This example calculates the ideal number of patient to schedule per day. It is assumed that the physician's workload is 30 patients per day. It is estimated that 25% of patients fail to show up to their appointments leading to loss of revenue for the physician.

-40.0 is the number of patients that should be scheduled (linear approach) assuming only 75% of patients will show up.

-A binomial distribution model uses historical data to assume 25% of patients do not show up for their appointments. The probability density function (binom.pmf()) is calculated similiar to a Cumulative Density Function a normal distribution. The pmf function creates a vector of pmf values.

-The plot distribution shows a binomial distribution centered at 30.

-The cumulative distribution function calculates that 0.4395 % of the time the the physician will be overbooked given linearPrediction = 40, workload = 30, and prob = 0.75.

-The binomial ppf() function is used to calculate that 34.0 appointments can be scheduled to limit the risk of having more than 30 appointments per day to 5%.