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Question: A Monte Carlo Simulation. Implement the process of converting historical data into the probabilit...

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A Monte Carlo Simulation.

Implement the process of converting historical data into the probability of occurrence of the various outcomes, extracting probability distributions, assigning random number intervals, and running a simulation. Assume we are dealing with bacteria counts in Escambia bay. Our data might be like the following for 100 days of observation:

100 7 0-2000/ml: 15 2000-4000/ml: 25 4000-8000/ml: 20 8000-12000/ml: 15 12000-18000/ml: 10 18000-24000/ml: 10

24000-28000/ml: 5

The goal will be to read the data, compute the probability distribution, carry out a simulation, and predict the expected bacterial reading. Data will be in a file entitled readings.txt. The file readings.txt will first have an integer indicating how many days of data are included (100 in the above example) followed by the number of categories of data (7 in the above example). This second integer will be followed by the textual description of the range of values, a colon (:), and then the number of days on which that observation occurred. This part of the program should read these values, simulate a user-specified number of days (see below), and compute and display the expected value from the analytical model.

Inputs and Outputs When your program runs, it will first automatically generate the data sets for part 1, carry out the simulation, and report results. Then, it will read the readings.txt for part 2. It will prompt the user for the number of days to simulate and then perform the simulation. It will also calculate and report the expected value of the analytical model. ALL results will go to the console. Note that different data (categories and percents) in readings.txt should work properly.

program must be in c