SAS (Statistical Analysis System) is a 4th generation procedural language that allows programmers to use statistical data analysis and data dining to build descriptive and predictive statistical models. Data mining is the process of discovering previously unknown, actionable and profitable information from large consolidated databases and using it to support tactical and strategic business decisions and it is made very easy with SAS.

The statistical techniques of data mining include linear and logistic regression, multivariate analysis, principal components analysis, decision trees and neural networks. Traditional approaches to statistical inference fail with large databases, however, because with hundreds of thousands or millions of cases and hundreds or thousands of variables there will be a high level of redundancy among the variables. With this data, there will be spurious relationships, and even the weakest relationships will be highly significant by any statistical test. The objective with SAS is to build a model with significant predictive power. It is not enough just to find which relationships are statistically significant.

Other capabilities included SAS modeling management & deployment which streamlines the process of creating, managing and deploying analytical models. Another capability of the language is forecasting or analyzing and predicting outcomes based on statistical historical patterns. Econometrics, statistical methods to economic data, problems and trends is also available. The language also allows for continuous quality assurance and improvement by the ability to identify, monitor and measure quality processes over time. Operations research is also available by applying techniques such as optimization, scheduling, and simulation to achieve the best results over time.

Robert Stein has worked for Microsoft and Lotus and has been an avid SAS programmer for over a decade. Please visit for additional info on SAS.