Given:
\[
x_1 = 1, \, 2, \, 3, \, 4 \quad {and} \quad P(X = x_i) = 0.3, \, 0.1, \, 0.4, \, 0.3
\]
Now, the expected value \( E(x) \) is calculated as:
\[
E(x) = \sum x_i P(x_i) = 1 \times 0.3 + 2 \times 0.1 + 3 \times 0.4 + 4 \times 0.3 = 4.1
\]
Next, the expected value of \( x^2 \) is:
\[
E(x^2) = \sum x_i^2 P(x_i) = 1^2 \times 0.3 + 2^2 \times 0.1 + 4^2 \times 0.3 + 4^2 \times 0.3 = 24.7
\]
The variance is:
\[
V(x) = E(x^2) - (E(x))^2 = 24.7 - (4.1)^2 = 7.89
\]
Finally, the standard deviation is:
\[
\sigma = \sqrt{7.89} = 2.808
\]
Thus, the standard deviation of the random variable is \( 2.8 \).