In order to compute the regression coefficients, the following table needs to be used:
X Y X Y X 2 Y 2 2 8 16 4 64 4 10 40 16 100 6 12 72 36 144 7 6 42 49 36 10 16 160 100 256 S u m = 29 52 330 205 600 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2 \\ \hline
& 2 & 8 & 16 & 4 & 64 \\
\hdashline
& 4 & 10 & 40 & 16 & 100 \\
\hdashline
& 6 & 12 & 72 & 36 & 144 \\
\hdashline
& 7 & 6 & 42 & 49 & 36 \\
\hdashline
& 10 & 16 & 160 & 100 & 256 \\
\hdashline
Sum= & 29 & 52 & 330 & 205 & 600 \\
\hdashline
\end{array} S u m = X 2 4 6 7 10 29 Y 8 10 12 6 16 52 X Y 16 40 72 42 160 330 X 2 4 16 36 49 100 205 Y 2 64 100 144 36 256 600
X ˉ = 1 n ∑ i X i = 29 5 = 5.8 \bar{X}=\dfrac{1}{n}\sum _{i}X_i=\dfrac{29}{5}=5.8 X ˉ = n 1 i ∑ X i = 5 29 = 5.8
Y ˉ = 1 n ∑ i Y i = 52 5 = 10.4 \bar{Y}=\dfrac{1}{n}\sum _{i}Y_i=\dfrac{52}{5}=10.4 Y ˉ = n 1 i ∑ Y i = 5 52 = 10.4
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 = 205 − 2 9 2 5 = 36.8 =205-\dfrac{29^2}{5}=36.8 = 205 − 5 2 9 2 = 36.8
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 = 600 − ( 52 ) 2 5 = 59.2 =600-\dfrac{(52)^2}{5}=59.2 = 600 − 5 ( 52 ) 2 = 59.2
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 ) = 330 − 29 ( 52 ) 5 = 28.4 =330-\dfrac{29(52)}{5}=28.4 = 330 − 5 29 ( 52 ) = 28.4
r = S S X Y S S X X S S Y Y = 28.4 36.8 ( 59.2 ) r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}SS_{YY}}}=\dfrac{28.4}{\sqrt{36.8(59.2)}} r = S S XX S S YY S S X Y = 36.8 ( 59.2 ) 28.4
= 0.608462 =0.608462 = 0.608462
0.4 < r < 0.7 0.4<r<0.7 0.4 < r < 0.7 Moderate positive correlation
m = s l o p e = S S X Y S S X X = 28.4 36.8 = 0.7717 m=slope=\dfrac{SS_{XY}}{SS_{XX}}=\dfrac{28.4}{36.8}=0.7717 m = s l o p e = S S XX S S X Y = 36.8 28.4 = 0.7717 n = Y ˉ − m X ˉ = 10.4 − 28.4 36.8 ( 5.8 ) = 5.9239 n=\bar{Y}-m\bar{X}=10.4-\dfrac{28.4}{36.8}(5.8)=5.9239 n = Y ˉ − m X ˉ = 10.4 − 36.8 28.4 ( 5.8 ) = 5.9239
The regression equation is:
y = 5.9239 + 0.7717 x y=5.9239+0.7717x y = 5.9239 + 0.7717 x
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