the SAS program and finally assembling a SAS macro for use by non-programming users |
proc step, macro definition | source | 6 |
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