1. Analyzing Pickup and Delivery Times:

Pickup Time: the pickup time does not vary much between the three couriers, averaging around 14 minutes for all the couriers However, Metro performs significantly worse, when it comes to delivery times.

2. Analyzing Mileage and Speed:

Before grading the three couriers, we must make sure that they are traveling the same distances. We can also plot the average speed (in MPH), in order to ‘normalize’ their performance.

We can see that even though Metro is taking more time in deliveries, they are also making much longer trips. When we look at average speed, metro is significantly faster than DEW and Carborne. DEW being significantly slower than the rest, and Carborne being right in the middle of the distribution. We plotted notches in order to make observations on significance; “If the notches of the two plots do not overlap there is ‘strong evidence’ that the two medians defer.” (Chambers et al, 1983).

3. Analyzing Cost:

Even though Metro is significantly faster, that does not mean that it is the best choice. To make that assertion, we have to make observations on the cost of delivery.

We will also normalize cost given Total Delivery Time, as well as Mileage.

We can see that Metro is the cheapest option on a per minute basis. Metro is also cheap per mileage. We can see that DEW is significantly more expensive than Metro and Carborne.

4. Joint Cost and Mileage Analysis:

The last type of analysis we will conduct, will be based on how price changes per mileage. We can see that Metro remains as the cheapest up to 10 miles. From 10-15 miles, the prices of all three are more or less the same, however, Metro becomes the most expensive 15 miles or over.

Given the mean Mileage is 11.89 miles, Metro remains the best option for Cost / Mileage.

print(paste('The mean mileage is:', mean(X.Mileage.)))
## [1] "The mean mileage is: 11.8956043956044"