a. Let [tex]F_X(x)[/tex] be the CDF of [tex]X[/tex]. The CDF of [tex]Y[/tex] is
[tex]F_Y(y)=P(Y\le y)=P(5X+2\le y)=P\left(X\le\dfrac{y-2}5\right)=F_X\left(\dfrac{y-2}5\right)[/tex]
which is to say, [tex]Y[/tex] is also normally distributed, but with different parameters. In particular,
[tex]E[Y]=E[5X+2]=5E[X]+2=17[/tex]
[tex]\mathrm{Var}[Y]=\mathrm{Var}[5X+2]=5^2\mathrm{Var}[X]=100[/tex]
b. Using the appropriate CDFs, we have
[tex]P(Y<10)=F_Y(10)=F_X\left(\dfrac{10-2}5\right)=F_X(1.6)\approx0.242[/tex]
[tex]P(X<10)=F_X(10)\approx0.9998[/tex]
c. The 99th percentile for any distribution [tex]D[/tex] is the value of [tex]d_{0.99}[/tex] such that [tex]P(D\le d_{0.99})=0.99[/tex], i.e. all values of [tex]d[/tex] below [tex]d_{0.99}[/tex] make up the lower 99% of the distribution.
We have
[tex]P(Y\le y_{0.99})=0.99\implies y_{0.99}\approx40.26[/tex]
d. On the other hand, the 99th percentile for [tex]X[/tex] is
[tex]P(X\le x_{0.99})=0.99\implies x_{0.99}\approx7.653[/tex]
e. We have
[tex]F_W(w)=P(W\le w)=P\left(e^Y\le w\right)=P(Y\le\ln w)=F_Y(\ln w)[/tex]
which suggests that [tex]\ln W[/tex] is normally distributed, or [tex]W[/tex] is log-normally distributed. Recall that the moment-generating function for [tex]Y[/tex] is
[tex]M_Y(t)=\exp\left(17t+\dfrac{100t^2}2\right)[/tex]
But we also have
[tex]M_Y(t)=E[e^{tY}]=E[e^{t\ln W}]=E[W^t][/tex]
Then
[tex]E[W]=M_Y(1)=e^{67}[/tex]
and
[tex]E[W^2]=M_Y(2)=e^{234}\implies\mathrm{Var}[W]=E[W^2]-E[W]^2=e^{234}-e^{134}[/tex]