To find the variance of \( g(X) \), we need to follow these steps:
- Compute the expected value \( E[g(X)] \).
- Compute the expected value \( E[g(X)^2] \).
- Use the formula for variance: \( \sigma_{g(X)}^2 = E[g(X)^2] - (E[g(X)])^2 \).
Given the density function:
\[
f(x) = \begin{cases}
\frac{2(x+1)}{3}, & 0 < x < 1 \\
0, & \text{elsewhere}
\end{cases}
\]
and the function \( g(X) = 2X^2 + 3 \).
The expected value \( E[g(X)] \) is calculated as:
\[
E[g(X)] = \int_{0}^{1} g(x) f(x) \, dx = \int_{0}^{1} (2x^2 + 3) \left( \frac{2(x+1)}{3} \right) \, dx
\]
Evaluating this integral, we get:
\[
E[g(X)] = \frac{34}{9}
\]
First, compute \( g(X)^2 \):
\[
g(X)^2 = (2X^2 + 3)^2 = 4X^4 + 12X^2 + 9
\]
Then, compute the expected value \( E[g(X)^2] \):
\[
E[g(X)^2] = \int_{0}^{1} g(x)^2 f(x) \, dx = \int_{0}^{1} (4x^4 + 12x^2 + 9) \left( \frac{2(x+1)}{3} \right) \, dx
\]
Evaluating this integral, we get:
\[
E[g(X)^2] = \frac{659}{45}
\]
Using the formula for variance:
\[
\sigma_{g(X)}^2 = E[g(X)^2] - (E[g(X)])^2
\]
Substitute the values:
\[
\sigma_{g(X)}^2 = \frac{659}{45} - \left( \frac{34}{9} \right)^2
\]
Simplify the expression:
\[
\sigma_{g(X)}^2 = \frac{151}{405}
\]
Convert to decimal and round to three decimal places:
\[
\sigma_{g(X)}^2 \approx 0.373
\]