Using OLS to solve for the slope and intercept of a simple linear regression will yield a regression line that satisfies which of the following conditions?
A) $\frac { \sum _ { i = 1 } ^ { N } e _ { i } X _ { i } } { N }$ = 0
B) $\frac { \sum _ { i = 1 } ^ { N } \left( Y _ { i } - b - m X _ { i } \right) X _ { i } } { N }$ = $\bar { Y }$
C) Sum of squared residuals equals 0
D) None of the answers is correct.

Question

Quizplus · Accepted Answer

The condition A represents one of the key properties of the Ordinary Least Squares (OLS) method in linear regression, which ensures that the sum of the product of residuals (errors) and the independent variable values, averaged over all observations, equals zero. This property is crucial for minimizing the sum of squared residuals, leading to the best-fitting regression line.

Using OLS to Solve for the Slope and Intercept of a Simple