The table contains data on the number of females graduating from an engineering program each year, starting with the inaugural year, year 0. using your scatter plot tool, draw the scatter plot and display the best-fit line. which value is closest to the value of the regression line’s y-intercept?
year female engineering graduates
0 6340
1 7747
2 8777
3 9809
4 10299
5 11083
6 12373
7 12971
8 13128
9 13608
10 14679
a) 7,000
b) 8,000
c) 9,000
d) 10,000

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.