SUGI.ONE 1976 Papers - Index

30 papers. Source: SAS Users Group International (SUGI.ONE) papers and slides from the first SAS conference.

go 1977

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Comparison of BMD, SAS and SPSS	T. Papaioannou, G.P.H. Styan, L.L. Ward		anova, regression analysis, covariance, correlation, sample, manova, ancova, proc freq, proc means, factor analysis, proc reg, proc glm, multivariate analysis, fixed effects, missing value, multiple regression, proc mixed, sample size, histogram	source	21
2	A Conversational Front End to SAS	Julian Horwich, Mark Phelps		dataset, data science	source	5
3	Abbott Written Additions to SAS	Jane Abel, George C. Chao, Julian Horwich, Jim Trefren	The SAS language provides a vehicle for custom developing specialized computer sub-languages to fit a given application. At Abbott, SAS development first began with the modification of existing FROCs. Later our own FROCs were designed and written. The FROCs were developed by the four authors and by a summer worker, Carolyn Smelter.	numeric variable, dataset	source	2
4	ANOVA on Unbalanced Design with Unequal Cells Using SAS	George C. Chao		main effect, proc glm	source	5
5	Calculating Coefficients in Expected Mean Squares Using SAS.	Henry K. Hess	CALCULATDfG COEFFICIENTS IN EXPECTED MEAN :J\UAliES USING BAS Henry K. Hess NUS Corporation Eoological Soiences Division The method of "synthesis" used in calculating the coefficients in the expected mean squares is illustrated with the use of program statements	anova, proc mixed, proc anova, response variable, two-way anova	source	14
6	Comments on ANOVA Calculations for Messy Data	Shayle R. Searle		anova, residual	source	11
7	Data Management for Environmental Research	Rodney H. Strand		data governance, quality control, data science, access control, missing value	source	18
8	Discriminant or Cluster Analysis	Paul D. Hopkins, Harold F. Huddleston		proc step, proc discrim, training set, covariance, sample, confusion matrix	source	11
9	Econometric Uses of SAS	Max Moszer, Martha S. Shuler		regression analysis, predictor variable, time series analysis, proc step, correlation, sample, response variable, proc reg, maximum likelihood estimation, outlier	source	26
10	General Linear Model Procedure	James H. Goodnight		proc genmod, main effect, response variable, class variable, interval variable, regression analysis, proc step, proc glm, anova	source	39
11	Map Construction and.Geolocational.Analyses with SAS and a GEOZIP File	Steven R. Borbash		proc step, input statement	source	21
12	Medical Data Base Management and Report Generation Using SAS	Richard H. Browne		dataset, proc step	source	13
13	Meeting Federal Health Manpower Statistical System Requirements with SAS	Steven R. Borbash		proc step, data wrangling	source	17
14	More Efficient Use of SAS (or the Effects of Indulging SAS)	Robert L. Anderson	The purpose of this paper is not to expose any previously unknown or highlY sophisticated techniques for making mare efficient use of SAS. The author's intent is to discuss a collection of very simple methods he uses'for much greater efficiency. Hopefully, this will work as a catalyst to stimulate discussion and thinking among other users so that the total effect will be great savings to all. Many userS of SAS are not. systems analysts or even programmers and :ar~ not aware of the effect that various uses (or abuses) of SAS ~ : have on an Operating System. I have heard complaints from many of my associates in academia, government, and in business about the inefficiencies of SAS. SAS is not inefficient at doing wnat it is designed to do and with proper caution it can be used as a valuable tool for many non-statistical endeavors. Hopefully, through more consciousness in our .use of SAS we can generate a much wider acceptance of it as the valuable . tool that it is. : My remarks will	dataset, proc options	source	6
15	Non-Statistical Uses of SAS	Steven Mays		dataset, proc step, proc print, comment, proc sort, proc report	source	15
16	NPAR Using SAS 75	Daniel M. Chilko		anova, format statement, correlation, data science, spearman correlation	source	4
17	Plots of Paging Data	J.F. Chambers			source	3
18	Reply to Comments on a Comparison of BMD, SAS and SPSS & The Relationship of SAS to Other Packages	James W. Frane, Jonathan B. Fry, James H. Goodnight, T. Papaioannou, Stewart Robinovitz, G.P.H. Styan, Neal Van Eck, L.L. Ward	This article is by far better than the average comparison of statistical pack ages in spite of the general difficulty of keeping reasonably current in more than one package. I have just a few remarks. I am not sure how to take the statements regarding the BMDP manual. I don't view a complete description of a program in an organized style as incompatible with a system being integrated. While allowing communication through Save Files and utilizing a common control language, the BMDP programs can each be run without wading through a large number of pages of introduction. While the BMDP control language and self-documented Save File are certainly important improvements over the old BMD programs, the improvements in the data ana lytic capabil iti es are much more important. Use of Br·1DP programs is suggested not so much because of ease but because of the information they provide	regression analysis, stepwise selection	source	10
19	SAS/SMF Report	Jim Guthrie		cpu time, length statement	source	15
20	Statistical and Data Support to a Heterogeneous User Community	Marion C. Carter, Richard C. Roistacher		dataset, format statement, data governance, data wrangling, data science	source	14
21	The Design of the SAS Matrix Procedure	John P. Sall		proc step, do loop	source	15
22	The In-House Training of-Non-Computer Oriented Research Personnel in the Use of SAS	Neil M. Casey		input statement, proc print	source	16
23	The Relationship of SAS to Other Packages	J. Philip Miller		proc freq, data science	source	8
24	Tutorial on Procedure Writing	John P. Sall		decimal, proc step, sample	source	9
25	Two Tools for Residual Analysis in Multiple Regression	Ronald B. Wiley		outlier, regression analysis, predictor variable, multiple regression	source	36
26	Unbalanced Two-Way ANOVA and SAS	David G. Herr, William A. Powers		anova, cpu time, response variable, main effect, regression analysis	source	9
27	Use of SAS in Systems Programming Department	Jim Trefren			source	14
28	Use of SAS in Teaching a First Course in Statistics	Edward L. Spitznagel	In this paper I will describe the evolutionary process by Which w£ have come to use SAS in Our basic statistics course. We did not begin our use of computing with SAS, and we have not yet fully converted to SAS, but I think you will find the story I can tell so far to be an interesting one. It began in 1970, When we modified our traditional two semester probability-and-statistics course to one that placed more statistical methods into the first semester. The topics covered in the new first semester became: 1) Bssic ideas of discrete probability, leading to a test for fairness of a coin. 2) Three nonparametric tests: the test of a 2 by 2 contingency table (Which we dubbed Pearson's test), the Wilcoaon-Mann-Whitney test, and the test for significance of Kendall's tau statistic. 3) Discrete and continuous rsndom variables, mean and variance, normal random variables, the central Ifmit theorem. 4) Known-variance methods for one and two populations	proc freq, nonparametric test	source	5
29	Use of the Harvey Procedure	Walter R. Harvey	Numerous computer programs have been written to analyze data under the fixed linear model. Many of these are available for a nominal charge. Most of these programs seem to have been written as ordinary multiple regression programs but some of them do contain options which allow the user to obtain sums of squares for discrete sets of effects and adjusted or least squares treatment means.	correlation, treatment effect, proc freq, regression analysis, proc calis, response variable, covariance, proc mixed	source	3
30	Using SAS for Univariate Time Series Analysis via the Box Jenkins Technique	Mark J. Nicolich	There are a multitude of reasons for relating a set of independent variables to a dependent variable; either forecasting, modeling, or determination of influences. The best known and widely used technique is regression analysis.	correlation, sample, moving average, time series analysis, proc step, ma, homoscedasticity, response variable, first difference, predictor variable, stationarity, regression analysis	source	20

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