Know that in this case, I did select the name of the variable which is in cell B1.

Now, we go to the ASP tab and in the tools group we find fit.

Click on fit and the fit options dialogue appears.

There are two general types of this distribution functions,

continuous and discrete.

In this case we have discrete data and we choose the discrete option.

This is the only option that we're going to change.

We will leave the other options under their full values.

Click the fifth button and the results windows is displayed.

The results window shows the comparison chart.

This chart compares the frequency distribution of the historical data in

blue and the frequency of the proposed distribution function in pink.

The idea is to visually inspect how well the proposed function fits their values.

In this case the analysis is suggesting a Poisson distribution with a mean

value of 150.4 passengers.

Other alternatives are shown in the left panel of the window.

These alternatives are shown in order of fitness.

So, the analysis is suggesting that the Poisson is the best fit,

followed by the negative Binomial.

We're going to keep the Poisson distribution as it

looks like a good representation of the demand.

If we close this window,

we are asked whether we want to accept the fitted distribution.

Say yes,

the ASP then allows you to place the side function somewhere on the spreadsheet.

Place it in cell H4.

We can verify that H4 now contains this signed Poisson function

with an average of 150.4.

For the show up rate the processes are slightly different.

In our simulation we model the show up rate as a Binomial distribution.

The Binomial distribution makes a lot of sense because given the number of trials

and the probability of success in each trial.

The distribution represents the frequency of the number of successes.

The number of trials in our context is the number of booked passengers.

Success means that the passenger shows up for the flight.

Or best estimate of the show up rate is not 92% which we've found by

averaging the historical percentage of passengers showing up.

So we have a an estimate of the probability of success.

We also have the number of trials because the number of booked passengers is known.

That means that we just need to verify that the number

passengers that show up for the flight follow a binomial distribution.

We can use the fit tool to do this.

Select the data on column D and click on fit.