Understanding the given data:
- u: 10, 15, 25, 20, 35
- v: 24, 36

Calculating the correlation coefficient:
1. Calculate the mean of u and v:
- Mean of u = (10 + 15 + 25 + 20 + 35) / 5 = 21
- Mean of v = (24 + 36) / 2 = 30
2. Calculate the covariance of u and v:
- Covariance(u, v) = Σ[(u - mean of u) * (v - mean of v)] / n-1
- = [(10-21)*(24-30) + (15-21)*(36-30) + (25-21)*(24-30) + (20-21)*(36-30) + (35-21)*(24-30)] / 4
- = [(-11)*(-6) + (-6)*6 + (4)*(-6) + (-1)*6 + (14)*(-6)] / 4
- = (66 - 36 - 24 - 6 - 84) / 4
- = (-84) / 4
- = -21
3. Calculate the standard deviation of u and v:
- Standard deviation of u = √[Σ(u - mean of u)^2 / n-1]
- Standard deviation of v = √[Σ(v - mean of v)^2 / n-1]
4. Calculate the correlation coefficient:
- Correlation coefficient = Covariance(u, v) / (Standard deviation of u * Standard deviation of v)
- = -21 / (standard deviation of u * standard deviation of v)

Interpreting the correlation coefficient:
- If the correlation coefficient is close to 1, it indicates a strong positive relationship between u and v.
- If the correlation coefficient is close to -1, it indicates a strong negative relationship between u and v.
- If the correlation coefficient is close to 0, it indicates no relationship between u and v.
Therefore, by calculating the correlation coefficient, we can determine the relationship between u and v in the given data set.