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MY NOTES || ASK YOUR TEACHER PRACTICE ANOTHER [0.3/1 Points] DETAILS PREVIOUS ANSWERS ASWSBE13 14.E.037. You may need to use

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Answer #1
X Y XY
22 9.6 211.2 484 92.16
27 9.6 259.2 729 92.16
32 10.1 323.2 1024 102.01
48 11.1 532.8 2304 123.21
65 11.5 747.5 4225 132.25
85 17.7 1504.5 7225 313.29
120 25.5 3060 14400 650.25
Ʃx = 399
Ʃy = 95.1
Ʃxy = 6638.4
Ʃx² = 30391
Ʃy² = 1505.33
Sample size, n = 7
x̅ = Ʃx/n = 399/7 = 57
y̅ = Ʃy/n = 95.1/7 = 13.585714
SSxx = Ʃx² - (Ʃx)²/n = 30391 - (399)²/7 = 7648
SSyy = Ʃy² - (Ʃy)²/n = 1505.33 - (95.1)²/7 = 213.32857
SSxy = Ʃxy - (Ʃx)(Ʃy)/n = 6638.4 - (399)(95.1)/7 = 1217.7

Sum of Square error, SSE = SSyy -SSxy²/SSxx = 213.32857 - (1217.7)²/7648 = 19.448696

Standard error, se = √(SSE/(n-2)) = √(19.4487/(7-2)) = 1.9722

Slope, b = SSxy/SSxx = 1217.7/7648 = 0.16

y-intercept, a = y̅ -b* x̅ = 13.58571 - (0.16)*57 = 4.51

Regression equation :

ŷ = 4.51 + (0.16) x

Predicted value of y at x = 52.5

ŷ = 4.51 + (0.16) * 52.5 = 12.91

a)

Critical value, t_c = T.INV.2T(0.05, 5) = 2.5706

95% Confidence interval :

Lower limit = ŷ - tc*se*√((1/n) + ((x-x̅)²/(SSxx))) = 12.91 - 2.5706*1.9722*√((1/7) + ((52.5 - 57)²/(7648))) = 10.98

Upper limit = ŷ + tc*se*√((1/n) + ((x-x̅)²/(SSxx))) = 12.91 + 2.5706*1.9722*√((1/7) + ((52.5 - 57)²/(7648))) = 14.84

b)

95% Prediction interval :

Lower limit = ŷ - tc*se*√(1 + (1/n) + ((x-x̅)²/(SSxx))) = 12.91 - 2.5706*1.9722*√(1 + (1/7) + ((52.5 - 57)²/(7648))) = 7.48

Upper limit = ŷ + tc*se*√(1 + (1/n) + ((x-x̅)²/(SSxx))) = 12.91 + 2.5706*1.9722*√(1 + (1/7) + ((52.5 - 57)²/(7648))) = 18.34

d)

Any deductions exceeding the $ 18.34 thousand upper limit could suggest an audit.

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