X Y X Y X 2 Y 2 26 345 8970 676 119025 27 322 8694 729 103684 28 357 9996 784 127449 29 423 12267 841 178929 30 435 13050 900 189225 S u m = 140 1882 52977 3930 718312 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2\\
\hline
& 26 & 345 & 8970 & 676 & 119025\\
& 27 & 322 & 8694 & 729 & 103684\\
& 28 & 357 & 9996 & 784 & 127449\\
& 29 & 423 & 12267 & 841 & 178929\\
& 30 & 435 & 13050 & 900 & 189225\\
Sum= & 140 & 1882 & 52977 & 3930 & 718312\\
\end{array} S u m = X 26 27 28 29 30 140 Y 345 322 357 423 435 1882 X Y 8970 8694 9996 12267 13050 52977 X 2 676 729 784 841 900 3930 Y 2 119025 103684 127449 178929 189225 718312 X ˉ = 1 n ∑ i X i = 140 5 = 28 \bar{X}=\dfrac{1}{n}\sum_iX_i=\dfrac{140}{5}=28 X ˉ = n 1 i ∑ X i = 5 140 = 28
Y ˉ = 1 n ∑ i Y i = 1882 5 = 376.4 \bar{Y}=\dfrac{1}{n}\sum_iY_i=\dfrac{1882}{5}=376.4 Y ˉ = n 1 i ∑ Y i = 5 1882 = 376.4
S S X X = ∑ i ( X i − X ˉ ) 2 = 10 SS_{XX}=\sum_i(X_i-\bar{X})^2=10 S S XX = i ∑ ( X i − X ˉ ) 2 = 10
S S Y Y = ∑ i ( Y i − Y ˉ ) 2 = 9927.2 SS_{YY}=\sum_i(Y_i-\bar{Y})^2=9927.2 S S YY = i ∑ ( Y i − Y ˉ ) 2 = 9927.2
S S X Y = ∑ i ( X i − X ˉ ) ( Y i − Y ˉ ) = 281 SS_{XY}=\sum_i(X_i-\bar{X})(Y_i-\bar{Y})=281 S S X Y = i ∑ ( X i − X ˉ ) ( Y i − Y ˉ ) = 281
m = s l o p e = S S X Y S S X X = 281 10 = 28.1 m=slope=\dfrac{SS_{XY}}{SS_{XX}}=\dfrac{281}{10}=28.1 m = s l o p e = S S XX S S X Y = 10 281 = 28.1
n = Y ˉ − m X ˉ n=\bar{Y}-m\bar{X} n = Y ˉ − m X ˉ
= 376.4 − 28.1 ( 28 ) =376.4-28.1(28) = 376.4 − 28.1 ( 28 )
= − 410.4 =-410.4 = − 410.4 Therefore, we find that the regression equation is:
Y = − 410.4 + 28.1 X Y=-410.4+28.1X Y = − 410.4 + 28.1 X
Correlation coefficient:
r = S S X Y S S X X S S Y Y r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}}\sqrt{SS_{YY}}} r = S S XX S S YY S S X Y
= 281 10 9927.2 = 0.8918523 =\dfrac{281}{\sqrt{10}\sqrt{9927.2}}=0.8918523 = 10 9927.2 281 = 0.8918523 We have strong positive correlation.
The regression equation is:
Y = − 410.4 + 28.1 X Y=-410.4+28.1X Y = − 410.4 + 28.1 X
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