Ohio Assessments for Educators (OAE) Mathematics Practice Exam

A simple regression is fundamentally used to represent the relationship between two variables, specifically to model the relationship in a way that allows for predictions or insights about how one variable affects another. This statistical method calculates a best-fit line through data points on a scatterplot, where typically one variable is independent (often referred to as the predictor or explanatory variable) and the other is dependent (the response variable). The main goal is to describe how changes in the independent variable correspond to changes in the dependent variable.

Through this modeling, simple regression provides not only a visual representation of the relationship but also quantifies it with an equation. This equation can be used to make predictions about the dependent variable using known values of the independent variable. Thus, simple regression is a powerful tool for understanding linear relationships in data, making option B the correct choice.

The other options focus on different statistical concepts: calculating the median or mode deals with measures of central tendency, while measuring dispersion pertains to the spread of data points around a central value, which are not the primary functions of simple regression.