What is used to make predictions about scores on one variable from another?

Regression is a statistical method used to make predictions about one variable based on the scores of another variable. It establishes a relationship between the dependent variable (the variable being predicted) and one or more independent variables (the predictors). By analyzing the relationship and trends in the data, regression allows for estimating unknown values of the dependent variable when the independent variable values are known.

In practical terms, if you have a dataset where you want to predict, for example, a person's test score based on hours studied, regression will create a mathematical equation that best fits the data points. The output is a predictive model that can help in making informed decisions based on the relationship identified.

This differs from other methods mentioned. For instance, factor analysis is employed to identify underlying relationships among a set of variables, not directly to make predictions. Correlation measures the strength and direction of a relationship between two variables but does not involve predicting one variable based on another. Descriptive analysis, on the other hand, focuses on summarizing data to describe its main features, without attempting to predict outcomes.