3. The regression line p = 3 + 2x has been fitted to the data points (4, 8), (2, 5), and (1, 2). The sum of the squared residuals will be: a. 7 b. 15 C. B d. 22 4. A regression analysis between sales (in $1000) and advertising (in $) resulted in the following least squares line: Sales = 80,000 + 4 Advertising. This implies that: a. an increase of $1 in advertising is expected to result in an increase of $4 in sales b. an increase $4 in advertising is expected to result in an increase of $4,000 in sales c. an increase of $1 in advertising is expected to result in an increase of $80,004 in sales d. an increase of $1 in advertising is expected to result in an increase of $4,000 in sales 5. Given the least squares regression line Y = 2.48 - 1.63 X, and a coefficient of determination of 0.81, the coefficient of correlation is: a. -0.85 b. 0.85 C. -0.90 d. 10.90