The minimizes the sum of values. $\left( Y - Y ^ { \prime } \right) ^ { 2 }$
A)least-squares regression line
B)coefficient of determination
C)correlation line
D)standard error of the estimate

Question

Quizplus · Accepted Answer

The least-squares regression line minimizes the sum of squared residuals (differences between observed and predicted values), which is equivalent to minimizing the sum of (Y - Y'). Therefore, option A is the correct choice. The coefficient of determination (option B) represents the proportion of variability in Y that is explained by the regression model, not the sum of squared differences. The correlation line (option C) is not a commonly used term in regression analysis. The standard error of the estimate (option D) is a measure of the variability around the regression line, but it does not directly minimize the sum of squared differences.

The Minimizes the Sum of Values (Y−Y′)2\left( Y - Y ^ { \prime } \right) ^ { 2 }(Y−Y′)2

The Minimizes the Sum of Values $\left( Y - Y ^ { \prime } \right) ^ { 2 }$