X Y X Y X 2 Y 2 4 4 16 16 16 5 6 30 25 36 3 5 15.8 9 25 6 7 42 36 49 10 7 70 100 49 S u m = 28 29 173 186 175 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2\\
\hline
& 4 & 4 & 16 & 16 & 16\\
& 5 & 6 & 30 & 25 & 36\\
& 3 & 5 & 15.8 & 9 & 25\\
& 6 & 7 & 42 & 36 & 49\\
& 10 & 7 & 70 & 100 & 49\\
Sum= & 28 & 29 & 173 & 186 & 175\\
\end{array} S u m = X 4 5 3 6 10 28 Y 4 6 5 7 7 29 X Y 16 30 15.8 42 70 173 X 2 16 25 9 36 100 186 Y 2 16 36 25 49 49 175 ∑ i X i = 28 , ∑ i Y i = 29 \sum_iX_i=28, \sum_iY_i=29 i ∑ X i = 28 , i ∑ Y i = 29
∑ i X i Y i = 173 , ∑ i X i = 186 , ∑ i Y i = 175 \sum_iX_iY_i=173,\sum_iX_i=186, \sum_iY_i=175 i ∑ X i Y i = 173 , i ∑ X i = 186 , i ∑ Y i = 175 X ˉ = 1 n ∑ i X i = 28 5 = 5.6 \bar{X}=\dfrac{1}{n}\sum_iX_i=\dfrac{28}{5}=5.6 X ˉ = n 1 i ∑ X i = 5 28 = 5.6
Y ˉ = 1 n ∑ i Y i = 29 5 = 5.8 \bar{Y}=\dfrac{1}{n}\sum_iY_i=\dfrac{29}{5}=5.8 Y ˉ = n 1 i ∑ Y i = 5 29 = 5.8
S S X X = ∑ i X i 2 − 1 n ( ∑ i X i ) 2 = 100 − ( 28 ) 2 5 SS_{XX}=\sum_iX_i^2-\dfrac{1}{n}(\sum_iX_i)^2=100-\dfrac{(28)^2}{5} S S XX = i ∑ X i 2 − n 1 ( i ∑ X i ) 2 = 100 − 5 ( 28 ) 2
= 29.2 =29.2 = 29.2
S S Y Y = ∑ i Y i 2 − 1 n ( ∑ i Y i ) 2 = 175 − ( 29 ) 2 5 SS_{YY}=\sum_iY_i^2-\dfrac{1}{n}(\sum_iY_i)^2=175-\dfrac{(29)^2}{5} S S YY = i ∑ Y i 2 − n 1 ( i ∑ Y i ) 2 = 175 − 5 ( 29 ) 2
= 6.8 =6.8 = 6.8
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_iX_i)(\sum_iY_i) S S X Y = i ∑ X i Y i − n 1 ( i ∑ X i ) ( i ∑ Y i )
= 173 − 28 ( 29 ) 5 = 10.6 =173-\dfrac{28(29)}{5}=10.6 = 173 − 5 28 ( 29 ) = 10.6
m = s l o p e = S S X Y S S X X m=slope=\dfrac{SS_{XY}}{SS_{XX}} m = s l o p e = S S XX S S X Y
= 10.6 29.2 = 0.3630137 =\dfrac{10.6}{29.2}=0.3630137 = 29.2 10.6 = 0.3630137
n = Y ˉ − m X ˉ n=\bar{Y}-m\bar{X} n = Y ˉ − m X ˉ
= 5.8 − 0.3630137 ( 5.6 ) =5.8-0.3630137(5.6) = 5.8 − 0.3630137 ( 5.6 )
= 3.7671233 =3.7671233 = 3.7671233 Therefore, we find that the regression equation is:
Y = 3.7671233 + 0.363013 X Y=3.7671233+0.363013X Y = 3.7671233 + 0.363013 X
Correlation coefficient:
r = S S X Y S S X X S S Y Y = 10.6 29.2 6.8 r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}}\sqrt{SS_{YY}}}=\dfrac{10.6}{\sqrt{29.2}\sqrt{6.8}} r = S S XX S S YY S S X Y = 29.2 6.8 10.6
= 0.752246 =0.752246 = 0.752246
Strong positive correlation.
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