QuickCart, a grocery delivery app, is reviewing order delivery times before building a service-level dashboard. A few extreme delays may be data errors or rare operational incidents, so the analytics team needs a defensible way to identify outliers and decide how to handle them.
You are given a small sample of delivery times (in minutes) from one city on a single day. Identify outliers using the IQR rule, compare the mean before and after treatment, and recommend what to do with the flagged values.
| Order ID | Delivery Time (min) |
|---|---|
| 1 | 24 |
| 2 | 26 |
| 3 | 27 |
| 4 | 28 |
| 5 | 29 |
| 6 | 30 |
| 7 | 31 |
| 8 | 32 |
| 9 | 33 |
| 10 | 34 |
| 11 | 35 |
| 12 | 36 |
| 13 | 37 |
| 14 | 38 |
| 15 | 39 |
| 16 | 41 |
| 17 | 43 |
| 18 | 95 |
| 19 | 110 |
| 20 | 125 |
Assume the data are already sorted.