Which of the following best describes a dummy variable?

A dummy variable is best described as a variable that can indicate categorical outcomes. It is a representation of a categorical variable that transforms qualitative data into a quantitative format. For instance, in statistical modeling, a dummy variable can take on binary values—typically 0 and 1—to indicate the absence or presence of a certain characteristic or category. This allows for the inclusion of categorical information in regression analyses and other statistical models effectively.

Using dummy variables enables analysts to incorporate categorical data, such as gender, color preference, or any other nominal data, into mathematical formulas while maintaining the integrity of the information. By coding these categories in such a binary fashion, you create a way to quantify qualitative distinctions that can be analyzed within a statistical context.

The other choices do not accurately characterize dummy variables. Continuous values pertain to numerical data that can take on an infinite number of values rather than categorical distinctions. While dummy variables are often used in linear regression, they are not limited exclusively to this type of modeling, as they can also be utilized in various statistical contexts. Finally, although a dummy variable can take the value of one, it does not always do so; its value depends on the specific category being represented (it can also be zero for other categories).