A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?
Linear regression directly models a linear relationship between independent and dependent variables.
Why this answer
Linear regression is the most appropriate technique because the data shows a linear trend with no seasonality, making a straight-line model the simplest and most effective fit. It directly models the relationship between the independent variable (e.g., time) and the dependent variable (monthly sales) using a linear equation, minimizing the sum of squared residuals.
Exam trap
The trap here is that candidates often confuse 'linear trend' with 'linear in parameters' and incorrectly choose polynomial regression, thinking it adds flexibility, when the question explicitly states no seasonality and a linear trend, making simple linear regression the optimal choice.
How to eliminate wrong answers
Option A is wrong because polynomial regression introduces higher-degree terms (e.g., x², x³) to model curvature, which is unnecessary and risks overfitting when the trend is explicitly linear. Option B is wrong because logistic regression is used for binary classification problems (e.g., predicting yes/no outcomes), not for forecasting continuous numeric values like monthly sales. Option D is wrong because ridge regression is a regularization technique designed to handle multicollinearity or overfitting by adding an L2 penalty, but it is not a distinct regression type for linear trends and would be overkill when a simple linear model suffices.