Continuous Random Variable

Let X be a random variable with density function fx(.) such that: fx(s) = ke$^{-s}$ for s > 2 and fx(s) = 0 for s $\le$ 2.

1.) Determine k

2.) Calculate P(0 $\le$ X $\le$ 5).

3.) Calculate E(X)

My attempt:

For 1, I got that $\int_2^\infty k e^{-s}\,ds=2.$ I integrated and got that k = $2e^2$.

For 2, I got that $\int_0^5 k e^{-s}\,ds.$ I integrated and got that $[(2/e^3) -2e^2]$.

For 3, I got that $\int_2^\infty k se^{-s}ds.$ I integrated and got 6.

Is this correct?

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We have $k=e^2$. –  André Nicolas Oct 2 '12 at 3:44
How did you get that? Did I do the limits of integration wrong? –  Q.matin Oct 2 '12 at 3:46
$\int_2^\infty e^{-x}=e^{-2}$. By the way, answer to (2) is $1-e^{-3}$ I think. You need to integrate from $2$ to $5$. –  André Nicolas Oct 2 '12 at 3:48

$1.$ An antiderivative of $e^{-s}$ is $-e^{-s}$, so $$\int_2^\infty ke^{-s}\,ds=ke^{-2}.$$ We want our integral to be $1$, that is always the case for a density. So $k=e^2$.

$2.$ The density function is zero to the left of $2$. So we want $$\int_2^5 e^2 e^{-s}\,ds.$$

$3.$ We want $$\int_2^\infty (s)(e^2e^{-s})\,ds.$$ Now do integration by parts. Note again that the density function is $0$ to the left of $2$, hence the limits. Your answer, which is negative (impossible) seems to have been obtained by integrating from $0$.

Now do integration by parts. It looks as if you got this right, apart from having a $k$, and hence an answer, twice as lage as it should be.

Remark: There is an (in this case) sloghtly easier way to calculate the expectation, that you may not have been taught. If $X$ is non-negative, with *cumulative distribution function $F_X(s)$, then $$E(X)=\int_0^\infty(1-F_X(s))\,ds.$$ In our case, we would really be integrating from $2$ to infinity, since the cdf is $0$ to the left of $2$.

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Thanks again, Andre!! –  Q.matin Oct 2 '12 at 4:16

By definition $$f_X(x) = \cases{ k \exp(-x) & x>2 \\ 0 & x \leqslant 0}$$ The normalization constant is determined by requiring $1 = \int_\mathbb{R}f_X(x) \mathrm{d}x$: $$1 = \int_\mathbb{R}f_X(x) \mathrm{d}x = \int_2^\infty k \exp(-x) \mathrm{d}x \stackrel{x=2+y}{=} k \mathrm{e}^{-2} \underbrace{ \int_0^\infty \exp(-y) \mathrm{d}y}_{ = 1}$$ hence $k = \mathrm{e}^2$. Thus: $$f_X(x) = \cases{ \exp(-(x-2)) & x>2 \\ 0 & x \leqslant 0}$$ that is $X= 2+Y$, where $Y$ is the exponential random variable with unit mean. Thus $$\mathbb{P}\left(0\leqslant X\leqslant5\right) = \mathbb{P}\left(-2\leqslant Y\leqslant3\right) = \mathbb{P}\left(0\leqslant Y\leqslant3\right) = 1 - \underbrace{\mathbb{P}(Y>3)}_{\int_3^\infty \exp(-t) \mathrm{d}t = \exp(-3)} = 1 - \exp(-3)$$ The mean is also easy: $$\mathbb{E}(X) = \mathbb{E}(2+Y) = 2 + \mathbb{E}(Y) = 2 + 1 =3$$

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