Outlier Detection for Delivery Times

Business Context

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.

Problem Statement

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.

Given Data

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.

Requirements

Compute $Q_1$ , median, and $Q_3$
Calculate the interquartile range and outlier fences
Identify which observations are outliers
Compute the sample mean and standard deviation using all data
Recompute the mean after removing IQR-based outliers
Recommend whether to remove, cap, or keep the outliers, with justification

Assumptions

Use the median-of-halves method for quartiles
Treat the sample as one operational segment; do not split by courier or neighborhood
Assume extreme values could be either legitimate delays or logging issues, so business context matters before deletion

Business Context

Problem Statement

Given Data

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.

Requirements

Compute $Q_1$ , median, and $Q_3$
Calculate the interquartile range and outlier fences
Identify which observations are outliers
Compute the sample mean and standard deviation using all data
Recompute the mean after removing IQR-based outliers
Recommend whether to remove, cap, or keep the outliers, with justification

Assumptions

Use the median-of-halves method for quartiles
Treat the sample as one operational segment; do not split by courier or neighborhood
Assume extreme values could be either legitimate delays or logging issues, so business context matters before deletion

Business Context

Problem Statement

Given Data

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.

Requirements

Compute $Q_1$ , median, and $Q_3$
Calculate the interquartile range and outlier fences
Identify which observations are outliers
Compute the sample mean and standard deviation using all data
Recompute the mean after removing IQR-based outliers
Recommend whether to remove, cap, or keep the outliers, with justification

Assumptions

Use the median-of-halves method for quartiles
Treat the sample as one operational segment; do not split by courier or neighborhood
Assume extreme values could be either legitimate delays or logging issues, so business context matters before deletion

Business Context

Problem Statement

Given Data

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.

Requirements

Compute $Q_1$ , median, and $Q_3$
Calculate the interquartile range and outlier fences
Identify which observations are outliers
Compute the sample mean and standard deviation using all data
Recompute the mean after removing IQR-based outliers
Recommend whether to remove, cap, or keep the outliers, with justification

Assumptions

Use the median-of-halves method for quartiles
Treat the sample as one operational segment; do not split by courier or neighborhood
Assume extreme values could be either legitimate delays or logging issues, so business context matters before deletion

Interview Guides

Business Context

Problem Statement

Given Data

Requirements

Assumptions

Outlier Detection for Delivery Times

Business Context

Problem Statement

Given Data

Requirements

Assumptions

Your Answer

Outlier Detection for Delivery Times

Business Context

Problem Statement

Given Data

Requirements

Assumptions

Outlier Detection for Delivery Times

Business Context

Problem Statement

Given Data

Requirements

Assumptions

Your Answer

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

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

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

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

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

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

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

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

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

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

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

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