Ace the Business Stats & Analytics Exam 2026 – Level Up Your Data Game!

Linear regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In this context, the dependent variable is numerical, meaning it is continuously measurable, such as height, weight, or income.

When using linear regression, the model seeks to find the best-fitting line (or hyperplane in multiple dimensions) that represents the relationship between the independent variables (which can be either numerical or categorical) and the dependent variable, allowing for the prediction of outcomes based on those independent variables. This capability is a core strength of linear regression.

Given this understanding, the statement that linear regression allows the prediction of a numerical dependent variable from independent variables is indeed true, as the primary function of the method is to establish that predictive relationship, making the answer correct.

It's also important to note that while independent variables can be either numerical or categorical—after appropriate encoding for categorical variables—the analysis fundamentally targets numerical predictions for the dependent variable, reinforcing the accuracy of the response.