Answer:
Written in Python:
def var_based_estimator(waitingtimes):
isum = 0
for i in range(0,len(waitingtimes)):
isum = isum + waitingtimes[i]
mean = isum/len(waitingtimes)
varsum=0
for i in range(0,len(waitingtimes)):
varsum = varsum + (waitingtimes[i] - mean)**2
variance = varsum/len(waitingtimes)
print(round(variance,3))
Explanation:
(1) To calculate variance, first we need to determine the mean,
(2) Then we subtract the mean from individual data
(3) Then we take the squares of (2)
(4) Sum the results in (3)
(5) Divide the result in (4) by the mean
This is implemented in python as follows:
This line defines the function
def var_based_estimator(waitingtimes):
We start by calculating the mean, here i.e. (1)
This line initializes the sum of all data to 0
isum = 0
The following iteration adds up the elements of waitingtimes list
for i in range(0,len(waitingtimes)):
isum = isum + waitingtimes[i]
The mean is calculated here; [tex]Mean = \frac{\sum x}{n}[/tex]
mean = isum/len(waitingtimes)
(2), (3) and (4) are implemented in the following iteration
First the sum is initialized to 0
varsum=0
This iterates through the list; subtracts each element from the mean; take the square and sum the results together
for i in range(0,len(waitingtimes)):
varsum = varsum + (waitingtimes[i] - mean)**2
This calculates the variance by dividing the (4) by the mean
variance = varsum/len(waitingtimes)
This prints the variance rounded to three decimal placed
print(round(variance,3))
