To find the standard deviation of the probability distribution, we can use the following formula:
σ = ∑ ( x i − μ ) 2 ⋅ P ( x i ) \sigma=\sqrt{\sum(x_i-\mu)^2\cdot P(x_i)} σ = ∑ ( x i − μ ) 2 ⋅ P ( x i ) where:
x i : x_i: x i : the i t h i^{th} i t h value
μ : \mu: μ : the mean of the distribution
P ( x i ) : P(x_i): P ( x i ) : the probability of the i t h i^{th} i t h value
μ = 1 ⋅ 0.10 + 2 ⋅ 0.35 + 3 ⋅ 0.15 + 4 ⋅ 0.23 + 5 ⋅ 0.17 = 3.02 \mu=1\cdot0.10+2\cdot0.35+3\cdot0.15+4\cdot0.23+5\cdot0.17=3.02 μ = 1 ⋅ 0.10 + 2 ⋅ 0.35 + 3 ⋅ 0.15 + 4 ⋅ 0.23 + 5 ⋅ 0.17 = 3.02 X − μ = { 1 − 3.02 , 2 − 3.02 , 3 − 3.02 , 4 − 3.02 , 5 − 3.02 } = X-\mu=\begin{Bmatrix}
1-3.02, 2-3.02, 3-3.02, 4-3.02, 5-3.02
\end{Bmatrix}= X − μ = { 1 − 3.02 , 2 − 3.02 , 3 − 3.02 , 4 − 3.02 , 5 − 3.02 } =
= { − 2.02 , − 1.02 , − 0.02 , 0.98 , 1.98 } =\begin{Bmatrix}
-2.02, -1.02, -0.02, 0.98, 1.98
\end{Bmatrix} = { − 2.02 , − 1.02 , − 0.02 , 0.98 , 1.98 }
σ = ( − 2.02 ) 2 ⋅ 0.10 + ( − 1.02 ) 2 ⋅ 0.35 + ( − 0.02 ) 2 ⋅ 0.15 + 0.9 8 2 ⋅ 0.23 + 1.9 8 2 ⋅ 0.17 = \sigma=\sqrt{(-2.02)^2\cdot 0.10+(-1.02)^2\cdot 0.35+(-0.02)^2\cdot 0.15+0.98^2\cdot 0.23+1.98^2\cdot 0.17}= σ = ( − 2.02 ) 2 ⋅ 0.10 + ( − 1.02 ) 2 ⋅ 0.35 + ( − 0.02 ) 2 ⋅ 0.15 + 0.9 8 2 ⋅ 0.23 + 1.9 8 2 ⋅ 0.17 =
= 1.6596 = 1.288 =\sqrt{1.6596}=1.288 = 1.6596 = 1.288