The probability distribution function (PDF) of the given random variable X is
f X ( x ) = 3 2 x 2 f_X(x)=\frac{3}{2}x^2 f X ( x ) = 2 3 x 2 , for − 1 ≤ x ≤ 1 -1\leq x\leq 1 − 1 ≤ x ≤ 1 and f X ( x ) = 0 f_X(x)=0 f X ( x ) = 0 elsewhere.
The cumulative distribution function (CDF) of X is
F X ( x ) = ∫ − ∞ x f X ( t ) d t F_X(x)=\int\limits_{-\infty}^{x}f_X(t)dt F X ( x ) = − ∞ ∫ x f X ( t ) d t
Hence
F X ( x ) = 0 F_X(x)=0 F X ( x ) = 0 , for x<1
F X ( x ) = 1 F_X(x)=1 F X ( x ) = 1 , for x>1
F X ( x ) = ∫ − 1 x 3 2 t 2 d t = 1 2 t 3 ∣ − 1 x = ( x 3 + 1 ) / 2 F_X(x)=\int\limits_{-1}^x\frac{3}{2}t^2dt=\frac{1}{2}t^3|_{-1}^x=(x^3+1)/2 F X ( x ) = − 1 ∫ x 2 3 t 2 d t = 2 1 t 3 ∣ − 1 x = ( x 3 + 1 ) /2 for − 1 ≤ x ≤ 1 -1\leq x\leq 1 − 1 ≤ x ≤ 1 .
Consider the random variable Y = X 2 / 2 Y=X^2/2 Y = X 2 /2 .
F Y ( y ) = P ( Y ≤ y ) = P ( X 2 / 2 ≤ y ) = P ( X 2 ≤ 2 y ) F_Y(y)=P(Y\leq y)=P(X^2/2\leq y)=P(X^2\leq 2y) F Y ( y ) = P ( Y ≤ y ) = P ( X 2 /2 ≤ y ) = P ( X 2 ≤ 2 y )
Hence
F Y ( y ) = 0 F_Y(y)=0 F Y ( y ) = 0 , if y<0
F Y ( y ) = 1 F_Y(y)=1 F Y ( y ) = 1 , for 2y>1 (i.e. y>1/2)
F Y ( y ) = F X ( 2 y ) − F X ( − 2 y ) = F_Y(y)=F_X(\sqrt{2y})-F_X(-\sqrt{2y})= F Y ( y ) = F X ( 2 y ) − F X ( − 2 y ) =
( 1 + ( 2 y ) 3 ) / 2 − ( 1 − ( 2 y ) 3 ) / 2 = 2 2 y 3 / 2 (1+(\sqrt{2y})^3)/2-(1-(\sqrt{2y})^3)/2=2\sqrt{2}y^{3/2} ( 1 + ( 2 y ) 3 ) /2 − ( 1 − ( 2 y ) 3 ) /2 = 2 2 y 3/2
Then
f Y ( y ) = d d y F Y ( y ) f_Y(y)=\frac{d}{dy}F_Y(y) f Y ( y ) = d y d F Y ( y )
f Y ( y ) = 3 2 y 1 / 2 f_Y(y)=3\sqrt{2}y^{1/2} f Y ( y ) = 3 2 y 1/2 for y ∈ [ 0 , 1 / 2 ] y\in[0,1/2] y ∈ [ 0 , 1/2 ] and f Y ( y ) = 0 f_Y(y)=0 f Y ( y ) = 0 elsewhere.
Expectation of Y Y Y is E ( Y ) = ∫ 0 1 / 2 y f Y ( y ) d y = ∫ 0 1 / 2 3 2 y 3 / 2 d y = E(Y)=\int\limits_0^{1/2} yf_Y(y)dy=\int\limits_0^{1/2}3\sqrt{2}y^{3/2}dy= E ( Y ) = 0 ∫ 1/2 y f Y ( y ) d y = 0 ∫ 1/2 3 2 y 3/2 d y =
= 3 2 ⋅ 2 5 y 5 / 2 ∣ 0 1 / 2 = 3 / 10 = 0.3 =3\sqrt{2}\cdot \frac{2}{5}y^{5/2}|_0^{1/2}=3/10=0.3 = 3 2 ⋅ 5 2 y 5/2 ∣ 0 1/2 = 3/10 = 0.3
Expectation of Y 2 Y^2 Y 2 is E ( Y 2 ) = ∫ 0 1 / 2 y 2 f Y ( y ) d y = ∫ 0 1 / 2 3 2 y 5 / 2 d y = E(Y^2)=\int\limits_0^{1/2} y^2f_Y(y)dy=\int\limits_0^{1/2}3\sqrt{2}y^{5/2}dy= E ( Y 2 ) = 0 ∫ 1/2 y 2 f Y ( y ) d y = 0 ∫ 1/2 3 2 y 5/2 d y =
= 3 2 ⋅ 2 7 y 7 / 2 ∣ 0 1 / 2 = 3 / 28 =3\sqrt{2}\cdot \frac{2}{7}y^{7/2}|_0^{1/2}=3/28 = 3 2 ⋅ 7 2 y 7/2 ∣ 0 1/2 = 3/28
Variance of Y Y Y is V ( Y ) = E ( Y 2 ) − E ( Y ) 2 = 3 / 28 − 9 / 100 = 3 / 175 V(Y)=E(Y^2)-E(Y)^2=3/28-9/100=3/175 V ( Y ) = E ( Y 2 ) − E ( Y ) 2 = 3/28 − 9/100 = 3/175
Standard deviation is σ Y = V ( Y ) = 3 / 175 = 21 / 35 = 0.131 \sigma_Y=\sqrt{V(Y)}=\sqrt{3/175}=\sqrt{21}/35=0.131 σ Y = V ( Y ) = 3/175 = 21 /35 = 0.131
Answer . E ( Y ) = 0.3 E(Y)=0.3 E ( Y ) = 0.3 ; σ Y = 0.131 \sigma_Y=0.131 σ Y = 0.131 .
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