Update:Time series plot of median and interquartile range
Dataset for median, interquartile range of intraday merits. list week merit median q3 q1
+------------------------------------------+
| week merit median q3 q1 |
|------------------------------------------|
1. | 2018w26 4457 733 991 609 |
2. | 2018w27 4253 715 979 598 |
3. | 2018w28 4239 707 963 592 |
4. | 2018w29 4159 693 922 589 |
5. | 2018w30 3652 684 902 577 |
|------------------------------------------|
6. | 2018w31 3798 682 891 575 |
7. | 2018w32 3994 675 880 567 |
8. | 2018w33 3618 667 867 559 |
9. | 2018w34 3789 652 848 555 |
10. | 2018w35 3065 642 844 537 |
|------------------------------------------|
11. | 2018w36 3574 639 838 528 |
12. | 2018w37 5630 634 829 528 |
13. | 2018w38 7825 641 846 530 |
14. | 2018w39 4388 640 839 531 |
15. | 2018w40 4271 639 829 528 |
|------------------------------------------|
16. | 2018w41 3800 637 808 528 |
17. | 2018w42 4821 639 807 530 |
18. | 2018w43 3945 639 801 528 |
19. | 2018w44 3339 628 796 521 |
20. | 2018w45 4513 630 789 522 |
|------------------------------------------|
21. | 2018w46 3722 626 786 521 |
22. | 2018w48 3750 626 774 521 |
23. | 2018w49 3560 621.5 773 517 |
24. | 2018w50 3782 619 768 517 |
25. | 2018w51 3753 618.5 766.5 515 |
|------------------------------------------|
26. | 2018w52 3278 616.5 762.5 509.5 |
27. | 2019w1 4793 616 766 510 |
28. | 2019w2 6624 618.5 773 513 |
29. | 2019w3 5306 620 774 514 |
30. | 2019w4 4659 621.5 770.5 516.5 |
|------------------------------------------|
31. | 2019w5 4474 620 773 516 |
32. | 2019w6 4318 619.5 768 517 |
33. | 2019w7 4207 618 767 519 |
34. | 2019w8 4507 618.5 766.5 518.5 |
35. | 2019w9 4625 619 766 518 |
|------------------------------------------|
36. | 2019w10 4901 623 764 521 |
37. | 2019w11 4318 623 761 521 |
38. | 2019w12 4598 625.5 759.5 521 |
39. | 2019w13 6120 626 764 522 |
40. | 2019w14 4418 626 760 523 |
|------------------------------------------|
List of median, q1, q3 of intra-day merits over weeks, in descending orders of medians.. list week merit median q1 q3
+------------------------------------------+
| week merit median q1 q3 |
|------------------------------------------|
1. | 2019w1 4793 616 510 766 |
2. | 2018w52 3278 616.5 509.5 762.5 |
3. | 2019w7 4207 618 519 767 |
4. | 2019w2 6624 618.5 513 773 |
5. | 2018w51 3753 618.5 515 766.5 |
|------------------------------------------|
6. | 2019w8 4507 618.5 518.5 766.5 |
7. | 2019w9 4625 619 518 766 |
8. | 2018w50 3782 619 517 768 |
9. | 2019w6 4318 619.5 517 768 |
10. | 2019w3 5306 620 514 774 |
|------------------------------------------|
11. | 2019w5 4474 620 516 773 |
12. | 2019w4 4659 621.5 516.5 770.5 |
13. | 2018w49 3560 621.5 517 773 |
14. | 2019w11 4318 623 521 761 |
15. | 2019w10 4901 623 521 764 |
|------------------------------------------|
16. | 2019w12 4598 625.5 521 759.5 |
17. | 2019w13 6120 626 522 764 |
18. | 2018w46 3722 626 521 786 |
19. | 2019w14 4418 626 523 760 |
20. | 2018w48 3750 626 521 774 |
|------------------------------------------|
21. | 2018w44 3339 628 521 796 |
22. | 2018w45 4513 630 522 789 |
23. | 2018w37 5630 634 528 829 |
24. | 2018w41 3800 637 528 808 |
25. | 2018w43 3945 639 528 801 |
|------------------------------------------|
26. | 2018w36 3574 639 528 838 |
27. | 2018w42 4821 639 530 807 |
28. | 2018w40 4271 639 528 829 |
29. | 2018w39 4388 640 531 839 |
30. | 2018w38 7825 641 530 846 |
|------------------------------------------|
31. | 2018w35 3065 642 537 844 |
32. | 2018w34 3789 652 555 848 |
33. | 2018w33 3618 667 559 867 |
34. | 2018w32 3994 675 567 880 |
35. | 2018w31 3798 682 575 891 |
|------------------------------------------|
36. | 2018w30 3652 684 577 902 |
37. | 2018w29 4159 693 589 922 |
38. | 2018w28 4239 707 592 963 |
39. | 2018w27 4253 715 598 979 |
40. | 2018w26 4457 733 609 991 |
|------------------------------------------|
- 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 08/4/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 08/4/2019 truncated as well due to incomplete week (the 2019w15);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 168 or 1116;
(2) Median of intraday merits over the period is 626;
(3) 50% of observed days have their intra-day merits range from 523 to 760 (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 567, and 615, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of median and mean, at 666, and 740.
(6) There are 26 potential outliers in total, and there is only three potential outlier days happened in early weeks of 2019, on 09/01/2019, 14/01/2019, and 27/3/2019, at 1161, 1127, and 1249, 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 (2019w14).
(1) The median of intra-week merits is 4507;
(2) 50% of observed weeks (63 weeeks in total), have total merits in the range from 3854 to 5487 (the interquaritle range of intra-week merits);
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Seven potential outliers [beyond 1405 or 7937], all of them occurred in the year 2018.
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Update on intra-week merits (from 24/1/2018 to 08/4/2019)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 |
+-----------------+
- 50% of observed weeks (63 weeks) have total intra-week merits above 4507, whilst the rest 50% of them have total intra-week merits below 4507. 4507 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3854 to 5487 (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 | 63 5675.889 4180.547 4507 3854 5487 3065 30949
----------------------------------------------------------------------------------------------
Potential outliers: It means that potential outliers are weeks that have intra-week merits beyond 1405 or 7937. How many weeks are potential outliers? . count if (merit >= 7937 | merit < 1405) & merit != .
6
weeks are outliers, in total. List of those seven weeks: . list merit week if merit >=7937 | merit <= 1405
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, Wednesday, and Thursday at 666, 654, and 644, respectively; whislt the lowest days are Friday, Sunday, and Saturday, at 567, 608, and 618, respectively. - In means, the highest days are Monday, Wednesday, Sunday, and Tuesday at 740, 719, and 696 (the last twos), respectively; whilst the lowest days are Friday, Saturday, and Thursday at 615, 623, and 674, 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 | 59.0 695.3 315.4 608.0 486.0 804.0 389.0 2463.0
Monday | 60.0 740.8 283.1 666.0 567.5 815.0 312.0 1862.0
Tuesday | 59.0 695.9 214.6 637.0 580.0 760.0 383.0 1326.0
Wednesday | 59.0 718.4 219.4 654.0 559.0 761.0 435.0 1268.0
Thursday | 59.0 673.8 213.6 644.0 514.0 774.0 347.0 1333.0
Friday | 59.0 614.3 215.7 567.0 487.0 698.0 348.0 1696.0
Saturday | 59.0 622.7 208.2 618.0 463.0 688.0 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 414.0 680.3 244.1 626.0 523.0 760.0 312.0 2463.0
-------------------------------------------------------------------------------------------
Outliers displayed as red circles. Outliers non-displayed.
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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 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 |
|-------------------------------------------------------------------------------|
16. | 2141 16 08feb2018 Thursday 8 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. | 1186 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 |
|-------------------------------------------------------------------------------|
46. | 1169 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 |
47. | 1161 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 |
48. | 1159 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 |
49. | 1146 69 02apr2018 Monday 2 4 2018 2018w14 2018m4 |
50. | 1138 153 25jun2018 Monday 25 6 2018 2018w26 2018m6 |
|-------------------------------------------------------------------------------|
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. | 412 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 |
23. | 412 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 |
24. | 415 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 |
25. | 415 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 |
|-------------------------------------------------------------------------------|
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. | 424 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 |
|-------------------------------------------------------------------------------|
31. | 424 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 |
32. | 426 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 |
33. | 428 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 |
34. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
35. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
|-------------------------------------------------------------------------------|
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. | 444 182 24jul2018 Tuesday 24 7 2018 2018w30 2018m7 |
42. | 445 143 15jun2018 Friday 15 6 2018 2018w24 2018m6 |
43. | 450 373 31jan2019 Thursday 31 1 2019 2019w5 2019m1 |
44. | 451 206 17aug2018 Friday 17 8 2018 2018w33 2018m8 |
45. | 454 283 02nov2018 Friday 2 11 2018 2018w44 2018m11 |
|-------------------------------------------------------------------------------|
46. | 455 167 09jul2018 Monday 9 7 2018 2018w28 2018m7 |
47. | 455 229 09sep2018 Sunday 9 9 2018 2018w36 2018m9 |
48. | 457 216 27aug2018 Monday 27 8 2018 2018w35 2018m8 |
49. | 458 227 07sep2018 Friday 7 9 2018 2018w36 2018m9 |
50. | 458 324 13dec2018 Thursday 13 12 2018 2018w50 2018m12 |
|-------------------------------------------------------------------------------|
During the period from 24/1/2018 to 08/4/2019, the minimum and maximum of intra-day merits are 312 and 13018 , on 24/12/2018 and 24/1/2018, respectively. |
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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 | 438.0 771.2 493.1 641.0 530.0 796.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 131 or 1195 . di 796-530
266
. di 266*1.5
399
. di 796+399
1195
. di 530-399
131
There are 42 outliers in full dataset, in total. Those days are: . count if (merit >= 1195 | merit <= 131) & merit != .
42
. list id merit date if (merit >= 1195 | merit <= 131) & 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 |
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 | 414.0 680.3 244.1 626.0 523.0 760.0 312.0 2463.0
----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 168 or 1116. . di 760-523
237
. di 237*1.5
355.5
. di 760+355.5
1115.5
. di 523-355.5
167.5
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 >= 1116 | merit <= 168) & merit != .
26
. list id merit date if (merit >= 1116 | merit <= 168) & 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 |
+-------------------------+
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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 |
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For days in 2018, please get it there
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I thank you because it is a very precious recommendation. However, I still thought of how about using Archive to store Telegram messages for later use if we think that those parts of chat is important. Another issue, it is hard to prove that Telegram account is connected with forum user, so I think we should stick to official forum PMs for important deals. Keep it to private messages in the forums.
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I am non native English speaker, so I appreciated your words that help me to learn a new vocabulary, sockpuppet, and its example usage.  |
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May or may not be worth 10 merits, it's up to the sending person. There is no abuse though.
It is the key point. If I get troubles, maybe crypto troubles or real-life troubles, then I fortunately see posts of someone in which I see point or points that I satisfy with, why not send them merits. Maybe, the point makes sense for me, and only at specific time in my life; but it does not make sense for anyone else. I don't judge the post pinpointed above is a merit-abused one or not. It depends on each reader, and theymos don't care about merit abusements if not too extremely over-abusements. |
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Quality posters will definitely rank up. Each quality poster has his/her own pace of merit earnings, but finally they will all to be ranked up. Someone might need 1 year to move from Junior Member to Full Member. Someone else might need longer period (2 or 3 years) to move to Jr. Member to Full Member. Quality posters deserve to rank up and they will certainly rank up when they collect enough merits, even years does not a serious matter. However, after all the works by merit distributors, it is true that the users are not ranking up even if he/she is a quality poster.
I think current requirements are acceptable to prevent shitty posters to move up to higher rank fastly. There are probably two reasons for this, (1) Merit Requirement Per Rank Excessive
I thought it is another wrong point. Merit sources have enough merits to send to good posts, but good posts are hardly to find. It's the matter, merit sources, total merits of merit sources are not matter. (2) Merit Sources are low in number in comparison with the members this forum has[/b].
I don't suggest to decrease merit requirements on ranks, but if such merit requirements decreased adjustments implemented, what should be treated with old generations, who simply ranked up with old rank requirements, without merit requirements. They even can not earn themselves a single merit so far. Let's envision furthere, such as 5 years later, what happen if a old Legendary who will remain actively posting in the forum next 5 years (more than six years in total since merit birthday) won't be able to earn a single merit from 100, 1000 posts during 6 year period. There are some users who selfmade 1x, 2x of required merits for Legendary rank (LoyceV, DdmrDdmr). For users who are inactively in the forum for years, it is obviously that they don't care about their ranks. |
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OP I understand your point of view that it benefits the forum and reduces the workload of moderators if you delete your worthless old posts yourself instead of letting it get reported for deletion.
It is not only a contribution to reduce workload on forum staffs, but also help to improve the overview, general post quality of the forum. Let's me explain more. The fact, you clearly knew that if you join forum in 2017 or 2018, is that most of topics burried with shitty posts months ago, before the beginning of magical merit system. I rarely see forum staffs delete posts (even they are shitty posts) that have more than month- or year-old . Forum staffs delete old topics sometimes, but they don't spend too much time to screen all old posts and delete them. Report button should be used by normal forum users. If you occasionally find shitty posts that made months or years ago, you should report them to mods, then topics might become cleaner. If newcomers visit topics, they will have better impression on those topics, in particular, and better overview on the forum, in general. No one want to stay using forum that is burried with trash posts. |
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Bittrex has done some shady things in the past, the most recent one, they failed to do proper DD on their first IEO.
Small exchanges can become big ones, while big ones can become small ones. It is a simple fact from market selections over time. There is no exchanes or platforms can be a big or biggest one forever. Good sites can become bad ones, and vice versa, so it is probability that users' identities on exchanges will be leaked someday for any type of reason. Therefore, it is why we need to have multi-level of security method, from our registered emails (strong passwords, 2FA for email log-in), to exchanges' security methods (passwords, 2FA). Together, all of those security methods at different levels can have overal-combined good secured level for our accounts. However, it is always bad ideas to accept KYC requirements of airdrops, bounties just to get coins, tokens, money from them. |
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As I can see on the chart, about 500 newbies who received at least one merit from September 17, 2018 until October 1, 2018, have been ultimately banned. Another 250 newbies who were supported in October 2018 have also been banned. Currently, the number of banned newbies does not increase significantly.
Interesting, it implies that massive merit abusements in September last year mostly ended with perma bans. After that wave of abusements, the figure of banned accounts fell dramatically. There are some reasons, maybe people understood that they won't get anything good by abusing the system, eventually they might all with perma bans. Or maybe, their main accounts dried out of airdropped merits, so they are not able to abuse and help their newbies move up to Junior members one more wave. |
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I found a great service which shows real volumes on exchanges and what is more important real buy and sells walls. We all know that almost 90% of exchanges listed on CMC are showing fake volumes and there is not much CMC can do about it as you said @crypto mania. Service I am talking about is called CoinmarketbookOn the page you can read: Market cap is a lie. Buy support tells the true story. Buy support rating separates investments from gamblers. Coinmarket cap and Coinmarketbook, I don't like them. I would prefer to use the term: CoinMarket Value. It means total value of coins on market. It changes over time, not only from supply and demands, but also from availability of capital flow from investors. When investors have more spare money to invest, and really decide to buy, total market value will increase. In contrast, total market value will decrease. |
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