Missing data can be informative. Sometimes missing values in one variable are related to missing values in another variable. Other times missing values in one variable are independent of missing values in other variables. As part of the exploratory phase of data analysis, you should investigate whether there are patterns
In SAS procedures, the WHERE clause is a useful way to filter observations so that the procedure receives only a subset of the data to analyze. The IML procedure supports the WHERE clause in two separate statements. On the USE statement, the WHERE clause acts as a global filter. The
Descriptive univariate statistics are the foundation of data analysis. Before you create a statistical model for new data, you should examine descriptive univariate statistics such as the mean, standard deviation, quantiles, and the number of nonmissing observations. In SAS, there is an easy way to create a data set that
