What defines simple linear regression?

Simple linear regression is defined as a statistical method that models the relationship between two variables by fitting a linear equation to the observed data. In this context, it specifically involves one dependent variable that you are trying to predict and one independent (explanatory) variable that is used for the prediction. The goal is to find the best-fitting line that minimizes the differences between the observed values and the values predicted by the model, which allows for making predictions or understanding the relationship between the two variables.

Simple linear regression is used in a variety of fields, such as economics, biology, and social sciences, to assess how changes in the explanatory variable affect outcomes in the dependent variable. The method conveys a straightforward interpretation: for each unit change in the independent variable, the dependent variable will change by a certain amount as indicated by the slope of the line.

The other options describe concepts that are not aligned with the definition of simple linear regression. For example, multiple explanatory variables would pertain to multiple linear regression. Analyzing qualitative data does not fit within the framework of linear regression, as it typically deals with quantitative variables. Finally, involving more than two variables would align with multiple correlation analysis, which also diverges from the single-variable focus of simple linear regression.