Tell you, friend, to invite more friend by referral link and invest also so that you can make some money by inviting your friends  Is it your first time to join crypto casinos? It is basic additional income from most of casinos, not only BitVest. Casinos mostly give bonus for their users through referral links. There are some exchanges apply the same strategy to catch new users, but it is less common compared to casinos. |
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SwC has decided to cover the bitcoin network fee for player withdrawals while we build lightning network functionality.
Lightning Network on SwC Poker platform, that will help to increase speed of transactions; and decrease transactions fees. Because there are more competitors, there are less dictators, or in other words cheaper transaction speed is utmost thing for SwC growing further. Moreover, SwC team has shown their strong opinion on abusers and the SwC platform has already had its protections for such potential abusements. |
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I voted for DASH and Stellar, then saw that Stellar by now has stood at the first position of the Vote. It is likely that Stellar got big supports due to its cheap price, and cheap transaction fees. DASH has also had good position with high percentage of vote. Monero, can be another good candidate, but I don't support it because no one knows how governments will react with Monero in months or years to come. For stable casino operations, we should stay away from potential vulnerable coin in aspect of local law enforcement.
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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 |
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. | 2018w50 619 517 768 3782 |
8. | 2019w9 619 518 766 4625 |
9. | 2019w6 619.5 517 768 4318 |
10. | 2019w5 620 516 773 4474 |
|------------------------------------------|
11. | 2019w3 620 514 774 5306 |
12. | 2019w4 621.5 516.5 770.5 4659 |
13. | 2018w49 621.5 517 773 3560 |
14. | 2019w10 623 521 764 4901 |
15. | 2019w11 623 521 761 4318 |
|------------------------------------------|
16. | 2019w12 625.5 521 759.5 4598 |
17. | 2019w14 626 523 760 4418 |
18. | 2018w48 626 521 774 3750 |
19. | 2018w46 626 521 786 3722 |
20. | 2019w13 626 522 764 6120 |
|------------------------------------------|
21. | 2018w47 626.5 521 782 4558 |
22. | 2019w15 628 526 761 5259 |
23. | 2019w17 628 528 761 4450 |
24. | 2019w18 628 528 761 4756 |
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. | 2019w20 636 531 765 5202 |
|------------------------------------------|
31. | 2019w21 636 531 764 4571 |
32. | 2018w41 637 528 808 3800 |
33. | 2019w22 637 531 760 4336 |
34. | 2018w42 639 530 807 4821 |
35. | 2018w43 639 528 801 3945 |
|------------------------------------------|
36. | 2018w36 639 528 838 3574 |
37. | 2018w40 639 528 829 4271 |
38. | 2018w39 640 531 839 4388 |
39. | 2018w38 641 530 846 7825 |
40. | 2018w35 642 537 844 3065 |
|------------------------------------------|
41. | 2018w34 652 555 848 3789 |
42. | 2018w33 667 559 867 3618 |
43. | 2018w32 675 567 880 3994 |
44. | 2018w31 682 575 891 3798 |
45. | 2018w30 684 577 902 3652 |
|------------------------------------------|
46. | 2018w29 693 589 922 4159 |
47. | 2018w28 707 592 963 4239 |
48. | 2018w27 715 598 979 4253 |
49. | 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 03/6/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 03/6/2019 truncated as well due to incomplete week (the 2019w23);
- Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 182 or 1114;
(2) Median of intraday merits over the period is 637;
(3) 50% of observed days have their intra-day merits range from 531 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 580, and 614, respectively.
(5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of median and mean, at 676, and 742.
(6) There are 28 potential outliers in total, and there is only four potential outlier days happened in early weeks of 2019, on 09/01/2019, 14/01/2019, 27/3/2019, and 13/5/2019, at 1161, 1127, 1249, and 1150, 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 (2019w23).
(1) The median of intra-week merits is 4527;
(2) 50% of observed weeks (71 weeeks in total), have total merits in the range from 3994 to 5306 (the interquaritle range of intra-week merits).
(3) Minimum and maximum of intraweek merits are 3065 and 30949, in 2018w35, and 2018w4, respectively;
(4) Eight potential outliers [beyond 2026 or 7274], all of them occurred in the year 2018.
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Update on intra-week merits (from 24/1/2018 to 03/6/2019)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 |
+-----------------+
- 50% of observed weeks ( 71 weeks) have total intra-week merits above 4527, whilst the rest 50% of them have total intra-week merits below 4527. 4527 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 3994 to 5306 (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 | 71 5581.254 3945.613 4527 3994 5306 3065 30949
----------------------------------------------------------------------------------------------
. di 5306-3994
1312
. di 1312*1.5
1968
. di 5306+1968
7274
. di 3994-1968
2026
It means that potential outliers are weeks that have intra-week merits beyond 2026 or 7274. How many weeks are potential outliers? . count if (merit >= 7274 | merit < 2026) & merit != .
8
weeks are outliers, in total. List of those seven weeks: . list merit week if merit >=7274 | merit <= 2026
+-----------------+
| merit week |
|-----------------|
1. | 30949 2018w4 |
2. | 19958 2018w5 |
3. | 13304 2018w6 |
4. | 11722 2018w7 |
5. | 8758 2018w8 |
|-----------------|
6. | 8806 2018w9 |
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 676, 672, and 665, respectively; whislt the lowest days are Friday, Sunday, and Saturday at 580, 617, and 618, respectively. - In means, the highest days are Monday, Wednesday, and Tuesday, at 742, 717, and 695, respectively; whilst the lowest days are Friday, Saturday, and Thursday at 614, 626, and 686, 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 | 67.0 691.4 298.9 617.0 511.0 796.0 389.0 2463.0
Monday | 68.0 741.4 271.9 676.0 573.0 803.0 312.0 1862.0
Tuesday | 67.0 694.7 204.7 638.0 585.0 758.0 383.0 1326.0
Wednesday | 67.0 716.8 208.8 665.0 562.0 761.0 435.0 1268.0
Thursday | 67.0 686.0 207.5 672.0 528.0 804.0 347.0 1333.0
Friday | 67.0 613.8 203.2 580.0 499.0 682.0 348.0 1696.0
Saturday | 67.0 626.0 200.8 618.0 482.0 688.0 316.0 1409.0
----------+--------------------------------------------------------------------------------
Total | 470.0 681.6 233.5 636.5 531.0 760.0 312.0 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. | 1150 475 13may2019 Monday 13 5 2019 2019w19 2019m5 |
50. | 1146 69 02apr2018 Monday 2 4 2018 2018w14 2018m4 |
|-------------------------------------------------------------------------------|
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. | 415 109 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. | 424 276 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. | 430 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 |
35. | 430 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 |
|-------------------------------------------------------------------------------|
36. | 432 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 |
37. | 432 221 01sep2018 Saturday 1 9 2018 2018w35 2018m9 |
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. | 458 324 13dec2018 Thursday 13 12 2018 2018w50 2018m12 |
50. | 458 227 07sep2018 Friday 7 9 2018 2018w36 2018m9 |
|-------------------------------------------------------------------------------|
During the period from 24/1/2018 to 03/6/2019, 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 | 494.0 762.1 467.0 643.0 536.0 787.0 312.0 4493.0
----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 160 or 1164. . di 787-536
251
. di 251*1.5
376.5
. di 787+376.5
1163.5
. di 536-376.5
159.5
There are 44 outliers in full dataset, in total. . count if (merit >= 1164 | merit <= 160) & merit != .
44
Those days are: . list id merit date if (merit >= 1164 | merit <= 160) & merit != .
+-------------------------+
| id merit date |
|-------------------------|
1. | 3 4493 26jan2018 |
2. | 4 3489 27jan2018 |
3. | 5 3188 28jan2018 |
4. | 6 3799 29jan2018 |
5. | 7 4192 30jan2018 |
|-------------------------|
6. | 8 2820 31jan2018 |
7. | 9 2545 01feb2018 |
8. | 10 2568 02feb2018 |
9. | 11 1867 03feb2018 |
10. | 12 2167 04feb2018 |
|-------------------------|
11. | 13 2077 05feb2018 |
12. | 14 2308 06feb2018 |
13. | 15 2141 07feb2018 |
14. | 16 2141 08feb2018 |
15. | 17 1448 09feb2018 |
|-------------------------|
16. | 18 1747 10feb2018 |
17. | 19 1442 11feb2018 |
18. | 20 1331 12feb2018 |
19. | 21 1579 13feb2018 |
20. | 22 2513 14feb2018 |
|-------------------------|
21. | 23 1991 15feb2018 |
22. | 24 1411 16feb2018 |
23. | 25 1608 17feb2018 |
24. | 26 1289 18feb2018 |
25. | 27 1403 19feb2018 |
|-------------------------|
26. | 28 1169 20feb2018 |
27. | 29 1266 21feb2018 |
28. | 30 1279 22feb2018 |
30. | 32 1409 24feb2018 |
31. | 33 1186 25feb2018 |
|-------------------------|
32. | 34 1382 26feb2018 |
33. | 35 1326 27feb2018 |
35. | 37 1333 01mar2018 |
36. | 38 1696 02mar2018 |
39. | 41 1245 05mar2018 |
|-------------------------|
46. | 48 1354 12mar2018 |
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 | 470.0 681.6 233.5 636.5 531.0 760.0 312.0 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 28 outliers in total, only four of them occured in 2019, on 09/1/2019, 14/01/2019, 27/3/2019, and 13/5/2019, at 1161, 1127, 1249, and 1150, respectively. . count if (merit >= 1104 | merit <=188) & merit != .
28
List of those 28 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 |
+-------------------------+
|
|
|
|
Unless there is some funny things going on around you account and the coins you deposited in them, they will not ask for KYC and there is never a complaint from any legit users regarding any issues with the site even when they are withdrawing their money and the only issue i remember they had was when there was a DNS hijack and they locked the accounts even with two factor authentication and while contacting the support they took swift action and was able to recover the account.
If there is no signal of abusements on the platform, multi accounts, for example; or there is no huge withdrawal, KYCs won't be required by Stake.com. There is only one thing should be considered if someone think that they might do huge withdrawals in the future, in such withdrawals, KYCs will be asked, but if they don't want to do KYCs, they should have detailed plans to withdraw their funds gradually, not immidiately at a single withdrawal. |
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|
It is really a big deal in this world of bitcoin, because the purpose of bitcoin exist is to be anonymous for everyone so by giving out the KYC, it will be problem for most of the people right here. What is the point of giving bitcoin from the start if you still exist? If your there is a small chance to let your privacy get out then you will risk it, by getting scam in the first place. So it is better not to submit to any KYC
If someone really don't want to do KYCs and lose their privacy a little bit, they should separate their withdrawals by many rounds, and avoid KYC requirements. There are usually thresholds of withdrawals per day on casinos (as same as on exchanges), but if someone have intention to stay anonymous, they should withdraw gradually whenever they feel their balance exceeds their setup threshold. Managing account like this will help them to avoid KYCs |
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Livecoin Overview (from 02/4/2019 to 04/6/2019) 10 weeks in total so far Converted dataset from the spreadsheet:. list, abb(30)
+------------------------------------------------------------------------------------------------+
| id week postcount btcpaid day month2 year date week2 month quarter |
|------------------------------------------------------------------------------------------------|
1. | 1 1 748 .2792 2 4 2019 02apr2019 2019w14 2019m4 2019q2 |
2. | 2 2 878 .329 9 4 2019 09apr2019 2019w15 2019m4 2019q2 |
3. | 3 3 863 .3224 16 4 2019 16apr2019 2019w16 2019m4 2019q2 |
4. | 4 4 875 .3274 23 4 2019 23apr2019 2019w17 2019m4 2019q2 |
5. | 5 5 820 .3068 30 4 2019 30apr2019 2019w18 2019m4 2019q2 |
|------------------------------------------------------------------------------------------------|
6. | 6 6 964 .3599 7 5 2019 07may2019 2019w19 2019m5 2019q2 |
7. | 7 7 1211 .3928 14 5 2019 14may2019 2019w20 2019m5 2019q2 |
8. | 8 8 1072 .3435 21 5 2019 21may2019 2019w21 2019m5 2019q2 |
9. | 9 9 1134 .3659 28 5 2019 28may2019 2019w22 2019m5 2019q2 |
10. | 10 10 1105 .3598 4 6 2019 04jun2019 2019w23 2019m6 2019q2 |
+------------------------------------------------------------------------------------------------+
  Statistics:- Postcount: 50% of observed weeks have total postcounts above 921, whilst 50% of rest weeks have total postcounts below 921, the median (p50). The mean and standard deviation are 928.9 and 149.2, respectively. Min and max are 748 and 1211, respectively. 50% of total weeks have total postcounts in the range from 863 and 1105 (the interquartile range that lasts from p25 to p75) - BTC paid: 50% of observed weeks have total BTC paid above 0.3363, whilst 50% of rest weeks have total BTC paid below 0.3363, the median (p50). The mean and standard deviation are 0.3387 and 0.0327, respectively. Min and max are 0.2792 and 0.3928, respectively. 50% of total weeks have total BTC paid in the range from 0.3224 and 0.3599 (the interquartile range that lasts 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 | 10 967 154.2991 921 863 1105 748 1211
btcpaid | 10 .33867 .0327103 .33625 .3224 .3599 .2792 .3928
----------------------------------------------------------------------------------------------
. list id week postcount day month2 year date week2 month quarter, abb(30)
+--------------------------------------------------------------------------------------+
| id week postcount day month2 year date week2 month quarter |
|--------------------------------------------------------------------------------------|
1. | 7 7 1211 14 5 2019 14may2019 2019w20 2019m5 2019q2 |
2. | 9 9 1134 28 5 2019 28may2019 2019w22 2019m5 2019q2 |
3. | 10 10 1105 4 6 2019 04jun2019 2019w23 2019m6 2019q2 |
4. | 8 8 1072 21 5 2019 21may2019 2019w21 2019m5 2019q2 |
5. | 6 6 964 7 5 2019 07may2019 2019w19 2019m5 2019q2 |
|--------------------------------------------------------------------------------------|
6. | 2 2 878 9 4 2019 09apr2019 2019w15 2019m4 2019q2 |
7. | 4 4 875 23 4 2019 23apr2019 2019w17 2019m4 2019q2 |
8. | 3 3 863 16 4 2019 16apr2019 2019w16 2019m4 2019q2 |
9. | 5 5 820 30 4 2019 30apr2019 2019w18 2019m4 2019q2 |
10. | 1 1 748 2 4 2019 02apr2019 2019w14 2019m4 2019q2 |
+--------------------------------------------------------------------------------------+
. list id week btcpaid day month2 year date week2 month quarter, abb(30)
+------------------------------------------------------------------------------------+
| id week btcpaid day month2 year date week2 month quarter |
|------------------------------------------------------------------------------------|
1. | 7 7 .3928 14 5 2019 14may2019 2019w20 2019m5 2019q2 |
2. | 6 6 .3599 7 5 2019 07may2019 2019w19 2019m5 2019q2 |
3. | 8 8 .3435 21 5 2019 21may2019 2019w21 2019m5 2019q2 |
4. | 2 2 .329 9 4 2019 09apr2019 2019w15 2019m4 2019q2 |
5. | 4 4 .3274 23 4 2019 23apr2019 2019w17 2019m4 2019q2 |
|------------------------------------------------------------------------------------|
6. | 3 3 .3224 16 4 2019 16apr2019 2019w16 2019m4 2019q2 |
7. | 5 5 .3068 30 4 2019 30apr2019 2019w18 2019m4 2019q2 |
8. | 1 1 .2792 2 4 2019 02apr2019 2019w14 2019m4 2019q2 |
+------------------------------------------------------------------------------------+
|
|
|
|
777 coin campaign observations (since 30/12/2018 to 02/6/2019) 23 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 |
+-------------------------------------------------------------------------------------------------+
  The time series plots show that both weekly postcounts and BTC-paid have reached new all time high last week. Statistics:- Postcount: 50% of observed weeks have total postcounts above 1203, whilst 50% of rest weeks have total postcounts below 1203, the median (p50). The mean and standard deviation are 1151 and 165, respectively. Min and max are 708 and 1392, respectively. 50% of total weekly postcounts range from 1049 to 1266 (the interquartile range from p25 to p75). - BTC paid: 50% of observed weeks have total BTC paid above 0.0562, whilst 50% of rest weeks have total BTC paid below 0.0562, the median (p50). The mean and standard deviation are 0.0544 and 0.0074, respectively. Min and max are 0.0308 and 0.0632, respectively. 50% of total weekly BTC-paid range from 0.0508 and 0.0601 (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 | 23 1150.957 164.0633 1203 1049 1266 708 1392
btcpaid | 23 .0544107 .0074233 .056165 .05084 .060075 .03082 .063185
----------------------------------------------------------------------------------------------
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. | 1392 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
2. | 1348 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
3. | 1313 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
4. | 1310 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
5. | 1303 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
|----------------------------------------------------------------------------------|
6. | 1266 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
7. | 1263 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
8. | 1258 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
9. | 1226 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
10. | 1210 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
11. | 1203 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
12. | 1203 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
13. | 1186 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
14. | 1120 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
15. | 1107 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|----------------------------------------------------------------------------------|
16. | 1071 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
17. | 1051 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
18. | 1049 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
19. | 1037 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
20. | 1016 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
21. | 922 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
22. | 910 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
23. | 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. | .063185 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
2. | .06284 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
3. | .06225 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
4. | .06126 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
5. | .06036 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
|--------------------------------------------------------------------------------|
6. | .060075 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
7. | .05849 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
8. | .058315 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
9. | .058255 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
10. | .057785 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
|--------------------------------------------------------------------------------|
11. | .05641 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
12. | .056165 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
13. | .05558 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
14. | .05373 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
15. | .052805 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|--------------------------------------------------------------------------------|
16. | .05278 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
17. | .05085 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
18. | .05084 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
19. | .049555 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
20. | .04953 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
|--------------------------------------------------------------------------------|
21. | .045645 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
22. | .04392 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
23. | .03082 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
+--------------------------------------------------------------------------------+
|
|
|
|
BitVest campaign observations (since 30/12/2018 to 02/6/2019) 23 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 |
+--------------------------------------------------------------------------------------------------+
  Statistics:- Postcount: 50% of observed weeks have total postcounts above 1492, whilst 50% of rest weeks have total postcounts below 1492, the median (p50). The mean and standard deviation are 1463 and 137, respectively. Min and max are 1116 and 1666, respectively. 50% of total weekly postcounts range from 1408 to 1523 (the interquartile range from p25 to p75). - BTC paid: 50% of observed weeks have total BTC paid above 0.089, whilst 50% of rest weeks have total BTC paid below 0.089, the median (p50). The mean and standard deviation are 0.0886 and 0.0057 respectively. Min and max aare 0.0712 and 0.0974, respectively. 50% of total weekly BTC-paid range from 0.0865 to 0.0923 (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 | 23 1462.261 136.6233 1492 1408 1544 1116 1666
btcpaid | 23 .0885681 .0057417 .08944 .086465 .092315 .0712025 .09738
----------------------------------------------------------------------------------------------
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. | 1666 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
2. | 1663 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
3. | 1659 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
4. | 1560 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
5. | 1551 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
|----------------------------------------------------------------------------------|
6. | 1544 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
7. | 1523 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
8. | 1523 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
9. | 1513 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
10. | 1510 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
|----------------------------------------------------------------------------------|
11. | 1500 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
12. | 1492 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
13. | 1455 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
14. | 1450 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
15. | 1447 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|----------------------------------------------------------------------------------|
16. | 1435 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
17. | 1418 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
18. | 1408 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
19. | 1391 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
20. | 1322 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
|----------------------------------------------------------------------------------|
21. | 1291 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
22. | 1195 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
23. | 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. | .09738 3 13 1 2019 13jan2019 2019w2 2019m1 2019q1 |
2. | .094425 22 26 5 2019 26may2019 2019w21 2019m5 2019q2 |
3. | .09399 2 6 1 2019 06jan2019 2019w1 2019m1 2019q1 |
4. | .093939 12 17 3 2019 17mar2019 2019w11 2019m3 2019q1 |
5. | .093524 13 24 3 2019 24mar2019 2019w12 2019m3 2019q1 |
|---------------------------------------------------------------------------------|
6. | .092315 5 27 1 2019 27jan2019 2019w4 2019m1 2019q1 |
7. | .091493 21 19 5 2019 19may2019 2019w20 2019m5 2019q2 |
8. | .091433 15 7 4 2019 07apr2019 2019w14 2019m4 2019q2 |
9. | .090766 6 3 2 2019 03feb2019 2019w5 2019m2 2019q1 |
10. | .09014 4 20 1 2019 20jan2019 2019w3 2019m1 2019q1 |
|---------------------------------------------------------------------------------|
11. | .090028 18 28 4 2019 28apr2019 2019w17 2019m4 2019q2 |
12. | .08944 11 10 3 2019 10mar2019 2019w10 2019m3 2019q1 |
13. | .0887885 23 2 6 2019 02jun2019 2019w22 2019m6 2019q2 |
14. | .088646 7 10 2 2019 10feb2019 2019w6 2019m2 2019q1 |
15. | .087986 16 14 4 2019 14apr2019 2019w15 2019m4 2019q2 |
|---------------------------------------------------------------------------------|
16. | .087748 9 24 2 2019 24feb2019 2019w8 2019m2 2019q1 |
17. | .086992 17 21 4 2019 21apr2019 2019w16 2019m4 2019q2 |
18. | .086465 14 31 3 2019 31mar2019 2019w13 2019m3 2019q1 |
19. | .085241 8 17 2 2019 17feb2019 2019w7 2019m2 2019q1 |
20. | .084589 20 12 5 2019 12may2019 2019w19 2019m5 2019q2 |
|---------------------------------------------------------------------------------|
21. | .08303 10 3 3 2019 03mar2019 2019w9 2019m3 2019q1 |
22. | .0775045 19 5 5 2019 05may2019 2019w18 2019m5 2019q2 |
23. | .0712025 1 30 12 2018 30dec2018 2018w52 2018m12 2018q4 |
+---------------------------------------------------------------------------------+
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