Assume the given table is:
x P r ( X = x ) 0 0.1 1 0.1 2 0.2 3 0.3 4 0.2 5 0.1 \begin{array}{c:c}
x &Pr(X=x) \\
\hdashline
0 & 0.1
\\
\hdashline
1 & 0.1\\
\hdashline
2 & 0.2\\
\hdashline
3 & 0.3\\
\hdashline
4 & 0.2
\\
\hdashline
5 & 0.1
\end{array} x 0 1 2 3 4 5 P r ( X = x ) 0.1 0.1 0.2 0.3 0.2 0.1
Now, mean= E ( X ) = Σ x . P ( x ) = 0 × 0.1 + 1 × 0.1 + 2 × 0.2 + 3 × 0.3 + 4 × 0.2 + 5 × 0.1 =E(X)=\Sigma x.P(x)=0\times0.1+1\times0.1+2\times0.2+3\times0.3+4\times0.2+5\times0.1 = E ( X ) = Σ x . P ( x ) = 0 × 0.1 + 1 × 0.1 + 2 × 0.2 + 3 × 0.3 + 4 × 0.2 + 5 × 0.1
= 0.1 + 0.4 + 0.9 + 0.8 + 0.5 = 2.7 =0.1+0.4+0.9+0.8+0.5
\\=2.7 = 0.1 + 0.4 + 0.9 + 0.8 + 0.5 = 2.7
E ( X 2 ) = Σ x 2 . P ( x ) E(X^2)=\Sigma x^2.P(x) E ( X 2 ) = Σ x 2 . P ( x )
= 0 2 × 0.1 + 1 2 × 0.1 + 2 2 × 0.2 + 3 2 × 0.3 + 4 2 × 0.2 + 5 2 × 0.1 = 0.1 + 0.8 + 2.7 + 3.2 + 2.5 = 9.3 =0^2\times0.1+1^2\times0.1+2^2\times0.2+3^2\times0.3+4^2\times0.2+5^2\times0.1
\\=0.1+0.8+2.7+3.2+2.5
\\=9.3 = 0 2 × 0.1 + 1 2 × 0.1 + 2 2 × 0.2 + 3 2 × 0.3 + 4 2 × 0.2 + 5 2 × 0.1 = 0.1 + 0.8 + 2.7 + 3.2 + 2.5 = 9.3
Then, v a r i a n c e = E ( X 2 ) − [ E ( x ) ] 2 = 9.3 − 2. 7 2 = 2.01 variance=E(X^2)-[E(x)]^2=9.3-2.7^2=2.01 v a r ian ce = E ( X 2 ) − [ E ( x ) ] 2 = 9.3 − 2. 7 2 = 2.01
And standard deviation= v a r i a n c e = 2.01 = 1.417 =\sqrt{variance}=\sqrt{2.01}=1.417 = v a r ian ce = 2.01 = 1.417
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