Answer:
The least squares regression minimizes the sum of squared differences between actual and predicted y-values. This is the last option in your list of possible answers.
Step-by-step explanation:
Recall that when one performs the least square regression, one deals with what are called the "residuals". These are the differences in between the predicted "y-value" from the best fitting function, and the actual value of the dependent variable plotted (the actual y-value of the plotted points)
What the least square regression does is to find the best function parameters that minimize the sum of these squared residuals.
