Answer:
a) 

![P(100\leq X \leq 200) = [1- e^{-0.01342*200}]-[1- e^{-0.01342*100}] =0.1930](https://tex.z-dn.net/?f=%20P%28100%5Cleq%20X%20%5Cleq%20200%29%20%3D%20%5B1-%20e%5E%7B-0.01342%2A200%7D%5D-%5B1-%20e%5E%7B-0.01342%2A100%7D%5D%20%3D0.1930)
b) ![P(X>223.547) = 1-P(X\leq 223.547) = 1-[1- e^{-0.01342*223.547}]=0.0498](https://tex.z-dn.net/?f=%20P%28X%3E223.547%29%20%3D%201-P%28X%5Cleq%20223.547%29%20%3D%201-%5B1-%20e%5E%7B-0.01342%2A223.547%7D%5D%3D0.0498)
c) 
d) 
Step-by-step explanation:
Previous concepts
The exponential distribution is "the probability distribution of the time between events in a Poisson process (a process in which events occur continuously and independently at a constant average rate). It is a particular case of the gamma distribution". The probability density function is given by:
Solution to the problem
For this case we have that X is represented by the following distribution:

Is important to remember that th cumulative distribution for X is given by:

Part a
For this case we want this probability:

And using the cumulative distribution function we have this:


![P(100\leq X \leq 200) = [1- e^{-0.01342*200}]-[1- e^{-0.01342*100}] =0.1930](https://tex.z-dn.net/?f=%20P%28100%5Cleq%20X%20%5Cleq%20200%29%20%3D%20%5B1-%20e%5E%7B-0.01342%2A200%7D%5D-%5B1-%20e%5E%7B-0.01342%2A100%7D%5D%20%3D0.1930)
Part b
Since we want the probability that the man exceeds the mean by more than 2 deviations
For this case the mean is given by:

And by properties the deviation is the same value 
So then 2 deviations correspond to 2*74.516=149.03
And the want this probability:

And we can find this probability using the complement rule:
![P(X>223.547) = 1-P(X\leq 223.547) = 1-[1- e^{-0.01342*223.547}]=0.0498](https://tex.z-dn.net/?f=%20P%28X%3E223.547%29%20%3D%201-P%28X%5Cleq%20223.547%29%20%3D%201-%5B1-%20e%5E%7B-0.01342%2A223.547%7D%5D%3D0.0498)
Part c
For the median we need to find a value of m such that:

If we use the cumulative distribution function we got:

And if we solve for m we got this:

If we apply natural log on both sides we got:


Part d
For this case we have this equation:

If we apply the cumulative distribution function we got:

If w solve for a we can do this:

Using natural log on btoh sides we got:

