Step 1: Understanding the Concept:
The correlation coefficient is a measure of the strength and direction of a linear relationship between two variables. It is scale-invariant.
Step 2: Key Formula or Approach:
The correlation coefficient between two variables \(X\) and \(Y\) is:
\[
r = \frac{\text{Cov}(X, Y)}{\text{SD}(X) \text{SD}(Y)}
\]
If we transform \(X \to aX\) and \(Y \to bY\) with \(a, b > 0\), then:
\[
\text{Cov}(aX, bY) = ab \, \text{Cov}(X, Y), \quad \text{SD}(aX) = a \, \text{SD}(X), \quad \text{SD}(bY) = b \, \text{SD}(Y)
\]
So,
\[
r_{new} = \frac{ab \, \text{Cov}(X, Y)}{a \, \text{SD}(X) \cdot b \, \text{SD}(Y)} = \frac{\text{Cov}(X, Y)}{\text{SD}(X) \text{SD}(Y)} = r
\]
Step 3: Detailed Explanation:
Since both variables are multiplied by positive constants, the correlation coefficient remains unchanged.
Thus, the new correlation coefficient is the same as the original.
Step 4: Final Answer:
Therefore, option (D) is correct.