We are given a uniform random variable \( U \sim U(10, 19) \). The parameters of this distribution are:
The quantile function for a uniform distribution is defined as:
\[ Q(p) = a + p \times (b - a) \]
For our case, we want to find the 0.75 quantile, so we set \( p = 0.75 \):
\[ Q(0.75) = 10 + 0.75 \times (19 - 10) \]
Calculating the difference:
\[ 19 - 10 = 9 \]
Now substituting back into the equation:
\[ Q(0.75) = 10 + 0.75 \times 9 \]
Calculating \( 0.75 \times 9 \):
\[ 0.75 \times 9 = 6.75 \]
Thus, we have:
\[ Q(0.75) = 10 + 6.75 = 16.75 \]
The calculated quantile \( Q(0.75) = 16.75 \) is already rounded to two decimal places.
The 0.75 quantile of the uniform distribution \( U(10, 19) \) is:
\[ \boxed{16.75} \]
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