data: duration in hours
num of groups: 7
group1:
| max of every group |
frequency |
| 190 |
23 |
| 380 |
6 |
| 570 |
2 |
| 760 |
1 |
| 950 |
2 |
| 1140 |
8 |
| 1330 |
4 |
group6
| max of every group |
frequency |
| 120 |
30 |
| 240 |
10 |
| 360 |
8 |
| 480 |
4 |
| 600 |
9 |
| 720 |
1 |
| 840 |
1 |
group8
| max of every group |
frequency |
| 160 |
49 |
| 320 |
10 |
| 480 |
4 |
| 640 |
0 |
| 800 |
1 |
| 960 |
2 |
| 1120 |
2 |
As duration hours goes high, its frequency decreases dramatically. Not a bell curve at all, but a downhill.
My opinion: we get the data, we sort them as ascending order, we find the max value of the data;
boundary = max * 90%, then we check the num of data which is larger that boundary, let's say it's N. Suppose the total num of data is M, we calculate P = N / M. P represents the rate of unusual long issue time.
P >= 5%: bad smell
In this way, P of group1 = 8.3%, P of group6 = 1.6% P of group8 = 2.9%
What's your opinion man?
data: duration in hours
num of groups: 7
group1:
group6
group8
As duration hours goes high, its frequency decreases dramatically. Not a bell curve at all, but a downhill.
My opinion: we get the data, we sort them as ascending order, we find the max value of the data;
boundary = max * 90%, then we check the num of data which is larger that boundary, let's say it's N. Suppose the total num of data is M, we calculate P = N / M. P represents the rate of unusual long issue time.
P >= 5%: bad smell
In this way, P of group1 = 8.3%, P of group6 = 1.6% P of group8 = 2.9%
What's your opinion man?