The OLS method A. minimizes the sum of the residuals. B. maximizes the sum of the residuals. C. maximizes the sum of the squared residuals. D. minimizes the sum of the squared residuals.

D. minimizes the sum of the squared residuals

Step-by-step explanation: The ordinary least square method is often used in locating the trendine which best fits a graphical linear model. The best is one in which the sum of the squared residual is smallest. The residual refers to the difference between the actual and the predicted points. The sum of the squared differences is obtained and the trend line is positioned where the residual is minimum. Choosing a OLS, and minimizing the sum.of the squared residual, the error difference between the predicted and actual score is minimized or reduced, hence, improving the prediction accuracy of our model.

d

step-by-step explanation:

d

1. 22

2. 1

3. 7

4. 18

step-by-step explanation

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