In order to compute the regression coefficients, the following table needs to be used:
X Y X Y X 2 Y 2 18 8 144 324 64 20 6 120 400 36 25 5 125 625 25 27 2 54 729 4 S u m = 90 21 443 2078 129 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2 \\ \hline
& 18 & 8 & 144 & 324 & 64 \\
\hdashline
& 20 & 6 & 120 & 400 & 36 \\
\hdashline
& 25 & 5 & 125 & 625 & 25 \\
\hdashline
& 27 & 2 & 54 & 729 & 4 \\
\hdashline
Sum= & 90 & 21 & 443 & 2078 & 129 \\
\end{array} S u m = X 18 20 25 27 90 Y 8 6 5 2 21 X Y 144 120 125 54 443 X 2 324 400 625 729 2078 Y 2 64 36 25 4 129
X ˉ = 1 n ∑ i X i = 90 4 = 22.5 \bar{X}=\dfrac{1}{n}\sum _{i}X_i=\dfrac{90}{4}=22.5 X ˉ = n 1 i ∑ X i = 4 90 = 22.5
Y ˉ = 1 n ∑ i Y i = 21 4 = 5.25 \bar{Y}=\dfrac{1}{n}\sum _{i}Y_i=\dfrac{21}{4}=5.25 Y ˉ = n 1 i ∑ Y i = 4 21 = 5.25
S S X X = ∑ i X i 2 − 1 n ( ∑ i X i ) 2 SS_{XX}=\sum_iX_i^2-\dfrac{1}{n}(\sum _{i}X_i)^2 S S XX = i ∑ X i 2 − n 1 ( i ∑ X i ) 2 = 2078 − 9 0 2 4 = 53 =2078-\dfrac{90^2}{4}=53 = 2078 − 4 9 0 2 = 53
S S Y Y = ∑ i Y i 2 − 1 n ( ∑ i Y i ) 2 SS_{YY}=\sum_iY_i^2-\dfrac{1}{n}(\sum _{i}Y_i)^2 S S YY = i ∑ Y i 2 − n 1 ( i ∑ Y i ) 2 = 129 − ( 21 ) 2 4 = 18.75 =129-\dfrac{(21)^2}{4}=18.75 = 129 − 4 ( 21 ) 2 = 18.75
S S X Y = ∑ i X i Y i − 1 n ( ∑ i X i ) ( ∑ i Y i ) SS_{XY}=\sum_iX_iY_i-\dfrac{1}{n}(\sum _{i}X_i)(\sum _{i}Y_i) S S X Y = i ∑ X i Y i − n 1 ( i ∑ X i ) ( i ∑ Y i ) = 443 − 90 ( 21 ) 4 = − 29.5 =443-\dfrac{90(21)}{4}=-29.5 = 443 − 4 90 ( 21 ) = − 29.5
r = S S X Y S S X X S S Y Y = − 29.5 53 ( 18.75 ) r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}SS_{YY}}}=\dfrac{-29.5}{\sqrt{53(18.75)}} r = S S XX S S YY S S X Y = 53 ( 18.75 ) − 29.5
= − 0.9358 =-0.9358 = − 0.9358
Strong negative correlation
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