What is the main purpose of Linear Regression?

Linear regression is primarily used to predict a numeric value based on the relationship between one or more independent variables and a dependent variable. The method involves finding the best-fit line that minimizes the differences between the predicted values and the actual values in the dataset. This line represents the linear relationship, and by applying this model, we can estimate the expected value of the dependent variable given new observations of the independent variables.

In context, predicting categories, visualizing data distribution, or showing parts of a whole do not align with the primary objective of linear regression, which focuses specifically on quantifying and predicting numeric outcomes. Thus, the option that highlights the prediction of a numeric value using a best-fit line directly addresses the core function of linear regression.