AI Engineering Degree 2025 – 400 Free Practice Questions to Pass the Exam

Multiple Linear Regression is particularly effective in scenarios where the relationship between the dependent variable and multiple independent variables is linear. In the context of predicting rainfall amounts, the factors of wind speed and temperature can contribute to a model where statistical relationships can be derived. These variables likely have a linear relationship with the amount of rainfall, making Multiple Linear Regression an appropriate choice for this analysis.

In contrast, estimating average sales based on historical data may not capture the complexities of sales dynamics effectively if numerous influencing factors are omitted, making it less suited for a regression analysis focused solely on averages. The second scenario, predicting a company's total profit based on advertising spend, is typically more aligned with a simpler linear regression model since profit may also be determined by many other factors not considered in this limited view. Lastly, classifying emails as spam or not is fundamentally a classification problem rather than a regression one, as it involves categorical outcomes rather than continuous numerical predictions, which is outside the scope of Multiple Linear Regression’s applicability. Thus, predicting rainfall amounts based on multiple continuous variables aligns best with the capabilities of Multiple Linear Regression.