Answer:
The probability that is raining given that Randy is late to class is P(R|L)=0.175.
Explanation:
We can apply the Bayes theorem to solve this question.
Being
L: Randy is late to class
R: It is raining
nL: Randy is on time to class
nR: It is not raining
We know:
P(L | R)= 1-0.7=0.3
P(L | nR) = 1-0.9=0.1
P(R) = 0.2 (probability of Randy going to class given that is raining)
P(nR) = 0.8
We have to calculate P(R | L): probability that is raining given that Randy is late to class.
If we apply the Bayes theorem, we have:

The probability that is raining given that Randy is late to class is P(R|L)=0.175.