What two values can a dummy variable regression model take on for beta?

In a dummy variable regression model, the primary focus is to capture categorical variables that can take on only limited values; thus, it is most common for these variables to represent two distinct states or categories. When applying dummy variables in regression, one can effectively encode binary outcomes or conditions—often represented numerically as 0 and 1.

In the context of this specific question about the values that beta can take on in a dummy variable regression model, it is important to understand that the regression coefficients (betas) associated with the dummy variables will reflect the difference in the expected value of the dependent variable between the two categories they represent.

The correct answer indicates that the two values for beta in a dummy variable regression model are indeed 0 and 1. These values depict the presence or absence of a particular characteristic or condition in the analysis, where a value of 0 signifies one category (for instance, the absence of a trait), and a value of 1 signifies the other (the presence of a trait).

In contrast, the other options relate to different financial concepts such as market betas (which quantify the volatility relative to a market index), alpha (which measures performance relative to a benchmark), and market return versus the risk-free rate, none of