The least squares regression line minimizes the sum of the Group of answer choices differences between actual and predicted y values. absolute deviations between actual and predicted y values. absolute deviations between actual and predicted x values. squared differences between actual and predicted y values

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.

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step-by-step explanation:

a. -0.3 > -0.33