Update on intra-week merits (from 24/1/2018 to 08/7/2019)Converted dataset:. list merit week
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
9. | 6941 2018w12 |
10. | 6707 2018w13 |
|-----------------|
11. | 6415 2018w14 |
12. | 5487 2018w15 |
13. | 4631 2018w16 |
14. | 4585 2018w17 |
15. | 4953 2018w18 |
|-----------------|
16. | 4753 2018w19 |
17. | 4346 2018w20 |
18. | 3854 2018w21 |
19. | 4183 2018w22 |
20. | 4527 2018w23 |
|-----------------|
21. | 3818 2018w24 |
22. | 4921 2018w25 |
23. | 4457 2018w26 |
24. | 4253 2018w27 |
25. | 4239 2018w28 |
|-----------------|
26. | 4159 2018w29 |
27. | 3652 2018w30 |
28. | 3798 2018w31 |
29. | 3994 2018w32 |
30. | 3618 2018w33 |
|-----------------|
31. | 3789 2018w34 |
32. | 3065 2018w35 |
33. | 3574 2018w36 |
34. | 5630 2018w37 |
35. | 7825 2018w38 |
|-----------------|
36. | 4388 2018w39 |
37. | 4271 2018w40 |
38. | 3800 2018w41 |
39. | 4821 2018w42 |
40. | 3945 2018w43 |
|-----------------|
41. | 3339 2018w44 |
42. | 4513 2018w45 |
43. | 3722 2018w46 |
44. | 4558 2018w47 |
45. | 3750 2018w48 |
|-----------------|
46. | 3560 2018w49 |
47. | 3782 2018w50 |
48. | 3753 2018w51 |
49. | 3278 2018w52 |
50. | 4793 2019w1 |
|-----------------|
51. | 6624 2019w2 |
52. | 5306 2019w3 |
53. | 4659 2019w4 |
54. | 4474 2019w5 |
55. | 4318 2019w6 |
|-----------------|
56. | 4207 2019w7 |
57. | 4507 2019w8 |
58. | 4625 2019w9 |
59. | 4901 2019w10 |
60. | 4318 2019w11 |
|-----------------|
61. | 4598 2019w12 |
62. | 6120 2019w13 |
63. | 4418 2019w14 |
64. | 5259 2019w15 |
65. | 4680 2019w16 |
|-----------------|
66. | 4450 2019w17 |
67. | 4756 2019w18 |
68. | 5434 2019w19 |
69. | 5202 2019w20 |
70. | 4571 2019w21 |
|-----------------|
71. | 4336 2019w22 |
72. | 4678 2019w23 |
73. | 5294 2019w24 |
74. | 4710 2019w25 |
75. | 4356 2019w26 |
|-----------------|
76. | 4214 2019w27 |
+-----------------+
- 50% of observed weeks ( 76 weeks) have total intra-week merits above 4543, whilst the rest 50% of them have total intra-week merits below 4543. 4543 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 4171 to 5277 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3065 - 30949. . tabstat merit, s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 76 5520.013 3820.111 4542.5 4171 5276.5 3065 30949
----------------------------------------------------------------------------------------------
. di 5276.5-4171
1105.5
. di 1105.5*1.5
1658.25
. di 5276.5+1658.25
6934.75
. di 4171-1658.25
2512.75
It means that potential outliers are weeks that have intra-week merits beyond 2513 or 6935. How many weeks are potential outliers? . count if (merit >= 6935 | merit < 2513) & merit != .
10
weeks are outliers, in total. List of those ten weeks: . list merit week if merit >=6935 | merit <= 2513
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
9. | 6941 2018w12 |
35. | 7825 2018w38 |
+-----------------+
All of them occured in the year 2018. 
|
|
|
|
Medians and means of intra-day merits over days of weeks.Colors: - Green: highest.- Red: Lowest.- In median, the highest days are Monday, Wednesday, and Thursday at 683, 665, and 663, respectively; whislt the lowest days are Friday, Sunday, and Saturday at 576, 613, and 616, respectively. - In means, the highest days are Monday, Wednesday, and Tuesday, at 743, 714, and 704, respectively; whilst the lowest days are Friday, Saturday, and Thursday, at 609, 627, and 683, respectively. - Monday has still been the highest day in terms of median and mean of intra-day merits over weeks, in contrast Friday is the lowest days in terms of median, and mean of intra-day merits over weeks. Calendar day is in GMT time.To take away all doubt: the first Merit was this one: 1516831941 1 2818066.msg28853325 35 877396 Use EpochConverter to convert 1516831941 (Unix Time) to GMT: Wednesday 24 January 2018 22:12:21. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f) by(dofw)
Summary for variables: merit
by categories of: dofw
dofw | N mean sd p50 p25 p75 min max
----------+--------------------------------------------------------------------------------
Sunday | 72.0 683.8 290.2 612.5 513.5 785.5 389.0 2463.0
Monday | 73.0 743.0 265.8 683.0 576.0 808.0 312.0 1862.0
Tuesday | 72.0 703.1 206.5 649.5 593.5 759.0 383.0 1326.0
Wednesday | 72.0 713.3 203.9 665.0 565.5 764.5 435.0 1268.0
Thursday | 72.0 683.0 203.1 662.5 529.0 779.0 347.0 1333.0
Friday | 72.0 608.7 197.5 575.5 503.5 662.0 348.0 1696.0
Saturday | 72.0 626.8 202.1 616.0 482.0 691.5 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 505.0 680.4 229.8 637.0 531.0 760.0 312.0 2463.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
|
|
|
|
During the period from 24/1/2018 to 08/7/2019, the minimum and maximum of intra-day merits are 312 and 13018 , on 24/12/2018 and 24/1/2018, respectively. List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 |
2. | 6761 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 |
3. | 4493 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 |
4. | 4192 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 |
5. | 3799 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 |
|-------------------------------------------------------------------------------|
6. | 3489 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 |
7. | 3188 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 |
8. | 2820 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 |
9. | 2568 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 |
10. | 2545 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 |
|-------------------------------------------------------------------------------|
11. | 2513 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 |
12. | 2463 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 |
13. | 2308 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 |
14. | 2167 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 |
15. | 2141 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2141 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
17. | 2077 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 |
18. | 1991 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 |
19. | 1867 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 |
20. | 1862 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 |
|-------------------------------------------------------------------------------|
21. | 1747 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 |
22. | 1696 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 |
23. | 1608 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 |
24. | 1579 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 |
25. | 1448 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
26. | 1442 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 |
27. | 1411 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 |
28. | 1409 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 |
29. | 1403 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 |
30. | 1382 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 |
|-------------------------------------------------------------------------------|
31. | 1354 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 |
32. | 1333 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 |
33. | 1331 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 |
34. | 1326 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 |
35. | 1322 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 |
|-------------------------------------------------------------------------------|
36. | 1294 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 |
37. | 1289 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 |
38. | 1279 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 |
39. | 1268 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 |
40. | 1266 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
41. | 1249 428 27mar2019 Wednesday 27 3 2019 2019w13 2019m3 |
42. | 1245 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 |
43. | 1233 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 |
44. | 1227 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 |
45. | 1187 504 11jun2019 Tuesday 11 6 2019 2019w24 2019m6 |
|-------------------------------------------------------------------------------|
46. | 1186 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
47. | 1169 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
48. | 1161 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
49. | 1159 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
50. | 1150 475 13may2019 Monday 13 5 2019 2019w19 2019m5 |
|-------------------------------------------------------------------------------|
List of the top 50-lowest days in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 312 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 |
2. | 316 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 |
3. | 325 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 |
4. | 347 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 |
5. | 347 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 |
|-------------------------------------------------------------------------------|
6. | 348 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 |
7. | 370 122 25may2018 Friday 25 5 2018 2018w21 2018m5 |
8. | 376 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 |
9. | 376 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 |
10. | 377 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
11. | 379 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 |
12. | 383 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 |
13. | 385 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 |
14. | 386 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 |
15. | 389 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
16. | 394 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 |
17. | 394 529 06jul2019 Saturday 6 7 2019 2019w27 2019m7 |
18. | 395 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 |
19. | 397 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 |
20. | 399 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 |
|-------------------------------------------------------------------------------|
21. | 402 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 |
22. | 405 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 |
23. | 412 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 |
24. | 412 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
25. | 413 527 04jul2019 Thursday 4 7 2019 2019w27 2019m7 |
|-------------------------------------------------------------------------------|
26. | 415 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
27. | 415 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
28. | 418 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 |
29. | 420 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 |
30. | 421 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 |
|-------------------------------------------------------------------------------|
31. | 422 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 |
32. | 424 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
33. | 424 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
34. | 426 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
35. | 428 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 |
|-------------------------------------------------------------------------------|
36. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
37. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
38. | 432 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
39. | 432 221 01sep2018 Saturday 1 9 2018 2018w35 2018m9 |
40. | 433 282 01nov2018 Thursday 1 11 2018 2018w44 2018m11 |
|-------------------------------------------------------------------------------|
41. | 435 190 01aug2018 Wednesday 1 8 2018 2018w31 2018m8 |
42. | 435 154 26jun2018 Tuesday 26 6 2018 2018w26 2018m6 |
43. | 441 521 28jun2019 Friday 28 6 2019 2019w26 2019m6 |
44. | 444 182 24jul2018 Tuesday 24 7 2018 2018w30 2018m7 |
45. | 445 143 15jun2018 Friday 15 6 2018 2018w24 2018m6 |
|-------------------------------------------------------------------------------|
46. | 450 373 31jan2019 Thursday 31 1 2019 2019w5 2019m1 |
47. | 451 206 17aug2018 Friday 17 8 2018 2018w33 2018m8 |
48. | 454 283 02nov2018 Friday 2 11 2018 2018w44 2018m11 |
49. | 455 167 09jul2018 Monday 9 7 2018 2018w28 2018m7 |
50. | 455 229 09sep2018 Sunday 9 9 2018 2018w36 2018m9 |
|-------------------------------------------------------------------------------|
|
|
|
|
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 | 529.0 755.7 454.1 643.0 536.0 784.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 164 or 1156. . di 784-536
248
. di 248*1.5
372
. di 784+372
1156
. di 536-372
164
There are 47 outliers in full dataset, in total. . count if (merit >= 1156 | merit <= 164) & merit != .
47
Those days are: . list id merit date if (merit >= 1156 | merit <= 164) & 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 | 505.0 680.4 229.8 637.0 531.0 760.0 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 188 or 1104. . di 760-531
229
. di 229*1.5
343.5
. di 760+343.5
1103.5
. di 531-343.5
187.5
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 <=188) & merit != .
29
List of those 29 outliers in truncated dataset . list id merit date if (merit >= 1104 | merit <= 188) & 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 |
+-------------------------+
|
|
|
|
Update:Converted intra-day merits for days in 2019. . list id merit date day month2 year week month dofw if year == 2019
+------------------------------------------------------------------------------+
| id merit date day month2 year week month dofw |
|------------------------------------------------------------------------------|
343. | 343 603 01jan2019 1 1 2019 2019w1 2019m1 Tuesday |
344. | 344 526 02jan2019 2 1 2019 2019w1 2019m1 Wednesday |
345. | 345 394 03jan2019 3 1 2019 2019w1 2019m1 Thursday |
346. | 346 1082 04jan2019 4 1 2019 2019w1 2019m1 Friday |
347. | 347 835 05jan2019 5 1 2019 2019w1 2019m1 Saturday |
|------------------------------------------------------------------------------|
348. | 348 783 06jan2019 6 1 2019 2019w1 2019m1 Sunday |
349. | 349 570 07jan2019 7 1 2019 2019w1 2019m1 Monday |
350. | 350 782 08jan2019 8 1 2019 2019w2 2019m1 Tuesday |
351. | 351 1161 09jan2019 9 1 2019 2019w2 2019m1 Wednesday |
352. | 352 987 10jan2019 10 1 2019 2019w2 2019m1 Thursday |
|------------------------------------------------------------------------------|
353. | 353 878 11jan2019 11 1 2019 2019w2 2019m1 Friday |
354. | 354 711 12jan2019 12 1 2019 2019w2 2019m1 Saturday |
355. | 355 978 13jan2019 13 1 2019 2019w2 2019m1 Sunday |
356. | 356 1127 14jan2019 14 1 2019 2019w2 2019m1 Monday |
357. | 357 813 15jan2019 15 1 2019 2019w3 2019m1 Tuesday |
|------------------------------------------------------------------------------|
358. | 358 880 16jan2019 16 1 2019 2019w3 2019m1 Wednesday |
359. | 359 1018 17jan2019 17 1 2019 2019w3 2019m1 Thursday |
360. | 360 611 18jan2019 18 1 2019 2019w3 2019m1 Friday |
361. | 361 643 19jan2019 19 1 2019 2019w3 2019m1 Saturday |
362. | 362 658 20jan2019 20 1 2019 2019w3 2019m1 Sunday |
|------------------------------------------------------------------------------|
363. | 363 683 21jan2019 21 1 2019 2019w3 2019m1 Monday |
364. | 364 618 22jan2019 22 1 2019 2019w4 2019m1 Tuesday |
365. | 365 735 23jan2019 23 1 2019 2019w4 2019m1 Wednesday |
366. | 366 715 24jan2019 24 1 2019 2019w4 2019m1 Thursday |
367. | 367 615 25jan2019 25 1 2019 2019w4 2019m1 Friday |
|------------------------------------------------------------------------------|
368. | 368 587 26jan2019 26 1 2019 2019w4 2019m1 Saturday |
369. | 369 655 27jan2019 27 1 2019 2019w4 2019m1 Sunday |
370. | 370 734 28jan2019 28 1 2019 2019w4 2019m1 Monday |
371. | 371 612 29jan2019 29 1 2019 2019w5 2019m1 Tuesday |
372. | 372 510 30jan2019 30 1 2019 2019w5 2019m1 Wednesday |
|------------------------------------------------------------------------------|
373. | 373 450 31jan2019 31 1 2019 2019w5 2019m1 Thursday |
374. | 374 595 01feb2019 1 2 2019 2019w5 2019m2 Friday |
375. | 375 940 02feb2019 2 2 2019 2019w5 2019m2 Saturday |
376. | 376 571 03feb2019 3 2 2019 2019w5 2019m2 Sunday |
377. | 377 796 04feb2019 4 2 2019 2019w5 2019m2 Monday |
|------------------------------------------------------------------------------|
378. | 378 776 05feb2019 5 2 2019 2019w6 2019m2 Tuesday |
379. | 379 559 06feb2019 6 2 2019 2019w6 2019m2 Wednesday |
380. | 380 548 07feb2019 7 2 2019 2019w6 2019m2 Thursday |
381. | 381 611 08feb2019 8 2 2019 2019w6 2019m2 Friday |
382. | 382 623 09feb2019 9 2 2019 2019w6 2019m2 Saturday |
|------------------------------------------------------------------------------|
383. | 383 559 10feb2019 10 2 2019 2019w6 2019m2 Sunday |
384. | 384 642 11feb2019 11 2 2019 2019w6 2019m2 Monday |
385. | 385 585 12feb2019 12 2 2019 2019w7 2019m2 Tuesday |
386. | 386 671 13feb2019 13 2 2019 2019w7 2019m2 Wednesday |
387. | 387 649 14feb2019 14 2 2019 2019w7 2019m2 Thursday |
|------------------------------------------------------------------------------|
388. | 388 607 15feb2019 15 2 2019 2019w7 2019m2 Friday |
389. | 389 523 16feb2019 16 2 2019 2019w7 2019m2 Saturday |
390. | 390 607 17feb2019 17 2 2019 2019w7 2019m2 Sunday |
391. | 391 565 18feb2019 18 2 2019 2019w7 2019m2 Monday |
392. | 392 637 19feb2019 19 2 2019 2019w8 2019m2 Tuesday |
|------------------------------------------------------------------------------|
393. | 393 696 20feb2019 20 2 2019 2019w8 2019m2 Wednesday |
394. | 394 504 21feb2019 21 2 2019 2019w8 2019m2 Thursday |
395. | 395 509 22feb2019 22 2 2019 2019w8 2019m2 Friday |
396. | 396 657 23feb2019 23 2 2019 2019w8 2019m2 Saturday |
397. | 397 608 24feb2019 24 2 2019 2019w8 2019m2 Sunday |
|------------------------------------------------------------------------------|
398. | 398 896 25feb2019 25 2 2019 2019w8 2019m2 Monday |
399. | 399 736 26feb2019 26 2 2019 2019w9 2019m2 Tuesday |
400. | 400 553 27feb2019 27 2 2019 2019w9 2019m2 Wednesday |
401. | 401 707 28feb2019 28 2 2019 2019w9 2019m2 Thursday |
402. | 402 508 01mar2019 1 3 2019 2019w9 2019m3 Friday |
|------------------------------------------------------------------------------|
403. | 403 412 02mar2019 2 3 2019 2019w9 2019m3 Saturday |
404. | 404 1001 03mar2019 3 3 2019 2019w9 2019m3 Sunday |
405. | 405 708 04mar2019 4 3 2019 2019w9 2019m3 Monday |
406. | 406 677 05mar2019 5 3 2019 2019w10 2019m3 Tuesday |
407. | 407 787 06mar2019 6 3 2019 2019w10 2019m3 Wednesday |
|------------------------------------------------------------------------------|
408. | 408 711 07mar2019 7 3 2019 2019w10 2019m3 Thursday |
409. | 409 712 08mar2019 8 3 2019 2019w10 2019m3 Friday |
410. | 410 723 09mar2019 9 3 2019 2019w10 2019m3 Saturday |
411. | 411 656 10mar2019 10 3 2019 2019w10 2019m3 Sunday |
412. | 412 635 11mar2019 11 3 2019 2019w10 2019m3 Monday |
|------------------------------------------------------------------------------|
413. | 413 680 12mar2019 12 3 2019 2019w11 2019m3 Tuesday |
414. | 414 687 13mar2019 13 3 2019 2019w11 2019m3 Wednesday |
415. | 415 804 14mar2019 14 3 2019 2019w11 2019m3 Thursday |
416. | 416 580 15mar2019 15 3 2019 2019w11 2019m3 Friday |
417. | 417 482 16mar2019 16 3 2019 2019w11 2019m3 Saturday |
|------------------------------------------------------------------------------|
418. | 418 428 17mar2019 17 3 2019 2019w11 2019m3 Sunday |
419. | 419 657 18mar2019 18 3 2019 2019w11 2019m3 Monday |
420. | 420 758 19mar2019 19 3 2019 2019w12 2019m3 Tuesday |
421. | 421 651 20mar2019 20 3 2019 2019w12 2019m3 Wednesday |
422. | 422 720 21mar2019 21 3 2019 2019w12 2019m3 Thursday |
|------------------------------------------------------------------------------|
423. | 423 674 22mar2019 22 3 2019 2019w12 2019m3 Friday |
424. | 424 625 23mar2019 23 3 2019 2019w12 2019m3 Saturday |
425. | 425 594 24mar2019 24 3 2019 2019w12 2019m3 Sunday |
426. | 426 576 25mar2019 25 3 2019 2019w12 2019m3 Monday |
427. | 427 726 26mar2019 26 3 2019 2019w13 2019m3 Tuesday |
|------------------------------------------------------------------------------|
428. | 428 1249 27mar2019 27 3 2019 2019w13 2019m3 Wednesday |
429. | 429 927 28mar2019 28 3 2019 2019w13 2019m3 Thursday |
430. | 430 725 29mar2019 29 3 2019 2019w13 2019m3 Friday |
431. | 431 655 30mar2019 30 3 2019 2019w13 2019m3 Saturday |
432. | 432 851 31mar2019 31 3 2019 2019w13 2019m3 Sunday |
|------------------------------------------------------------------------------|
433. | 433 987 01apr2019 1 4 2019 2019w13 2019m4 Monday |
434. | 434 700 02apr2019 2 4 2019 2019w14 2019m4 Tuesday |
435. | 435 616 03apr2019 3 4 2019 2019w14 2019m4 Wednesday |
436. | 436 532 04apr2019 4 4 2019 2019w14 2019m4 Thursday |
437. | 437 516 05apr2019 5 4 2019 2019w14 2019m4 Friday |
|------------------------------------------------------------------------------|
438. | 438 618 06apr2019 6 4 2019 2019w14 2019m4 Saturday |
439. | 439 728 07apr2019 7 4 2019 2019w14 2019m4 Sunday |
440. | 440 708 08apr2019 8 4 2019 2019w14 2019m4 Monday |
441. | 441 707 09apr2019 9 4 2019 2019w15 2019m4 Tuesday |
442. | 442 741 10apr2019 10 4 2019 2019w15 2019m4 Wednesday |
|------------------------------------------------------------------------------|
443. | 443 907 11apr2019 11 4 2019 2019w15 2019m4 Thursday |
444. | 444 612 12apr2019 12 4 2019 2019w15 2019m4 Friday |
445. | 445 787 13apr2019 13 4 2019 2019w15 2019m4 Saturday |
446. | 446 768 14apr2019 14 4 2019 2019w15 2019m4 Sunday |
447. | 447 737 15apr2019 15 4 2019 2019w15 2019m4 Monday |
|------------------------------------------------------------------------------|
448. | 448 677 16apr2019 16 4 2019 2019w16 2019m4 Tuesday |
449. | 449 628 17apr2019 17 4 2019 2019w16 2019m4 Wednesday |
450. | 450 784 18apr2019 18 4 2019 2019w16 2019m4 Thursday |
451. | 451 608 19apr2019 19 4 2019 2019w16 2019m4 Friday |
452. | 452 661 20apr2019 20 4 2019 2019w16 2019m4 Saturday |
|------------------------------------------------------------------------------|
453. | 453 776 21apr2019 21 4 2019 2019w16 2019m4 Sunday |
454. | 454 546 22apr2019 22 4 2019 2019w16 2019m4 Monday |
455. | 455 524 23apr2019 23 4 2019 2019w17 2019m4 Tuesday |
456. | 456 534 24apr2019 24 4 2019 2019w17 2019m4 Wednesday |
457. | 457 929 25apr2019 25 4 2019 2019w17 2019m4 Thursday |
|------------------------------------------------------------------------------|
458. | 458 650 26apr2019 26 4 2019 2019w17 2019m4 Friday |
459. | 459 486 27apr2019 27 4 2019 2019w17 2019m4 Saturday |
460. | 460 597 28apr2019 28 4 2019 2019w17 2019m4 Sunday |
461. | 461 730 29apr2019 29 4 2019 2019w17 2019m4 Monday |
462. | 462 623 30apr2019 30 4 2019 2019w18 2019m4 Tuesday |
|------------------------------------------------------------------------------|
463. | 463 588 01may2019 1 5 2019 2019w18 2019m5 Wednesday |
464. | 464 548 02may2019 2 5 2019 2019w18 2019m5 Thursday |
465. | 465 522 03may2019 3 5 2019 2019w18 2019m5 Friday |
466. | 466 918 04may2019 4 5 2019 2019w18 2019m5 Saturday |
467. | 467 863 05may2019 5 5 2019 2019w18 2019m5 Sunday |
|------------------------------------------------------------------------------|
468. | 468 694 06may2019 6 5 2019 2019w18 2019m5 Monday |
469. | 469 723 07may2019 7 5 2019 2019w19 2019m5 Tuesday |
470. | 470 754 08may2019 8 5 2019 2019w19 2019m5 Wednesday |
471. | 471 891 09may2019 9 5 2019 2019w19 2019m5 Thursday |
472. | 472 701 10may2019 10 5 2019 2019w19 2019m5 Friday |
|------------------------------------------------------------------------------|
473. | 473 592 11may2019 11 5 2019 2019w19 2019m5 Saturday |
474. | 474 623 12may2019 12 5 2019 2019w19 2019m5 Sunday |
475. | 475 1150 13may2019 13 5 2019 2019w19 2019m5 Monday |
476. | 476 912 14may2019 14 5 2019 2019w20 2019m5 Tuesday |
477. | 477 844 15may2019 15 5 2019 2019w20 2019m5 Wednesday |
|------------------------------------------------------------------------------|
478. | 478 750 16may2019 16 5 2019 2019w20 2019m5 Thursday |
479. | 479 642 17may2019 17 5 2019 2019w20 2019m5 Friday |
480. | 480 610 18may2019 18 5 2019 2019w20 2019m5 Saturday |
481. | 481 646 19may2019 19 5 2019 2019w20 2019m5 Sunday |
482. | 482 798 20may2019 20 5 2019 2019w20 2019m5 Monday |
|------------------------------------------------------------------------------|
483. | 483 729 21may2019 21 5 2019 2019w21 2019m5 Tuesday |
484. | 484 822 22may2019 22 5 2019 2019w21 2019m5 Wednesday |
485. | 485 672 23may2019 23 5 2019 2019w21 2019m5 Thursday |
486. | 486 626 24may2019 24 5 2019 2019w21 2019m5 Friday |
487. | 487 511 25may2019 25 5 2019 2019w21 2019m5 Saturday |
|------------------------------------------------------------------------------|
488. | 488 551 26may2019 26 5 2019 2019w21 2019m5 Sunday |
489. | 489 660 27may2019 27 5 2019 2019w21 2019m5 Monday |
490. | 490 591 28may2019 28 5 2019 2019w22 2019m5 Tuesday |
491. | 491 727 29may2019 29 5 2019 2019w22 2019m5 Wednesday |
492. | 492 732 30may2019 30 5 2019 2019w22 2019m5 Thursday |
|------------------------------------------------------------------------------|
493. | 493 525 31may2019 31 5 2019 2019w22 2019m5 Friday |
494. | 494 637 01jun2019 1 6 2019 2019w22 2019m6 Saturday |
495. | 495 474 02jun2019 2 6 2019 2019w22 2019m6 Sunday |
496. | 496 650 03jun2019 3 6 2019 2019w22 2019m6 Monday |
497. | 497 673 04jun2019 4 6 2019 2019w23 2019m6 Tuesday |
|------------------------------------------------------------------------------|
498. | 498 487 05jun2019 5 6 2019 2019w23 2019m6 Wednesday |
499. | 499 633 06jun2019 6 6 2019 2019w23 2019m6 Thursday |
500. | 500 586 07jun2019 7 6 2019 2019w23 2019m6 Friday |
501. | 501 993 08jun2019 8 6 2019 2019w23 2019m6 Saturday |
502. | 502 516 09jun2019 9 6 2019 2019w23 2019m6 Sunday |
|------------------------------------------------------------------------------|
503. | 503 790 10jun2019 10 6 2019 2019w23 2019m6 Monday |
504. | 504 1187 11jun2019 11 6 2019 2019w24 2019m6 Tuesday |
505. | 505 791 12jun2019 12 6 2019 2019w24 2019m6 Wednesday |
506. | 506 653 13jun2019 13 6 2019 2019w24 2019m6 Thursday |
507. | 507 533 14jun2019 14 6 2019 2019w24 2019m6 Friday |
|------------------------------------------------------------------------------|
508. | 508 777 15jun2019 15 6 2019 2019w24 2019m6 Saturday |
509. | 509 642 16jun2019 16 6 2019 2019w24 2019m6 Sunday |
510. | 510 711 17jun2019 17 6 2019 2019w24 2019m6 Monday |
511. | 511 658 18jun2019 18 6 2019 2019w25 2019m6 Tuesday |
512. | 512 672 19jun2019 19 6 2019 2019w25 2019m6 Wednesday |
|------------------------------------------------------------------------------|
513. | 513 759 20jun2019 20 6 2019 2019w25 2019m6 Thursday |
514. | 514 617 21jun2019 21 6 2019 2019w25 2019m6 Friday |
515. | 515 544 22jun2019 22 6 2019 2019w25 2019m6 Saturday |
516. | 516 486 23jun2019 23 6 2019 2019w25 2019m6 Sunday |
517. | 517 974 24jun2019 24 6 2019 2019w25 2019m6 Monday |
|------------------------------------------------------------------------------|
518. | 518 842 25jun2019 25 6 2019 2019w26 2019m6 Tuesday |
519. | 519 768 26jun2019 26 6 2019 2019w26 2019m6 Wednesday |
520. | 520 754 27jun2019 27 6 2019 2019w26 2019m6 Thursday |
521. | 521 441 28jun2019 28 6 2019 2019w26 2019m6 Friday |
522. | 522 482 29jun2019 29 6 2019 2019w26 2019m6 Saturday |
|------------------------------------------------------------------------------|
523. | 523 579 30jun2019 30 6 2019 2019w26 2019m6 Sunday |
524. | 524 490 01jul2019 1 7 2019 2019w26 2019m7 Monday |
525. | 525 722 02jul2019 2 7 2019 2019w27 2019m7 Tuesday |
526. | 526 616 03jul2019 3 7 2019 2019w27 2019m7 Wednesday |
527. | 527 413 04jul2019 4 7 2019 2019w27 2019m7 Thursday |
|------------------------------------------------------------------------------|
528. | 528 520 05jul2019 5 7 2019 2019w27 2019m7 Friday |
529. | 529 394 06jul2019 6 7 2019 2019w27 2019m7 Saturday |
530. | 530 688 07jul2019 7 7 2019 2019w27 2019m7 Sunday |
531. | 531 861 08jul2019 8 7 2019 2019w27 2019m7 Monday |
532. | 532 687 09jul2019 9 7 2019 2019w28 2019m7 Tuesday |
|------------------------------------------------------------------------------|
533. | 533 415 10jul2019 10 7 2019 2019w28 2019m7 Wednesday |
+------------------------------------------------------------------------------+
For days in 2018, please get it there
|
|
|
|
nobody does get paid for posting there - it makes me think that people don't shitpost just to get paid, they either do it for fun or simply do it because it's all they know !
Nobody get paid by posting in WO, but I observe the thread sometimes, and saw people get merits for funny posts there. Maybe it is one of reasons why they keep posting non-sense, and funny posts in that thread. |
|
|
|
777 coin campaign observations (since 30/12/2018 to 07/7/2019) 28 weeks in total, so far (only weeks managed by Hhampuz). Converted dataset from the spreadsheet:. list, abb(30)
+-------------------------------------------------------------------------------------------------+
| id week postcount btcpaid day month2 year date week2 month quarter |
|-------------------------------------------------------------------------------------------------|
1. | 1 1 708 .03082 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
2. | 2 2 1049 .04953 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
3. | 3 3 1226 .05641 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
4. | 4 4 1303 .060075 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
5. | 5 5 1348 .06284 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
|-------------------------------------------------------------------------------------------------|
6. | 6 6 1266 .06126 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
7. | 7 7 1263 .06225 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
8. | 8 8 1203 .05849 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
9. | 9 9 1203 .058315 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
10. | 10 10 1210 .056165 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|-------------------------------------------------------------------------------------------------|
11. | 11 11 1016 .05084 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
12. | 12 12 1258 .057785 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
13. | 13 13 1186 .05373 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
14. | 14 14 922 .04392 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
15. | 15 15 1107 .052805 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|-------------------------------------------------------------------------------------------------|
16. | 16 16 1071 .05278 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
17. | 17 17 1120 .05558 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
18. | 18 18 1037 .049555 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
19. | 19 19 910 .045645 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
20. | 20 20 1051 .05085 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|-------------------------------------------------------------------------------------------------|
21. | 21 21 1310 .06036 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
22. | 22 22 1392 .063185 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
23. | 23 23 1313 .058255 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
24. | 24 24 1362 .061228 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
25. | 25 25 1413 .06462 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
|-------------------------------------------------------------------------------------------------|
26. | 26 26 1557 .07048 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
27. | 27 27 1652 .072955 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
28. | 28 28 1418 .063805 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
+-------------------------------------------------------------------------------------------------+
  Statistics:- Postcount: 50% of observed weeks have total postcounts above 1218, whilst 50% of rest weeks have total postcounts below 1218, the median (p50). The mean and standard deviation are 1210 and 202, respectively. Min and max are 708 and 1652, respectively. 50% of total weekly postcounts range from 1061 to 1331 (the interquartile range from p25 to p75). - BTC paid: 50% of observed weeks have total BTC paid above 0.058, whilst 50% of rest weeks have total BTC paid below 0.058, the median (p50). The mean and standard deviation are 0.566 and 0.0084, respectively. Min and max are 0.0308 and 0.073 (the previous all time high is 0.0705), respectively. 50% of total weekly BTC-paid range from 0.0518 and 0.0618 (the interquartile range from p25 to p75). . tabstat postcount btcpaid, s(n mean sd p50 p25 p75 min max) c(s)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
postcount | 28 1209.786 201.4483 1218 1061 1330.5 708 1652
btcpaid | 28 .0565905 .0084335 .05802 .051815 .061755 .03082 .072955
----------------------------------------------------------------------------------------------
List of weeks in descending orders of total postcounts or total BTC paid Postcount:. list postcount week day month2 year date week2 month quarter, abb(30)
+----------------------------------------------------------------------------------+
| postcount week day month2 year date week2 month quarter |
|----------------------------------------------------------------------------------|
1. | 1652 27 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
2. | 1557 26 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
3. | 1418 28 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
4. | 1413 25 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
5. | 1392 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
|----------------------------------------------------------------------------------|
6. | 1362 24 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
7. | 1348 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
8. | 1313 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
9. | 1310 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
10. | 1303 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
|----------------------------------------------------------------------------------|
11. | 1266 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
12. | 1263 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
13. | 1258 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
14. | 1226 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
15. | 1210 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
16. | 1203 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
17. | 1203 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
18. | 1186 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
19. | 1120 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
20. | 1107 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|----------------------------------------------------------------------------------|
21. | 1071 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
22. | 1051 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
23. | 1049 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
24. | 1037 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
25. | 1016 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
26. | 922 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
27. | 910 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
28. | 708 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
+----------------------------------------------------------------------------------+
. list btcpaid week day month2 year date week2 month quarter, abb(30)
+--------------------------------------------------------------------------------+
| btcpaid week day month2 year date week2 month quarter |
|--------------------------------------------------------------------------------|
1. | .072955 27 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
2. | .07048 26 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
3. | .06462 25 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
4. | .063805 28 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
5. | .063185 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
|--------------------------------------------------------------------------------|
6. | .06284 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
7. | .06225 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
8. | .06126 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
9. | .061228 24 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
10. | .06036 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
|--------------------------------------------------------------------------------|
11. | .060075 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
12. | .05849 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
13. | .058315 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
14. | .058255 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
15. | .057785 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
|--------------------------------------------------------------------------------|
16. | .05641 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
17. | .056165 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
18. | .05558 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
19. | .05373 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
20. | .052805 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|--------------------------------------------------------------------------------|
21. | .05278 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
22. | .05085 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
23. | .05084 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
24. | .049555 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
25. | .04953 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
|--------------------------------------------------------------------------------|
26. | .045645 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
27. | .04392 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
28. | .03082 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
+--------------------------------------------------------------------------------+
|
|
|
|
BitVest campaign observations (since 30/12/2018 to 08/7/2019) 28 weeks in total, so far (only weeks managed by Hhampuz). Converted dataset from the spreadsheet:. list, abb(30)
+--------------------------------------------------------------------------------------------------+
| id week postcount btcpaid day month2 year date week2 month quarter |
|--------------------------------------------------------------------------------------------------|
1. | 1 1 1195 .0712025 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
2. | 2 2 1544 .09399 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
3. | 3 3 1666 .09738 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
4. | 4 4 1513 .09014 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
5. | 5 5 1523 .092315 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
|--------------------------------------------------------------------------------------------------|
6. | 6 6 1500 .090766 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
7. | 7 7 1455 .088646 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
8. | 8 8 1418 .085241 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
9. | 9 9 1435 .087748 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
10. | 10 10 1322 .08303 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|--------------------------------------------------------------------------------------------------|
11. | 11 11 1116 .08944 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
12. | 12 12 1663 .093939 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
13. | 13 13 1523 .093524 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
14. | 14 14 1408 .086465 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
15. | 15 15 1510 .091433 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|--------------------------------------------------------------------------------------------------|
16. | 16 16 1391 .087986 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
17. | 17 17 1450 .086992 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
18. | 18 18 1492 .090028 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
19. | 19 19 1291 .0775045 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
20. | 20 20 1447 .084589 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|--------------------------------------------------------------------------------------------------|
21. | 21 21 1551 .091493 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
22. | 22 22 1560 .094425 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
23. | 23 23 1659 .0887885 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
24. | 24 24 1650 .089767 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
25. | 25 25 1618 .0916645 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
|--------------------------------------------------------------------------------------------------|
26. | 26 26 1738 .0968755 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
27. | 27 27 1866 .105072 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
28. | 28 28 1802 .0937155 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
+--------------------------------------------------------------------------------------------------+
  Statistics:- Postcount: 50% of observed weeks have total postcounts above 1512, whilst 50% of rest weeks have total postcounts below 1512, the median (p50). The mean and standard deviation are 1511 and 168, respectively. Min and max are 1116 and 1866, respectively. 50% of total weekly postcounts range from 1426 to 1634 (the interquartile range from p25 to p75). - BTC paid: 50% of observed weeks have total BTC paid above 0.09, whilst 50% of rest weeks have total BTC paid below 0.09, the median (p50). The mean and standard deviation are 0.0898 and 0.0063 respectively. Min and max are 0.0712 and 0.1051, respectively. 50% of total weekly BTC-paid range from 0.087 to 0.0936 (the interquartile range from p25 to p75). . tabstat postcount btcpaid, s(n mean sd p50 p25 p75 min max) c(s)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
postcount | 28 1510.929 167.5855 1511.5 1426.5 1634 1116 1866
btcpaid | 28 .0897914 .0062724 .090084 .08737 .0936198 .0712025 .105072
----------------------------------------------------------------------------------------------
List of weeks in descending orders of total postcounts or total BTC paid Postcount:. list postcount week day month2 year date week2 month quarter, abb(30)
+----------------------------------------------------------------------------------+
| postcount week day month2 year date week2 month quarter |
|----------------------------------------------------------------------------------|
1. | 1866 27 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
2. | 1802 28 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
3. | 1738 26 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
4. | 1666 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
5. | 1663 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
6. | 1659 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
7. | 1650 24 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
8. | 1618 25 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
9. | 1560 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
10. | 1551 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
|----------------------------------------------------------------------------------|
11. | 1544 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
12. | 1523 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
13. | 1523 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
14. | 1513 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
15. | 1510 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|----------------------------------------------------------------------------------|
16. | 1500 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
17. | 1492 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
18. | 1455 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
19. | 1450 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
20. | 1447 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|----------------------------------------------------------------------------------|
21. | 1435 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
22. | 1418 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
23. | 1408 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
24. | 1391 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
25. | 1322 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
26. | 1291 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
27. | 1195 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
28. | 1116 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
+----------------------------------------------------------------------------------+
. list btcpaid week day month2 year date week2 month quarter, abb(30)
+---------------------------------------------------------------------------------+
| btcpaid week day month2 year date week2 month quarter |
|---------------------------------------------------------------------------------|
1. | .105072 27 30 6 2019 30jun2019 2019w26 2019m6 2019q2 |
2. | .09738 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
3. | .0968755 26 23 6 2019 23jun2019 2019w25 2019m6 2019q2 |
4. | .094425 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
5. | .09399 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
|---------------------------------------------------------------------------------|
6. | .093939 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
7. | .0937155 28 7 7 2019 07jul2019 2019w27 2019m7 2019q3 |
8. | .093524 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
9. | .092315 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
10. | .0916645 25 16 6 2019 16jun2019 2019w24 2019m6 2019q2 |
|---------------------------------------------------------------------------------|
11. | .091493 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
12. | .091433 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
13. | .090766 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
14. | .09014 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
15. | .090028 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
|---------------------------------------------------------------------------------|
16. | .089767 24 9 6 2019 09jun2019 2019w23 2019m6 2019q2 |
17. | .08944 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
18. | .0887885 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
19. | .088646 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
20. | .087986 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
|---------------------------------------------------------------------------------|
21. | .087748 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
22. | .086992 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
23. | .086465 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
24. | .085241 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
25. | .084589 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|---------------------------------------------------------------------------------|
26. | .08303 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
27. | .0775045 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
28. | .0712025 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
+---------------------------------------------------------------------------------+
|
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Update:Time series plot of median and interquartile range
Dataset for median, interquartile range of intraday merits. list week median q1 q3 merit
+------------------------------------------+
| week median q1 q3 merit |
|------------------------------------------|
1. | 2018w26 733 609 991 4457 |
2. | 2018w27 715 598 979 4253 |
3. | 2018w28 707 592 963 4239 |
4. | 2018w29 693 589 922 4159 |
5. | 2018w30 684 577 902 3652 |
|------------------------------------------|
6. | 2018w31 682 575 891 3798 |
7. | 2018w32 675 567 880 3994 |
8. | 2018w33 667 559 867 3618 |
9. | 2018w34 652 555 848 3789 |
10. | 2018w35 642 537 844 3065 |
|------------------------------------------|
11. | 2018w36 639 528 838 3574 |
12. | 2018w37 634 528 829 5630 |
13. | 2018w38 641 530 846 7825 |
14. | 2018w39 640 531 839 4388 |
15. | 2018w40 639 528 829 4271 |
|------------------------------------------|
16. | 2018w41 637 528 808 3800 |
17. | 2018w42 639 530 807 4821 |
18. | 2018w43 639 528 801 3945 |
19. | 2018w44 628 521 796 3339 |
20. | 2018w45 630 522 789 4513 |
|------------------------------------------|
21. | 2018w46 626 521 786 3722 |
22. | 2018w47 626.5 521 782 4558 |
23. | 2018w48 626 521 774 3750 |
24. | 2018w49 621.5 517 773 3560 |
25. | 2018w50 619 517 768 3782 |
|------------------------------------------|
26. | 2018w51 618.5 515 766.5 3753 |
27. | 2018w52 616.5 509.5 762.5 3278 |
28. | 2019w1 616 510 766 4793 |
29. | 2019w2 618.5 513 773 6624 |
30. | 2019w3 620 514 774 5306 |
|------------------------------------------|
31. | 2019w4 621.5 516.5 770.5 4659 |
32. | 2019w5 620 516 773 4474 |
33. | 2019w6 619.5 517 768 4318 |
34. | 2019w7 618 519 767 4207 |
35. | 2019w8 618.5 518.5 766.5 4507 |
|------------------------------------------|
36. | 2019w9 619 518 766 4625 |
37. | 2019w10 623 521 764 4901 |
38. | 2019w11 623 521 761 4318 |
39. | 2019w12 625.5 521 759.5 4598 |
40. | 2019w13 626 522 764 6120 |
|------------------------------------------|
41. | 2019w14 626 523 760 4418 |
42. | 2019w15 628 526 761 5259 |
43. | 2019w16 630 528 762.5 4680 |
44. | 2019w17 628 528 761 4450 |
45. | 2019w18 628 528 761 4756 |
|------------------------------------------|
46. | 2019w19 632 530 761 5434 |
47. | 2019w20 636 531 765 5202 |
48. | 2019w21 636 531 764 4571 |
49. | 2019w22 637 531 760 4336 |
50. | 2019w23 636 531 760 4678 |
|------------------------------------------|
51. | 2019w24 638 532 763 5294 |
52. | 2019w25 638 532 761 4710 |
53. | 2019w26 638 531 761 4356 |
List of median, q1, q3 of intra-day merits over weeks, in descending orders of medians.. list week median q1 q3 merit
+------------------------------------------+
| week median q1 q3 merit |
|------------------------------------------|
1. | 2019w1 616 510 766 4793 |
2. | 2018w52 616.5 509.5 762.5 3278 |
3. | 2019w7 618 519 767 4207 |
4. | 2018w51 618.5 515 766.5 3753 |
5. | 2019w8 618.5 518.5 766.5 4507 |
|------------------------------------------|
6. | 2019w2 618.5 513 773 6624 |
7. | 2019w9 619 518 766 4625 |
8. | 2018w50 619 517 768 3782 |
9. | 2019w6 619.5 517 768 4318 |
10. | 2019w5 620 516 773 4474 |
|------------------------------------------|
11. | 2019w3 620 514 774 5306 |
12. | 2018w49 621.5 517 773 3560 |
13. | 2019w4 621.5 516.5 770.5 4659 |
14. | 2019w11 623 521 761 4318 |
15. | 2019w10 623 521 764 4901 |
|------------------------------------------|
16. | 2019w12 625.5 521 759.5 4598 |
17. | 2018w46 626 521 786 3722 |
18. | 2018w48 626 521 774 3750 |
19. | 2019w13 626 522 764 6120 |
20. | 2019w14 626 523 760 4418 |
|------------------------------------------|
21. | 2018w47 626.5 521 782 4558 |
22. | 2019w15 628 526 761 5259 |
23. | 2019w18 628 528 761 4756 |
24. | 2019w17 628 528 761 4450 |
25. | 2018w44 628 521 796 3339 |
|------------------------------------------|
26. | 2019w16 630 528 762.5 4680 |
27. | 2018w45 630 522 789 4513 |
28. | 2019w19 632 530 761 5434 |
29. | 2018w37 634 528 829 5630 |
30. | 2019w21 636 531 764 4571 |
|------------------------------------------|
31. | 2019w20 636 531 765 5202 |
32. | 2019w23 636 531 760 4678 |
33. | 2018w41 637 528 808 3800 |
34. | 2019w22 637 531 760 4336 |
35. | 2019w24 638 532 763 5294 |
|------------------------------------------|
36. | 2019w25 638 532 761 4710 |
37. | 2019w26 638 531 761 4356 |
38. | 2018w42 639 530 807 4821 |
39. | 2018w43 639 528 801 3945 |
40. | 2018w40 639 528 829 4271 |
|------------------------------------------|
41. | 2018w36 639 528 838 3574 |
42. | 2018w39 640 531 839 4388 |
43. | 2018w38 641 530 846 7825 |
44. | 2018w35 642 537 844 3065 |
45. | 2018w34 652 555 848 3789 |
|------------------------------------------|
46. | 2018w33 667 559 867 3618 |
47. | 2018w32 675 567 880 3994 |
48. | 2018w31 682 575 891 3798 |
49. | 2018w30 684 577 902 3652 |
50. | 2018w29 693 589 922 4159 |
|------------------------------------------|
51. | 2018w28 707 592 963 4239 |
52. | 2018w27 715 598 979 4253 |
53. | 2018w26 733 609 991 4457 |
- From LoyceV's weekly data dumps. - From my converted datasets in the topic: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)
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ABSTRACT
Intra-day merits:
Notes:
- The part of the asbstract describes figures of intraday merits over the period from 19/2/2018 to 01/7/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 01/7/2019 truncated as well due to incomplete week (the 2019w27);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 186 or 1106;
(2) Median of intraday merits over the period is 638;
(3) 50% of observed days have their intra-day merits range from 531 to 761 (the interquartile range);
(4) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 580, and 610, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of median and mean, at 681, and 742.
(6) There are 29 potential outliers in total, and there is only five potential outlier days happened in early weeks of 2019, on 09/01/2019, 14/01/2019, 27/3/2019, 13/5/2019, and 11/6/2019, at 1161, 1127, 1249, 1150, and 1187, respectively.
(7) Minimum and maximum of intraday merits (full dataset) are 312 and 13018, on 11/2/2019 and 24/1/2018, respectively.
Intra-week merits:
Notes:
The part of the abstract use full dataset, only dropped last two days due to incomple week (2019w27).
(1) The median of intra-week merits is 4558;
(2) 50% of observed weeks (75 weeeks in total), have total merits in the range from 4159 to 5294 (the interquaritle range of intra-week merits).
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Nine potential outliers [beyond 2457 or 6997], all of them occurred in the year 2018.
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Update on intra-week merits (from 24/1/2018 to 01/7/2019)Converted dataset:. list merit week
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
9. | 6941 2018w12 |
10. | 6707 2018w13 |
|-----------------|
11. | 6415 2018w14 |
12. | 5487 2018w15 |
13. | 4631 2018w16 |
14. | 4585 2018w17 |
15. | 4953 2018w18 |
|-----------------|
16. | 4753 2018w19 |
17. | 4346 2018w20 |
18. | 3854 2018w21 |
19. | 4183 2018w22 |
20. | 4527 2018w23 |
|-----------------|
21. | 3818 2018w24 |
22. | 4921 2018w25 |
23. | 4457 2018w26 |
24. | 4253 2018w27 |
25. | 4239 2018w28 |
|-----------------|
26. | 4159 2018w29 |
27. | 3652 2018w30 |
28. | 3798 2018w31 |
29. | 3994 2018w32 |
30. | 3618 2018w33 |
|-----------------|
31. | 3789 2018w34 |
32. | 3065 2018w35 |
33. | 3574 2018w36 |
34. | 5630 2018w37 |
35. | 7825 2018w38 |
|-----------------|
36. | 4388 2018w39 |
37. | 4271 2018w40 |
38. | 3800 2018w41 |
39. | 4821 2018w42 |
40. | 3945 2018w43 |
|-----------------|
41. | 3339 2018w44 |
42. | 4513 2018w45 |
43. | 3722 2018w46 |
44. | 4558 2018w47 |
45. | 3750 2018w48 |
|-----------------|
46. | 3560 2018w49 |
47. | 3782 2018w50 |
48. | 3753 2018w51 |
49. | 3278 2018w52 |
50. | 4793 2019w1 |
|-----------------|
51. | 6624 2019w2 |
52. | 5306 2019w3 |
53. | 4659 2019w4 |
54. | 4474 2019w5 |
55. | 4318 2019w6 |
|-----------------|
56. | 4207 2019w7 |
57. | 4507 2019w8 |
58. | 4625 2019w9 |
59. | 4901 2019w10 |
60. | 4318 2019w11 |
|-----------------|
61. | 4598 2019w12 |
62. | 6120 2019w13 |
63. | 4418 2019w14 |
64. | 5259 2019w15 |
65. | 4680 2019w16 |
|-----------------|
66. | 4450 2019w17 |
67. | 4756 2019w18 |
68. | 5434 2019w19 |
69. | 5202 2019w20 |
70. | 4571 2019w21 |
|-----------------|
71. | 4336 2019w22 |
72. | 4678 2019w23 |
73. | 5294 2019w24 |
74. | 4710 2019w25 |
75. | 4356 2019w26 |
+-----------------+
- 50% of observed weeks ( 75 weeks) have total intra-week merits above 4558, whilst the rest 50% of them have total intra-week merits below 4558. 4558is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 4159 to 5294 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3065 - 30949. . tabstat merit, s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max
-------------+--------------------------------------------------------------------------------
merit | 75 5537.427 3842.798 4558 4159 5294 3065 30949
----------------------------------------------------------------------------------------------
. di 5294-4159
1135
. di 1135*1.5
1702.5
. di 5294+1702.5
6996.5
. di 4159-1702.5
2456.5
It means that potential outliers are weeks that have intra-week merits beyond 2457 or 6997. How many weeks are potential outliers? . count if (merit >= 6997 | merit < 2457) & merit != .
9
weeks are outliers, in total. List of those nine weeks: . list merit week if merit >=6997 | merit <= 2457
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
7. | 7253 2018w10 |
8. | 7309 2018w11 |
35. | 7825 2018w38 |
+-----------------+
All of them occured in the year 2018.
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Medians and means of intra-day merits over days of weeks.Colors: - Green: highest.- Red: Lowest.- In median, the highest days are Monday, Thursday, and Wednesday at 681, 672, and 665, respectively; whislt the lowest days are Friday, Sunday, and Saturday at 580, 608, and 618, respectively. - In means, the highest days are Monday, Wednesday, and Tuesday, at 742, 715, and 703, respectively; whilst the lowest days are Friday, Saturday, and Sunday, at 610, 631, and 684, respectively. - Monday has still been the highest day in terms of median and mean of intra-day merits over weeks, in contrast Friday is the lowest days in terms of median, and mean of intra-day merits over weeks. Calendar day is in GMT time.To take away all doubt: the first Merit was this one: 1516831941 1 2818066.msg28853325 35 877396 Use EpochConverter to convert 1516831941 (Unix Time) to GMT: Wednesday 24 January 2018 22:12:21. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f) by(dofw)
Summary for variables: merit
by categories of: dofw
dofw | N mean sd p50 p25 p75 min max
----------+--------------------------------------------------------------------------------
Sunday | 71.0 683.7 292.3 608.0 511.0 788.0 389.0 2463.0
Monday | 72.0 741.4 267.3 680.5 573.0 803.0 312.0 1862.0
Tuesday | 71.0 702.9 208.0 641.0 591.0 760.0 383.0 1326.0
Wednesday | 71.0 714.7 205.0 665.0 562.0 768.0 435.0 1268.0
Thursday | 71.0 686.8 201.9 672.0 530.0 784.0 347.0 1333.0
Friday | 71.0 609.9 198.6 580.0 499.0 674.0 348.0 1696.0
Saturday | 71.0 630.1 201.6 618.0 482.0 695.0 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 498.0 681.5 230.5 637.5 531.0 761.0 312.0 2463.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
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During the period from 24/1/2018 to 01/7/2019, the minimum and maximum of intra-day merits are 312 and 13018 , on 24/12/2018 and 24/1/2018, respectively. List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 |
2. | 6761 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 |
3. | 4493 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 |
4. | 4192 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 |
5. | 3799 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 |
|-------------------------------------------------------------------------------|
6. | 3489 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 |
7. | 3188 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 |
8. | 2820 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 |
9. | 2568 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 |
10. | 2545 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 |
|-------------------------------------------------------------------------------|
11. | 2513 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 |
12. | 2463 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 |
13. | 2308 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 |
14. | 2167 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 |
15. | 2141 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2141 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
17. | 2077 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 |
18. | 1991 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 |
19. | 1867 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 |
20. | 1862 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 |
|-------------------------------------------------------------------------------|
21. | 1747 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 |
22. | 1696 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 |
23. | 1608 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 |
24. | 1579 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 |
25. | 1448 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
26. | 1442 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 |
27. | 1411 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 |
28. | 1409 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 |
29. | 1403 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 |
30. | 1382 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 |
|-------------------------------------------------------------------------------|
31. | 1354 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 |
32. | 1333 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 |
33. | 1331 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 |
34. | 1326 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 |
35. | 1322 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 |
|-------------------------------------------------------------------------------|
36. | 1294 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 |
37. | 1289 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 |
38. | 1279 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 |
39. | 1268 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 |
40. | 1266 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
41. | 1249 428 27mar2019 Wednesday 27 3 2019 2019w13 2019m3 |
42. | 1245 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 |
43. | 1233 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 |
44. | 1227 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 |
45. | 1187 504 11jun2019 Tuesday 11 6 2019 2019w24 2019m6 |
|-------------------------------------------------------------------------------|
46. | 1186 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
47. | 1169 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
48. | 1161 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
49. | 1159 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
50. | 1150 475 13may2019 Monday 13 5 2019 2019w19 2019m5 |
|-------------------------------------------------------------------------------|
List of the top 50-lowest days in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+
| merit id date dofw day month2 year week month |
|-------------------------------------------------------------------------------|
1. | 312 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 |
2. | 316 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 |
3. | 325 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 |
4. | 347 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 |
5. | 347 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
6. | 348 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 |
7. | 370 122 25may2018 Friday 25 5 2018 2018w21 2018m5 |
8. | 376 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 |
9. | 376 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 |
10. | 377 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
11. | 379 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 |
12. | 383 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 |
13. | 385 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 |
14. | 386 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 |
15. | 389 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 |
|-------------------------------------------------------------------------------|
16. | 394 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 |
17. | 395 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 |
18. | 397 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 |
19. | 399 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 |
20. | 402 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 |
|-------------------------------------------------------------------------------|
21. | 405 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 |
22. | 412 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
23. | 412 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 |
24. | 415 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
25. | 415 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
26. | 418 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 |
27. | 420 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 |
28. | 421 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 |
29. | 422 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 |
30. | 424 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
|-------------------------------------------------------------------------------|
31. | 424 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
32. | 426 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
33. | 428 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 |
34. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
35. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
|-------------------------------------------------------------------------------|
36. | 432 221 01sep2018 Saturday 1 9 2018 2018w35 2018m9 |
37. | 432 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
38. | 433 282 01nov2018 Thursday 1 11 2018 2018w44 2018m11 |
39. | 435 190 01aug2018 Wednesday 1 8 2018 2018w31 2018m8 |
40. | 435 154 26jun2018 Tuesday 26 6 2018 2018w26 2018m6 |
|-------------------------------------------------------------------------------|
41. | 441 521 28jun2019 Friday 28 6 2019 2019w26 2019m6 |
42. | 444 182 24jul2018 Tuesday 24 7 2018 2018w30 2018m7 |
43. | 445 143 15jun2018 Friday 15 6 2018 2018w24 2018m6 |
44. | 450 373 31jan2019 Thursday 31 1 2019 2019w5 2019m1 |
45. | 451 206 17aug2018 Friday 17 8 2018 2018w33 2018m8 |
|-------------------------------------------------------------------------------|
46. | 454 283 02nov2018 Friday 2 11 2018 2018w44 2018m11 |
47. | 455 167 09jul2018 Monday 9 7 2018 2018w28 2018m7 |
48. | 455 229 09sep2018 Sunday 9 9 2018 2018w36 2018m9 |
49. | 457 216 27aug2018 Monday 27 8 2018 2018w35 2018m8 |
50. | 458 324 13dec2018 Thursday 13 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
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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 | 522.0 757.7 456.4 643.0 536.0 786.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 161 or 1161.
. di 786-536
250
. di 250*1.5
375
. di 786+375
1161
. di 536-375
161
There are 46 outliers in full dataset, in total. . count if (merit >= 1161 | merit <= 161) & merit != .
46
Those days are: . list id merit date if (merit >= 1161 | merit <= 161) & 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 |
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 | 498.0 681.5 230.5 637.5 531.0 761.0 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 186 or 1106. . di 761-531
230
. di 230*1.5
345
. di 761+345
1106
. di 531-345
186
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 >= 1106 | merit <=186) & merit != .
29
List of those 29 outliers in truncated dataset . list id merit date if (merit >= 1106 | 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 |
+-------------------------+
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