The variance of a linear transformation of a random variable follows the rule: \[ \text{Var}(ax + b) = a^2 \cdot \text{Var}(x) \] Given that \( a = h \) and \( b = 0 \), we apply this property: \[ \text{Var}(h \cdot x) = h^2 \cdot \text{Var}(x) \] Since \( \text{Var}(x) = \sigma^2 \), we get: \[ \text{Var}(h \cdot x) = h^2 \sigma^2 \]