The least squares regression line minimizes the sum of the:

(A) Differences between actual and predicted Y values.

(B) Absolute deviations between actual and predicted Y values.

(C) Absolute deviations between actual and predicted X values

(D) Squared differences between actual and predicted Y values

(E) Squared differences between actual and predicted X values.

d) Squared differences between actual and predicted Y values.

Step-by-step explanation:

Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable. Such formula could be used to generate values of given value X.

For example,

suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.

It minimize the squared difference between actual and predicted value.