Suppose that W1, W2, and W3 are independent uniform random variables with the following distributions: Wi ~ Uni(0,10*i). What is the standard deviation of W1+W2+W3? Using R, compute this value. Round the answer to 2 decimal places and enter into the box:

Respuesta :

I'll leave the computation via R to you. The [tex]W_i[/tex] are distributed uniformly on the intervals [tex][0,10i][/tex], so that

[tex]f_{W_i}(w)=\begin{cases}\dfrac1{10i}&\text{for }0\le w\le10i\\\\0&\text{otherwise}\end{cases}[/tex]

each with mean/expectation

[tex]E[W_i]=\displaystyle\int_{-\infty}^\infty wf_{W_i}(w)\,\mathrm dw=\int_0^{10i}\frac w{10i}\,\mathrm dw=5i[/tex]

and variance

[tex]\mathrm{Var}[W_i]=E[(W_i-E[W_i])^2]=E[{W_i}^2]-E[W_i]^2[/tex]

We have

[tex]E[{W_i}^2]=\displaystyle\int_{-\infty}^\infty w^2f_{W_i}(w)\,\mathrm dw=\int_0^{10i}\frac{w^2}{10i}\,\mathrm dw=\frac{100i^2}3[/tex]

so that

[tex]\mathrm{Var}[W_i]=\dfrac{25i^2}3[/tex]

Now,

[tex]E[W_1+W_2+W_3]=E[W_1]+E[W_2]+E[W_3]=5+10+15=30[/tex]

and

[tex]\mathrm{Var}[W_1+W_2+W_3]=E\left[\big((W_1+W_2+W_3)-E[W_1+W_2+W_3]\big)^2\right][/tex]

[tex]\mathrm{Var}[W_1+W_2+W_3]=E[(W_1+W_2+W_3)^2]-E[W_1+W_2+W_3]^2[/tex]

We have

[tex](W_1+W_2+W_3)^2={W_1}^2+{W_2}^2+{W_3}^2+2(W_1W_2+W_1W_3+W_2W_3)[/tex]

[tex]E[(W_1+W_2+W_3)^2][/tex]

[tex]=E[{W_1}^2]+E[{W_2}^2]+E[{W_3}^2]+2(E[W_1]E[W_2]+E[W_1]E[W_3]+E[W_2]E[W_3])[/tex]

because [tex]W_i[/tex] and [tex]W_j[/tex] are independent when [tex]i\neq j[/tex], and so

[tex]E[(W_1+W_2+W_3)^2]=\dfrac{100}3+\dfrac{400}3+300+2(50+75+150)=\dfrac{3050}3[/tex]

giving a variance of

[tex]\mathrm{Var}[W_1+W_2+W_3]=\dfrac{3050}3-30^2=\dfrac{350}3[/tex]

and so the standard deviation is [tex]\sqrt{\dfrac{350}3}\approx\boxed{116.67}[/tex]

# # #

A faster way, assuming you know the variance of a linear combination of independent random variables, is to compute

[tex]\mathrm{Var}[W_1+W_2+W_3][/tex]

[tex]=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3]+2(\mathrm{Cov}[W_1,W_2]+\mathrm{Cov}[W_1,W_3]+\mathrm{Cov}[W_2,W_3])[/tex]

and since the [tex]W_i[/tex] are independent, each covariance is 0. Then

[tex]\mathrm{Var}[W_1+W_2+W_3]=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3][/tex]

[tex]\mathrm{Var}[W_1+W_2+W_3]=\dfrac{25}3+\dfrac{100}3+75=\dfrac{350}3[/tex]

and take the square root to get the standard deviation.

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