written 7.8 years ago by
teamques10
★ 68k
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modified 7.8 years ago
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P(X<2,Y>0.2)=15$∫_0^2 {e^{-3x}} dx$. $∫_{0.2}^∞ {e^{-5y}} dy$
=15$({e^{-3x}/(-3))_0^2}. {e^{-5y}/(-5))_{0.2}^∞}$ = 15 ${1-e^{-6} )}{e^{-1} }$/15 =0.367
$f_X$ (x)=$∫_0^∞ 15{e^{-3x-3y}} dy$
=15 ${e^{-3x}} ({e^{-5y}/(-5))_0^∞}$ = 3${e^{-3x}}$ x>0
$f_X$ (x) = 3$e^{-3x}$ x>0
=0 otherwise
$f_Y$ (y)=$∫_0^∞ 15{e^{-3x-3y}} dx$
=15$e^{-5y} ({e^{-3x}/(-3))_0^∞}$= 5${e^{-5y}}$ y>0
$f_Y$ (y) = 5$e^{-5y} y\gt0
=0 otherwise
Now X, Y are independent if $f_{XY}$ (x,y)=$f_X$ (x).$f_Y$ (y)
Since$ f_X$ (x).$f_Y$ (y)=$(3.e^{-3x} )(5{e^{-3y}} )$ = 15$e^{-3x-3y}$
=$f_{XY}$ (x,y)
Hence X and Y are independent.
Since (X,Y) are independent, the conditional probability density functions are equal to marginal probability density function
∴$f_{X/Y}$ (x/y) = $f_X$ (x)
=3$e^{-3x}$, x\gt0
∴$f_{Y/X}$ (y/x) = $f_Y$ (y)
=3$e^{-3y}$, y\gt0
∴E(X/Y=x)=$∫_0^∞ x. $$f_{X/Y} (x/y)dx$
=$∫_0^∞ x. {f_X} (x) dx$
=$∫_0^∞ x. 3{e^{-3x}} dx$
=$[3x(e^{-3x}/(-3))_0^∞ . -3({e^{-3x}/9)}_0^∞ ]$ =1/3
Similarly
∴$f_{Y/X} (y/x)$ = $f_Y$ (y)
=5$e^{-3y}$, y\gt0
∴E(Y/X=y)=$∫_0^∞ y. {f_{Y/X}} (y/x)dy$
=$∫_0^∞ y.{f_Y} (y) dy$
=$∫_0^∞ y.3{e^{-3y}} dy$
=$[3y(e^{-3y}/(-3))_0^∞ -3({e^{-3y}/9})_0^∞ ]$=1/3