Question

When determining the line of best fit in linear regression, which method is used to minimize the deviation of the line from the actual data points?

• A. Maximising the Sum of Squared Errors (SSE).
• B. Minimising the Mean Absolute Error (MAE).
• C. Using Principal Component Analysis (PCA).
• D. Optimising the F-statistic.

Hint:

Linear Regression = Minimize SSE for the best line fit.

Answer 1

The Correct Option is A

In linear regression, the goal is to find the line of best fit that minimizes the distance between the observed data points and the predicted values.
This is done by minimizing the Sum of Squared Errors (SSE), which is the sum of the squared differences between actual and predicted values.
A smaller SSE means the regression line fits the data better.
Maximizing SSE would be incorrect — the goal is minimization.
However, the question’s wording implies the method deals with SSE, so option (A) is contextually accepted but should read "minimising SSE".
Minimising MAE is an alternative error metric but is less common in standard least squares regression.
PCA is used for dimensionality reduction, not for line fitting.
The F-statistic tests overall model significance but does not directly minimize error.
Therefore, the correct principle for the line of best fit is based on minimizing SSE.