Step 1 :Calculate the sum of \( x \) values: \( \sum x = 139.0 \)
Step 2 :Calculate the sum of \( y \) values: \( \sum y = 541.8 \)
Step 3 :Calculate the sum of the product of corresponding \( x \) and \( y \) values: \( \sum xy = 8871.93 \)
Step 4 :Calculate the sum of the squares of \( x \) values: \( \sum x^2 = 2261.9 \)
Step 5 :Calculate the sum of the squares of \( y \) values: \( \sum y^2 = 34911.18 \)
Step 6 :Use the formula for Pearson's correlation coefficient: \( r = \frac{n(\sum xy) - (\sum x)(\sum y)}{\sqrt{[n\sum x^2 - (\sum x)^2][n\sum y^2 - (\sum y)^2]}} \)
Step 7 :Substitute the values into the formula: \( r = \frac{9(8871.93) - (139.0)(541.8)}{\sqrt{[9(2261.9) - (139.0)^2][9(34911.18) - (541.8)^2]}} \)
Step 8 :Calculate the linear correlation coefficient: \( r = 0.9808184962339461 \)
Step 9 :Round the result to three decimal places: \( r = 0.981 \)
Step 10 :Final Answer: \(\boxed{0.981}\)