The article discusses different ways to analyze data in SAS, focusing on fixed-effect hypotheses and expected mean squares in linear models. It explores how the types of sums of squares vary based on the data structure, like the presence of empty cells. The researchers specifically look at two-factor analysis of variance models to illustrate key points. Overall, the study provides insights into how to effectively analyze data using SAS procedures for linear models.