Answer:
To find the best line model for the data, we will use linear regression analysis.
First, we need to calculate the mean of x and y:
mean of x = (0 + 2 + 4 + 6 + 8 + 10 + 12 + 24 + 14) / 9 = 8
mean of y = (9 + 9 + 10 + 11 + 11 + 12 + 13 + 13 + 14) / 9 = 11
Next, we need to calculate the deviations of x and y from their respective means:
deviation of x = x - mean of x
deviation of y = y - mean of y
x y deviation of x deviation of y (deviation of x)^2 deviation of x * deviation of y
0 9 -8 -2 64 16
2 9 -6 -2 36 12
4 10 -4 -1 16 4
6 11 -2 0 4 0
8 11 0 0 0 0
10 12 2 1 4 2
12 13 4 2 16 8
24 13 16 2 256 32
14 14 6 3 36 18
Then, we can calculate the slope (b) of the best-fit line using the formula:
b = sum of (deviation of x * deviation of y) / sum of (deviation of x)^2
b = (16 + 12 + 4 + 0 + 0 + 2 + 8 + 32 + 18) / (64 + 36 + 16 + 4 + 0 + 4 + 16 + 256 + 36) = 0.190
Next, we can calculate the y-intercept (a) of the best-fit line using the formula:
a = mean of y - b * mean of x
a = 11 - 0.190 * 8 = 7.48
Therefore, the equation of the best-fit line is:
y = 0.190x + 7.48
Finally, we can calculate the correlation coefficient (r) using the formula:
r = sum of (deviation of x * deviation of y) / (sqrt(sum of (deviation of x)^2) * sqrt(sum of (deviation of y)^2))
r = (16 + 12 + 4 + 0 + 0 + 2 + 8 + 32 + 18) / (sqrt(64 + 36 + 16 + 4 + 0 + 4 + 16 + 256 + 36) * sqrt(16 + 16 + 1 + 0 + 0 + 1 + 4 + 4 + 1)) = 0.905
Therefore, the best-fit line for the given data is y = 0.190x + 7.48, and the correlation coefficient is r = 0.905.
Explanation: