SAS has provided users with a new, intuitive, and powerful tool for developing information systems. SAS/EIS software provides users with a vast set of objects to aid in the development of custom information applications. In this tutorial, a simple bank account application will be developed. The following tasks will be stepped through:

Take another intriguing and sometimes mystifying tour of Humongus, Inc. and learn of the different ways that SAS arrays can assist you in your day-to-day programming requirements. This paper will present various programming situations in which SAS arrays can be used to streamline and automate work processes.

This tutorial introduces the SAS implementation of the SQL language. It explains the basic structures and functions used in PROC SQL. Attendees will be shown how to use SQL to extract data from SAS data sets, report on that data, and build new tables and views from extracted data. For listeners in a data mode, plenty of attention will be given the Select statement and the various forms of Union (or Join). Examples will compare the SQL way of data processing with other SAS techniques, including the DATA step and PROC SORT. Use of the SQL window may also be demonstrated (given time).

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 phoneticize 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 SAS 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.

PROC TABULATE is a versatile tool which can combine the functionality of PROC MEANS and PROC FREQ. Additional power exists in its ability to perform analysis on grouped data. This paper details the steps for constructing a table of marketing statistics using a variety of features. Missing values and formats are used to control the table calculations. Labeling and selected options provide finishing touches for controlling the table appearance.

As a SAS consultant, you often receive unfamiliar data. A first step in analyzing or reporting on the data is to familiarize yourself with the data. For small files, or only a few files, simply printing the data may suffice. As the number of files and size of each file increases, this becomes increasingly less effective. We cover several techniques to investigate large files including: checking the structure of the data sets, finding duplicate variables, determining if the data values are reasonable, finding holes (unexpectedly large numbers or missing or identical values), using formats to handle long character values, using indicator variables instead of numeric variables, and more. We also cover general strategies, such as documenting the data sets as received, documenting changes, using subsets of the data for testing, and keeping the original data sets intact. Many of these tools are useful even for cases where you cannot change the original data, since the results give you a better understanding of the data. Examples are primarily from the pharmaceutical industry, but the techniques can be useful for other types of data as well.

There are a number of simple efficiency techniques that should be kept in mind whenever you are constructing a SAS program. These basic practices will almost always result in programs that run faster and use less computer resources. Beginning programmers are often relieved to just get programs to execute successfully, without being mindful of speed or resources. But in many organizations, computer resources are a scarce commodity. As a result, inefficient code is - at the very least - frowned upon, if not directly penalized in the form of unnecessarily high chargebacks. This paper presents a few of the simple efficiency techniques that all programmers, especially beginners, should consider when constructing SAS programs.

A data warehouse can be defined as a stable and predictable environment which enables decision making. But just exactly does that mean? What really is a data warehouse? And how is one created? This talk covers the basics of data warehousing and covers the major issues that must be addressed in creating one. This is a talk for novices in this rapidly growing area, designed to give an appreciation of the scope of a warehouse project.

PROC TABULATE lets you create tables with statistics in the cells (instead of frequency counts), and with combinations of variables in each dimension of the table. On the other hand, very few procedures require their own 300-page manual. This tutorial presents a way to learn the procedure quickly, a method of getting started without having to study every available statement and option.

Statistical methods are tools which are used to summarize and analyze data. Exploratory data analysis is the application of graphical and statistical techniques to discover the structure of data. The goal of exploratory data analysis is to characterize the data and to reveal fundamental relationships among them. It is quick, dynamic, and highly interactive. Furthermore, exploratory data analysis is not just for use by professional statisticians -- the methods also are used by scientists, engineers, and many other types of researchers. This paper explains how to produce and to interpret scatter plots, histograms, stem-and-leaf plots, box-and-whisker plots, and various descriptive statistics, using Base SAS software. This paper should be accessible to people having experience with basic DATA and procedure programming, but no prior knowledge of statistics is required.