A predictor variable is a variable that is used to predict or estimate a future outcome based on given circumstances. It is also known as an independent variable or explanatory variable. Predictor variables can be either continuous or categorical. A continuous predictor variable is a variable that can take on any value within a certain range, while a categorical predictor variable is a variable that can take on a limited number of values.

In an experiment, variables of interest that are measured or observed are called response or dependent variables, while other variables that affect the response and can be set or measured by the experimenter are called predictor, explanatory, or independent variables. For example, in a cake recipe experiment, the baking time and oven temperature can be predictor variables, while the moisture and thickness of the cake can be response variables.

Predictor variables are commonly used in data science and the scientific method to make predictions for dependent variables. They are often mistaken for independent variables, but they differ slightly in definition. While an independent variable may be transformed or changed throughout the experiment, the predictor variable is not.

Overall, a predictor variable is a crucial component in predicting or estimating future outcomes based on given circumstances.