Which statement accurately describes the purpose of regression analysis?

The purpose of regression analysis is to determine the relationship between dependent and independent variables. In a regression model, one variable is considered the dependent variable (the outcome we want to predict or explain), while one or more independent variables are used to predict this outcome. By doing so, regression analysis quantifies how changes in the independent variables affect the dependent variable, allowing researchers and analysts to draw insights about the nature and strength of these relationships.

This approach is particularly useful in various fields, such as economics, medicine, and social sciences, where understanding how different factors influence an outcome is crucial. For example, a researcher might use regression analysis to examine how changes in income (an independent variable) relate to spending on education (a dependent variable).

The other choices do not accurately capture the primary purpose of regression analysis. While identifying outliers might be a part of data analysis, it is not the main goal of regression. Similarly, assessing the strength of a dataset or measuring central tendency pertains to descriptive statistics rather than the relationship modeling that regression focuses on. Therefore, the accurate depiction of regression analysis being tied to understanding the relationship between dependent and independent variables clarifies its central role in statistical modeling and inference.