Dummy variables were introduced in Section 12.8 in applications in models applied to two discrete categories of data. For example, we saw used to test for gender discrimination in the salary example. In this section we expand the potential applications of dummy present an application in which a regression model is applied to more x32 categories of data. Next, we show how dummy variables can be used sonal effects on a regression model applied to time-series data. Fina dummy variables can be used to analyze data from experimental sit defined by multiple-level categorical variables. We also provide an e how dummy variables can be used for public policy analysis. Example 13.1 Demand for Wool Products (Dummy Variable Model Analysis) A senior marketing analyst for the American Wool Producers Associa in estimating the demand for wool products in various cities as a fun posable income in the city. Data were gathered from 30 randomly s Metropolitan Statistical Areas (SMSAs). As a first step the analyst spe model for the relationship between sales and disposable income: Y = Bo + B1X1 where X, is the per capita annual disposable income for a city and Y sales of wool products in the city. After some additional discussions, ders if overall sales levels differ among different geographic regions: r south