IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

The best model for predicting salary based on education level is linear regression because the goal is to establish a relationship between a continuous dependent variable (salary) and an independent variable (education level). Linear regression is specifically designed for regression tasks, where the outcome is a continuous variable. It captures the linear relationship between the variables by fitting a line that best describes how the salary changes with variations in education level.

In this context, education level can often be treated as a numerical value or ordinal variable (for example, levels such as high school, bachelor's, master's, etc.), allowing for a direct mapping to a specific salary. Linear regression provides an interpretable model where coefficients represent the expected change in salary for a one-unit change in education level.

Other options, such as logistical regression and classification, are more suited for scenarios where the outcome is categorical rather than continuous. A sigmoid operation is a mathematical function that is often utilized within logistic regression but does not serve as a standalone model for prediction. Therefore, linear regression is the most appropriate choice for this context.