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 | 445.0 770.9 489.3 642.0 531.0 789.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 144 or 1176.
. di 789-531
258
. di 258*1.5
387
. di 789+387
1176
. di 531-387
144
There are
43 outliers in full dataset, in total.
. count if (merit >= 1176 | merit <= 144) & merit != .
43
Those days are:
. list id merit date if (merit >= 1176 | merit <= 144) & 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 |
|-------------------------|
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 |
|-------------------------|
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 |
426. | 428 1249 27mar2019 |
+-------------------------+
Only one of them occured in 2019, on 27/3/2019, at 1249 merits circulated in total.
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 | 421.0 681.5 242.5 628.0 526.0 761.0 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 174 or 1114.
. di 761-526
235
. di 235*1.5
352.5
. di 761+352.5
1113.5
. di 526-352.5
173.5
There are
26 outliers in total, only
three of them occured in 2019, on 09/1/2019, 14/01/2019, and 27/3/2019, at 1161, 1127, and 1249, respectively.
. count if (merit >= 1114 | merit <=174) & merit != .
26
. list id merit date if (merit >= 1114 | merit <= 174) & 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 |
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 |
+-------------------------+