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ABSTRACTAfter a brief review of the role of dummy variables in regression analysis and the current state‐of‐the art in rounding/truncation error detection in computerized least squares programs, this paper presents a theorem that can be used to detect this type of error whenever an analyst is running a regression program that has one (or more) dummy variables as independent variables.

This paper is concerned with the description of a specialized form of linear regression analysis commonly known as "dummy variable" regression analysis. Efforts are made to show the relationship between "dummy variable" regression analysis and other multivariate analysis techniques with a specific emphasis on the kinds of problems and data that sociologists deal with where "dummy variable" regression analysis is particularly appropriate and useful. Efforts are also made (1) to give illustrations and examples of problems to which this type of multiple-regression analysis might be applied productively; (2) to show how "dummy variable" regression analysis is both similar to and different from other multivariate techniques in terms of the analytical procedures and the kinds of interpretations that are made possible.