The course consists of three parts, which may be taken individually or as a whole:
I Windows Pre-session: The pre-session is intended to make the SAS sessions accessible to those without previous experience with Windows on personal computers. Only the bare essentials of Windows will be covered; those familiar with Windows need not attend.
II Basic Introduction: Sessions One and Two provide an overview of SAS and its underlying logic; an explanation of the use of the Display Manager System to run a SAS job; an introduction to the SAS Data step for reading, transforming, and storing data; and a demonstration of how statistical analyses may be performed in SAS Proc (procedure) steps.
III Intermediate Topics: Sessions Three and Four will concentrate on SAS programming techniques to modify data and enhance SAS output. As well, more statistical procedures will be introduced.
The purpose of this course is to show how to use S in a Unix environment. The course will have both lecture and hands-on components. Participants will receive access to an account for running S+. The four sessions of the course will cover approximately the following material:
I The Unix Environment: Logging in, mail, editing with vi, directory structure, basic file manipulation and introduction to the X-Window environment.
II Basic Use of S: Data input, manipulating data, printing, history mechanism, S arrays and data frames, random number generation, arithmetic operators; functions for manipulating data structures: apply and category; help facilities.
III Programming and Graphics in S: Writing S functions, one- and two-dimensional graphs, interactive graphs.
IV Introduction to the Use of Statistical Functions in S: Regression and regression diagnostics based on the linear-model function, lm().
An Introduction to S and S-Plus by Phil Spector (Duxbury Press, 1994, 286 pages, ISBN 0-534-19866-X, $35.95) is a recommended text and it is available from the York University Bookstore.
While graphical display techniques are common adjuncts to analysis of variance and regression, methods for plotting contingency table data are not as widely used. Moreover, while statistical methods can be used to determine which variables are related, the numerical summaries and parameter estimates do not provide easy ways to show how those variables are related.
This workshop provides a brief introduction to graphical methods which are useful for understanding the pattern of association among categorical variables. These methods can be helpful for both data exploration and for communicating results to others. Some of the methods described include:
This course is an updated version of Graphical Methods for Categorical Data, with somewhat greater introductory material on logistic regression and loglinear models. Categorical Data Analysis Using the SAS(R) System by Stokes, Davis, and Koch (SAS Institute, 1995, ISBN 1-55544-219-6) is a useful adjunct to the course and will be available in the York bookstore.