Respuesta :
Answer:
[tex]F(x,\lambda) = 1-e^{2x}[/tex] for [tex]x\geq0[/tex]
and [tex]F(x,\lambda) = 0[/tex] for [tex]x <0[/tex]
Step-by-step explanation:
Remember that the CDF of an exponental random variable is given by
[tex]F(x,\lambda) = \left \{ {{1-e^{\lambda x} \ \ \ \ \ x\geq 0} \atop {0} \ \ \ \ \ \ \ \ \ \ \ x<0} \right.[/tex]
where [tex]\lambda[/tex] is the parameter. In this case [tex]\lambda = 2[/tex] so, for this case
[tex]F(x,\lambda) = \left \{ {{1-e^{2x} \ \ \ \ \ x\geq 0} \atop {0} \ \ \ \ \ \ \ \ \ \ \ x<0} \right.[/tex]
Part(a) For [tex]x\leq 0,F_{x}\left ( x \right )=0[/tex]
Part(b) For [tex]x > 0,F_{x}\left ( x \right )=1-e^{-2x}[/tex]
Probability density function:
The probability density function (PDF) is used to define the random variable’s probability coming within a distinct range of values, as opposed to taking on anyone's value.
Part(a):
Given that,
[tex]x\sim Exp\left ( \lambda =2 \right )[/tex]
[tex]\therefore[/tex]The PDF of [tex]x[/tex] is
[tex]\Rightarrow f_{x}\left ( x \right )=\left\{\begin{matrix}\lambda e^{-\lambda x}=2.e^{-2x} \ ;for,x > 0 & \\ 0 \ ;otherwise & \end{matrix}\right.[/tex]
Define, [tex]F_{x}\left ( x \right )=p\left ( x\leq x \right )=cdf \ of \ x[/tex]
[tex]\Rightarrow[/tex]So, when [tex]x\leq 0;[/tex]we have,
[tex]F_{x}\left ( x \right )=p\left ( x\leq x \right )=0[/tex]
Part(b):
when [tex]x > 0;[/tex] we have,
[tex]x\leq 0,F_{x}\left ( x \right )=0 \\ x > 0,F_{x}\left ( x \right )=1-e^{-2x}x\sim Exp\left ( \lambda =2 \right ) \\ \Rightarrow f_{x}\left ( x \right )=\left\{\begin{matrix}\lambda e^{-\lambda x}=2.e^{-2x} \ ;for,x > 0 & \\ 0 \ ;otherwise & \end{matrix}\right. \\ F_{x}\left ( x \right )=p\left ( x\leq x \right )=cdf \ of \ x \\ F_{x}\left ( x \right )=p\left ( x\leq x \right )=0 \\ \x > 0;[/tex]
[tex]F_{x}\left ( x \right )=p\left ( x\leq x \right ) \\ =\int_{0}^{x}d_{x}\left ( t \right )dt \\ =\int_{0}^{x}2e^{-2t}dt\\=(1-e^{-2x})[/tex]
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