In order to compute the regression coefficients, the following table needs to be used:
X Y X Y X 2 Y 2 10 14 140 100 196 12 17 204 144 289 15 23 345 225 529 23 25 575 529 625 20 21 420 400 441 S u m = 80 100 1684 1398 2080 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2 \\ \hline
& 10 & 14 & 140 & 100 & 196 \\
\hdashline
& 12 & 17 & 204 & 144 & 289 \\
\hdashline
& 15 & 23 & 345 & 225 & 529 \\
\hdashline
& 23 & 25 & 575 & 529 & 625 \\
\hdashline
& 20 & 21 & 420 & 400 & 441 \\
\hdashline
Sum= & 80 & 100 & 1684 & 1398 & 2080 \\
\hdashline
\end{array} S u m = X 10 12 15 23 20 80 Y 14 17 23 25 21 100 X Y 140 204 345 575 420 1684 X 2 100 144 225 529 400 1398 Y 2 196 289 529 625 441 2080
X ˉ = 1 n ∑ i X i = 80 5 = 16 \bar{X}=\dfrac{1}{n}\sum _{i}X_i=\dfrac{80}{5}=16 X ˉ = n 1 i ∑ X i = 5 80 = 16
Y ˉ = 1 n ∑ i Y i = 100 5 = 20 \bar{Y}=\dfrac{1}{n}\sum _{i}Y_i=\dfrac{100}{5}=20 Y ˉ = n 1 i ∑ Y i = 5 100 = 20
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 = 1398 − 8 0 2 5 = 118 =1398-\dfrac{80^2}{5}=118 = 1398 − 5 8 0 2 = 118
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 = 2080 − ( 100 ) 2 5 = 80 =2080-\dfrac{(100)^2}{5}=80 = 2080 − 5 ( 100 ) 2 = 80
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 ) = 1684 − 80 ( 100 ) 5 = 84 =1684-\dfrac{80(100)}{5}=84 = 1684 − 5 80 ( 100 ) = 84
r = S S X Y S S X X S S Y Y = 84 118 ( 80 ) r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}SS_{YY}}}=\dfrac{84}{\sqrt{118(80)}} r = S S XX S S YY S S X Y = 118 ( 80 ) 84
= 0.8646 > 0.7 =0.8646>0.7 = 0.8646 > 0.7
Strong positive correlation
m = s l o p e = S S X Y S S X X = 84 118 = 0.7119 m=slope=\dfrac{SS_{XY}}{SS_{XX}}=\dfrac{84}{118}=0.7119 m = s l o p e = S S XX S S X Y = 118 84 = 0.7119 n = Y ˉ − m X ˉ = 20 − 84 118 ( 16 ) = 8.6102 n=\bar{Y}-m\bar{X}=20-\dfrac{84}{118}(16)=8.6102 n = Y ˉ − m X ˉ = 20 − 118 84 ( 16 ) = 8.6102
i) The regression equation is:
y = 8.6102 + 0.7119 x y=8.6102+0.7119x y = 8.6102 + 0.7119 x
ii)
x = 30 x=30 x = 30
y = 8.6102 + 0.7119 ( 30 ) y=8.6102+0.7119(30) y = 8.6102 + 0.7119 ( 30 )
y = 29.9672 S h s . m i l l i o n s y=29.9672\ Shs. millions y = 29.9672 S h s . mi ll i o n s
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