We have the following data from year 0 to year 10: 0 | 6340 1 | 7747 2 | 8777 3 | 9809 4 | 10299 5 | 11083 6 | 12373 7 | 12971 8 | 13128 9 | 13608 10| 14679 To manually compute for the regression line's y-intercept, we need to compute for the following first: sum of all the x's, sum of all the y's, sum of the squares of x, sum of the product of x and y Hence, we have the following: sum(x) = 55 sum(y) = 120814 sum(x^2) = 385 sum(xy) = 690660 The slope-intercept form of an equation is y = mx + b. And to estimate the value of the slope, m, we have m = [ sum(xy) - sum(x)sum(y)/n ] /[ sum(x^2) - (sum(x))^2/n ], where n is the number of data points m = [690660 - (55)(120814)/11]/ [385 - (55^2)/11] m ≈ 787.18 Next, to solve for the y-intercept, we have b = mean(y) - m*mean(x) b = 120814/11 - 787.18(55/11) b ≈ 7047.18 This is the manual calculation for the regression line. This can also be done easily with Excel or other data analysis tools available. The y-intercept can be estimated from the best-fit line equation. Seeing that the value of the y-intercept is close to 7000, the answer is A: 7000.