Time-series plots:Full dataset:Truncated dataset:
Basic statistics:Full dataset:. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 536.0 753.4 451.7 643.0 534.0 783.5 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 160 or 1158.
. di 783.5-534
249.5
. di 249.5*1.5
374.25
. di 783.5+374.25
1157.75
. di 534-374.25
159.75
There are
47 outliers in full dataset, in total.
. count if (merit >= 1158 | merit <= 160) & merit != .
47
Those days are:
. list id merit date if (merit >= 1158 | merit <= 160) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 3 4493 26jan2018 |
2. | 4 3489 27jan2018 |
3. | 5 3188 28jan2018 |
4. | 6 3799 29jan2018 |
5. | 7 4192 30jan2018 |
|-------------------------|
6. | 8 2820 31jan2018 |
7. | 9 2545 01feb2018 |
8. | 10 2568 02feb2018 |
9. | 11 1867 03feb2018 |
10. | 12 2167 04feb2018 |
|-------------------------|
11. | 13 2077 05feb2018 |
12. | 14 2308 06feb2018 |
13. | 15 2141 07feb2018 |
14. | 16 2141 08feb2018 |
15. | 17 1448 09feb2018 |
|-------------------------|
16. | 18 1747 10feb2018 |
17. | 19 1442 11feb2018 |
18. | 20 1331 12feb2018 |
19. | 21 1579 13feb2018 |
20. | 22 2513 14feb2018 |
|-------------------------|
21. | 23 1991 15feb2018 |
22. | 24 1411 16feb2018 |
23. | 25 1608 17feb2018 |
24. | 26 1289 18feb2018 |
25. | 27 1403 19feb2018 |
|-------------------------|
26. | 28 1169 20feb2018 |
27. | 29 1266 21feb2018 |
28. | 30 1279 22feb2018 |
30. | 32 1409 24feb2018 |
31. | 33 1186 25feb2018 |
|-------------------------|
32. | 34 1382 26feb2018 |
33. | 35 1326 27feb2018 |
35. | 37 1333 01mar2018 |
36. | 38 1696 02mar2018 |
39. | 41 1245 05mar2018 |
|-------------------------|
46. | 48 1354 12mar2018 |
48. | 50 1159 14mar2018 |
54. | 56 1322 20mar2018 |
55. | 57 1227 21mar2018 |
66. | 68 1233 01apr2018 |
|-------------------------|
234. | 236 2463 16sep2018 |
235. | 237 1862 17sep2018 |
236. | 238 1294 18sep2018 |
237. | 239 1268 19sep2018 |
349. | 351 1161 09jan2019 |
|-------------------------|
426. | 428 1249 27mar2019 |
502. | 504 1187 11jun2019 |
+-------------------------+
Only
three of them occured in 2019, on 09/1/2019, 27/3/2019, and 11/6/2019, at 1161, 1249, and 1187 merits circulated in total, respectively.
Truncated dataset:. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 512.0 679.1 228.9 636.5 530.0 759.5 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond
186 or
1104.
. di 759.5-530
229.5
. di 229.5*1.5
344.25
. di 759.5+344.25
1103.75
. di 530-344.25
185.75
There are
29 outliers in total, only
five of them occured in 2019, on 09/1/2019, 14/01/2019, 27/3/2019, 13/5/2019, and 11/6/2019, at 1161, 1127, 1249, 1150, and 1187,
respectively.
. count if (merit >= 1104 | merit <=186) & merit != .
29
List of those 29 outliers in truncated dataset
. list id merit date if (merit >= 1104 | merit <= 186) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 27 1403 19feb2018 |
2. | 28 1169 20feb2018 |
3. | 29 1266 21feb2018 |
4. | 30 1279 22feb2018 |
6. | 32 1409 24feb2018 |
|-------------------------|
7. | 33 1186 25feb2018 |
8. | 34 1382 26feb2018 |
9. | 35 1326 27feb2018 |
11. | 37 1333 01mar2018 |
12. | 38 1696 02mar2018 |
|-------------------------|
15. | 41 1245 05mar2018 |
17. | 43 1109 07mar2018 |
22. | 48 1354 12mar2018 |
24. | 50 1159 14mar2018 |
25. | 51 1130 15mar2018 |
|-------------------------|
30. | 56 1322 20mar2018 |
31. | 57 1227 21mar2018 |
42. | 68 1233 01apr2018 |
43. | 69 1146 02apr2018 |
127. | 153 1138 25jun2018 |
|-------------------------|
210. | 236 2463 16sep2018 |
211. | 237 1862 17sep2018 |
212. | 238 1294 18sep2018 |
213. | 239 1268 19sep2018 |
325. | 351 1161 09jan2019 |
|-------------------------|
330. | 356 1127 14jan2019 |
402. | 428 1249 27mar2019 |
449. | 475 1150 13may2019 |
478. | 504 1187 11jun2019 |
+-------------------------+