Answer on Question #43537, Math, Statistics and Probability
Let X X X be given by its "distribution" function F ( X ) F(X) F ( X ) such that:
F ( x ) = { 0 , if x ≤ 0 x 2 4 , if 0 < x ≤ 2 1 , if x > 2 F(x) = \begin{cases}
0, & \text{if } x \leq 0 \\
\frac{x^2}{4}, & \text{if } 0 < x \leq 2 \\
1, & \text{if } x > 2
\end{cases} F ( x ) = ⎩ ⎨ ⎧ 0 , 4 x 2 , 1 , if x ≤ 0 if 0 < x ≤ 2 if x > 2
Find E ( X ) E(X) E ( X ) , var ( X ) \operatorname{var}(X) var ( X ) and std \operatorname{std} std deviation ( X ) (X) ( X ) .
Solution.
By definition,
E ( X ) = ∫ − ∞ ∞ x f ( x ) d x E(X) = \int_{-\infty}^{\infty} x f(x) \, dx E ( X ) = ∫ − ∞ ∞ x f ( x ) d x
where f ( x ) = F ′ ( X ) f(x) = F'(X) f ( x ) = F ′ ( X ) . So,
f ( X ) = { 0 , if x ∉ ( 0 , 2 ] x 2 , if x ∈ ( 0 , 2 ] f(X) = \begin{cases}
0, & \text{if } x \notin (0, 2] \\
\frac{x}{2}, & \text{if } x \in (0, 2]
\end{cases} f ( X ) = { 0 , 2 x , if x ∈ / ( 0 , 2 ] if x ∈ ( 0 , 2 ]
Then,
E ( X ) = ∫ − ∞ 0 x ⋅ 0 d x + ∫ 0 2 x 2 2 d x + ∫ 2 ∞ x ⋅ 0 d x = 0 + 2 3 6 − 0 + 0 = 4 3 E(X) = \int_{-\infty}^{0} x \cdot 0 \, dx + \int_{0}^{2} \frac{x^2}{2} \, dx + \int_{2}^{\infty} x \cdot 0 \, dx = 0 + \frac{2^3}{6} - 0 + 0 = \frac{4}{3} E ( X ) = ∫ − ∞ 0 x ⋅ 0 d x + ∫ 0 2 2 x 2 d x + ∫ 2 ∞ x ⋅ 0 d x = 0 + 6 2 3 − 0 + 0 = 3 4
By definition,
var ( X ) = ∫ − ∞ ∞ x 2 f ( x ) d x − ( E ( X ) ) 2 = ∫ − ∞ 0 x 2 ⋅ 0 d x + ∫ 0 2 x 3 2 d x + ∫ 2 ∞ x 2 ⋅ 0 d x − 16 9 = = 0 + 2 4 8 + 0 − 16 9 = 2 − 16 9 = 2 9 \begin{aligned}
\operatorname{var}(X) &= \int_{-\infty}^{\infty} x^2 f(x) \, dx - \left(E(X)\right)^2 = \int_{-\infty}^{0} x^2 \cdot 0 \, dx + \int_{0}^{2} \frac{x^3}{2} \, dx + \int_{2}^{\infty} x^2 \cdot 0 \, dx - \frac{16}{9} = \\
&= 0 + \frac{2^4}{8} + 0 - \frac{16}{9} = 2 - \frac{16}{9} = \frac{2}{9}
\end{aligned} var ( X ) = ∫ − ∞ ∞ x 2 f ( x ) d x − ( E ( X ) ) 2 = ∫ − ∞ 0 x 2 ⋅ 0 d x + ∫ 0 2 2 x 3 d x + ∫ 2 ∞ x 2 ⋅ 0 d x − 9 16 = = 0 + 8 2 4 + 0 − 9 16 = 2 − 9 16 = 9 2
By definition,
std deviation ( X ) = var ( X ) \operatorname{std} \text{ deviation}(X) = \sqrt{\operatorname{var}(X)} std deviation ( X ) = var ( X )
So,
std deviation ( X ) = 2 3 \operatorname{std} \text{ deviation}(X) = \frac{\sqrt{2}}{3} std deviation ( X ) = 3 2
Answer: E ( X ) = 4 / 3 E(X) = 4/3 E ( X ) = 4/3 , var ( X ) = 2 / 9 \operatorname{var}(X) = 2/9 var ( X ) = 2/9 , std deviation ( X ) = 2 / 3 \operatorname{std} \text{ deviation}(X) = \sqrt{2}/3 std deviation ( X ) = 2 /3 .
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