\(P({ X }_{ 1 },{ X }_{ 2 }...{ X }_{ n }<x)=1-P({ { X }_{ 1 },{ X }_{ 2 }...{ X }_{ n }>x)\qquad }\)
\(=1-{ (1-F(x)) }^{ n }\)
\(=1-{ e }^{ -1000x/\lambda }\)
\(f(x)=\frac { d }{ dx } 1-{ e }^{ -1000x/\lambda }\)
\(f(x)=\frac { 1000 }{ \lambda } { e }^{ -1000x/lambda }\)
\(E(x)=\int _{ 0 }^{ \infty }{ \frac { 1000 }{ \lambda } x{ e }^{ -1000x/\lambda } }\)
\(u=x, dv = { e }^{-x/10 }\)
\(E(x)=\quad 1/10(-10x{ e }^{ -x/10 }{ | }_{ 0 }^{ \infty }-\int _{ 0 }^{ \infty }{10 { e }^{ -x/10 } }\)
\(E(x)=(1/10)(100)\)
\(=10\)
bulbs <- rexp(10000000, .001)
dim(bulbs) <- c(100000, 100)
mins <- apply(bulbs, 1, min)
mean(mins)
## [1] 9.936558
a) P(|X - 10| >= 2)
(100/3)/2**2
## [1] 8.333333
p <= 1
b) P(|X - 10| >= 5)
(100/3)/5**2
## [1] 1.333333
p <= 1
c) P(|X - 10| >= 9)
(100/3)/9**2
## [1] 0.4115226
p <= 0.4115226
d) P(|X - 10| >= 20)
(100/3)/20**2
## [1] 0.08333333
p <= 0.0833333