In: Statistics and Probability
Subject | x | y |
1 | 16 | 25 |
2 | 14 | 31 |
3 | 10 | 16 |
4 | 5 | 18 |
5 | 10 | 22 |
Find the linear correlation coefficient.
X: X Values
Y: Y Values
Mx: Mean of X Values
My: Mean of Y Values
X - Mx & Y - My: Deviation
scores
(X - Mx)2 & (Y -
My)2: Deviation Squared
(X - Mx)(Y - My): Product of
Deviation Scores
x | y | X - Mx | Y - My | (X - Mx)2 | (Y - My)2 | (X - Mx)(Y - My) |
16 | 25 | 5 | 2.6 | 25 | 6.76 | 13 |
14 | 31 | 3 | 8.6 | 9 | 73.96 | 25.8 |
10 | 16 | -1 | -6.4 | 1 | 40.96 | 6.4 |
5 | 18 | -6 | -4.4 | 36 | 19.36 | 26.4 |
10 | 22 | -1 | -0.4 | 1 | 0.16 | 0.4 |
Mx: 11.000 | My: 22.400 | Sum: 72.000 | Sum: 141.200 | Sum: 72.000 |
Result Details & Calculation
X Values
∑ = 55
Mean = 11
∑(X - Mx)2 = SSx = 72
Y Values
∑ = 112
Mean = 22.4
∑(Y - My)2 = SSy = 141.2
X and Y Combined
N = 5
∑(X - Mx)(Y - My) = 72
R Calculation
r = ∑((X - My)(Y - Mx)) / √((SSx)(SSy))
r = 72 / √((72)(141.2)) = 0.7141
Meta Numerics (cross-check)
r = 0.7141
The value of R is 0.7141.
This is a moderate positive correlation, which means there is a tendency for high X variable scores go with high Y variable scores (and vice versa).
The value of R2, the coefficient of determination, is 0.5099.