X Y X Y X 2 Y 2 20 80 1600 400 6400 25 85 2125 625 7225 50 125 6250 2500 15625 30 90 2700 900 8100 45 100 4500 2025 10000 60 135 8100 3600 18225 10 80 800 100 6400 15 70 1050 225 4900 35 100 3500 1225 10000 70 140 9800 4900 19600 S u m = 360 1005 40425 16500 106475 \def\arraystretch{1.5}
\begin{array}{c:c}
& X & Y & XY & X^2 & Y^2 \\ \hline
& 20 & 80 & 1600 & 400 & 6400 \\
& 25 & 85 & 2125 & 625 & 7225 \\
& 50 & 125 & 6250 & 2500 & 15625 \\
& 30 & 90 & 2700 & 900 & 8100 \\
& 45 & 100 & 4500 & 2025 & 10000 \\
& 60 & 135 & 8100 & 3600 & 18225 \\
& 10 & 80 & 800 & 100 & 6400 \\
& 15 & 70 & 1050 & 225 & 4900 \\
& 35 & 100 & 3500 & 1225 & 10000 \\
& 70 & 140 & 9800 & 4900 & 19600 \\
Sum=& 360 & 1005 & 40425 & 16500 & 106475
\end{array} S u m = X 20 25 50 30 45 60 10 15 35 70 360 Y 80 85 125 90 100 135 80 70 100 140 1005 X Y 1600 2125 6250 2700 4500 8100 800 1050 3500 9800 40425 X 2 400 625 2500 900 2025 3600 100 225 1225 4900 16500 Y 2 6400 7225 15625 8100 10000 18225 6400 4900 10000 19600 106475
X ˉ = 1 n ∑ i = 1 n X i = 360 10 = 36 \bar{X}={1\over n}\displaystyle\sum_{i=1}^nX_i={360\over 10}=36 X ˉ = n 1 i = 1 ∑ n X i = 10 360 = 36
Y ˉ = 1 n ∑ i = 1 n Y i = 1005 10 = 100.5 \bar{Y}={1\over n}\displaystyle\sum_{i=1}^nY_i={1005\over 10}=100.5 Y ˉ = n 1 i = 1 ∑ n Y i = 10 1005 = 100.5
S X X = ∑ i = 1 n X i 2 − 1 n ( ∑ i = 1 n X i ) 2 = S_{XX}=\displaystyle\sum_{i=1}^nX_i^2-{1\over n}(\displaystyle\sum_{i=1}^nX_i)^2= S XX = i = 1 ∑ n X i 2 − n 1 ( i = 1 ∑ n X i ) 2 = = 16500 − 1 10 ( 360 ) 2 = 3540 =16500-{1\over 10}(360)^2=3540 = 16500 − 10 1 ( 360 ) 2 = 3540
S Y Y = ∑ i = 1 n Y i 2 − 1 n ( ∑ i = 1 n Y i ) 2 = S_{YY}=\displaystyle\sum_{i=1}^nY_i^2-{1\over n}(\displaystyle\sum_{i=1}^nY_i)^2= S YY = i = 1 ∑ n Y i 2 − n 1 ( i = 1 ∑ n Y i ) 2 = = 106475 − 1 10 ( 1005 ) 2 = 5472.5 =106475-{1\over 10}(1005)^2=5472.5 = 106475 − 10 1 ( 1005 ) 2 = 5472.5
S X Y = ∑ i = 1 n X i Y i − 1 n ( ∑ i = 1 n X i ) ( ∑ i = 1 n Y i ) = S_{XY}=\displaystyle\sum_{i=1}^nX_iY_i-{1\over n}(\displaystyle\sum_{i=1}^nX_i)(\displaystyle\sum_{i=1}^nY_i)= S X Y = i = 1 ∑ n X i Y i − n 1 ( i = 1 ∑ n X i ) ( i = 1 ∑ n Y i ) = = 40425 − 1 10 ( 360 ) ( 1005 ) = 4245 =40425-{1\over 10}(360)(1005)=4245 = 40425 − 10 1 ( 360 ) ( 1005 ) = 4245 Correlation coefficient
r = S X Y S X X S Y Y r={S_{XY}\over \sqrt{S_{XX}}\sqrt{S_{YY}}} r = S XX S YY S X Y
r = 4245 3540 5472.5 ≈ 0.9645 r={4245\over \sqrt{3540}\sqrt{5472.5}}\approx0.9645 r = 3540 5472.5 4245 ≈ 0.9645 This is a strong positive correlation, which means that high X variable scores go with high Y variable scores (and vice versa).
B = S X Y S X X = 4245 3540 ≈ 1.1992 B={S_{XY}\over S_{XX}}={4245\over 3540}\approx1.1992 B = S XX S X Y = 3540 4245 ≈ 1.1992
A = Y ˉ − B X ˉ = 100.5 − 4245 3540 ( 36 ) ≈ 57.3305 A=\bar{Y}-B\bar{X}=100.5-{4245\over 3540}(36)\approx57.3305 A = Y ˉ − B X ˉ = 100.5 − 3540 4245 ( 36 ) ≈ 57.3305
y = 57.3305 + 1.1992 x y=57.3305+1.1992x y = 57.3305 + 1.1992 x Old adults should be given priority. Older adults suffer from high blood pressure.
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