Latest posts of: tranthidung

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8341

Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN] [PoW/MN] Gentarium (GTM) | Masternode hosting platform | Shared MN service

on: February 13, 2019, 01:49:08 AM

Quote from: vella85 on February 12, 2019, 10:31:46 PM Yes I agree, it now gives smaller guys a chance to stake and earn a share of the pie instead of just masternode holders and before people mining. Staking has always been a great way to earn some coins even with a small outlay. I can see GTM being a lot bigger in the next 6 months and by then staking can be very profitable. Staking integration on Gentarium network, somehow and at somewhat extent will help to spread hashes out to small hands of stakers. It is helpful to maintain to core value of crypto, keeping the network as decentralized as possible. Staking also help to reduce potential unexpected side-effects on global environment. Why? Stakers can use VPS and masternode hosting services or staking pools to stake their coins and earn rewards. They don't need to have and operate mining rigs, that requires huge supply of electricty and potentially contribute significant green house gases.

8342

Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN][DOGE] Dogecoin - very currency many coin - v1.10.0

on: February 13, 2019, 01:45:02 AM

Even when there is no problem with Bitcoin's or Ethereum's network (without network's congestions, I meant), I tend to use Dogecoin for my cross-exchange movements. I have never experienced network congestion with Dogecoin. I am not sure that over its long history, Dogecoin has experienced congestions or not. Someone who have more time in the crypto, can confirm that. Quote from: fapar on February 12, 2019, 07:39:02 PM I used Dogecoin for transfers between exchanges when the network Etherium was a big the cost of gas and problems with the network because Cryptokitties. In this regard, the Dogecoin is very profitable. 2 to 4 times per year. However, it seems right for most of altcoins, not only Dogecoin. LOL. Quote But as an asset Dogecoin interesting - one or two times a year it pump up.

8343

Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN] Ethereum: Welcome to the Beginning

on: February 13, 2019, 01:41:36 AM

Bullish market does not mean that everyone can take advantage of its common upwards trend and get profits for their invesments. Even in bullish market, where most of coins rising, investors can easily got losses if they made wrong decision. Buy at lows, sell at highs, investors can only get profits by applying the rule. Unfortunately, sometimes, impatient investors got dramatical losses that partially caused by their impatience.

8344

Other / Meta / Re: ⭐ Forum chronicle - UP Rank List - Congratulations! (BPIP Merit stat, Trust) ⭐

on: February 13, 2019, 01:32:32 AM

Congratulations, Cryptotourist got promotion to be a Full Member!

	Rank	User name	BPIP profile	BPIP Merit RECEIVED	BPIP Merit SEND	Trust	Status	Personal comment about yourself
		Cryptotourist	Cryptotourist	Cryptotourist	Cryptotourist	0: -0 / +0	active	*Information expected*

8345

Other / Meta / Re: [Suggestion] Disable the delete or edit rights on OP's page of OP authors

on: February 11, 2019, 11:40:11 PM

Quote from: LTU_btc on February 11, 2019, 08:51:20 PM Someone would have to make archives of all topics of projects. It's big manual work, who are going to do that? We would need some volunters who will actively watch announcement board and make archive when new thread was published. I don't know, maybe there are some ways to make this work automated. Something like what Vod has traced several forum statistics, such as merit, trust, activity count, postcount, account's security factors (password/ email changed). Of course, a big database for it needed to do this. And, one or several experts with enough skills and knowledge to do this.

8346

Other / Beginners & Help / Re: Guide on avoid red tags by supporting already known scam projects

on: February 11, 2019, 02:36:59 PM

Thank you for translate it into Filipino, and already added the version into the OP. However, I am curious why Fillipinos need to have a translation, because the locals there are proficient in English, I guess. Quote from: sheenshane on February 11, 2019, 11:33:04 AM I also translated your topic into the FILIPINO version Yeap, happy to see your motivational words for my topic. Quote I saw how helpful it is to our fellow countrymen especially newcomers/newbie.

8347

Other / Meta / Re: Merit & new rank requirements

on: February 11, 2019, 06:56:24 AM

The median of intra month merits (with the data contains 13 months, since Jan. 2018 to end of Jan. 2019) is 19597. However, let's take a look at the fluctuations around the median, by looking at the interquartile range of intra-month merits. The interquartile range found is 18047 to 23173. It means that 50 percent of 13 months observed have their total intra-month merits fluctuated in the range from 18047 to 23173 (including the median, at 19597, of course). We tend to only focus on the mean, but the median is a better statistics for centralization, and the interquartile range (IQR) is important, too. IQR help us to have a better overview on the distribution of what we observe. More details, please visit my topic and my newest intra-month merits analysis there: https://bitcointalk.org/index.php?topic=5069140.msg49685978#msg49685978

8348

Other / Meta / Re: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)

on: February 11, 2019, 06:46:00 AM

Intra-month merits update:
(from 24/01/2018 to 31/01/2019)

ABSTRACT

(1) 50% of observed months (13 months in total), have their total intra-month merits above 19597 (the median), whilst 50% of rest months have total intra-month merits below 19597.
(2) 50% of observed months have their total intra-month merits range from 18047 to 23173 (the interquartile range).
(3) Minimum and maximum of intra-month merits are 15327 (in 2018m12) and 46630 (in 2018m2), respectively.

Converted intra-month merits

Code:

. list month merit

     +-----------------+
     |   month   merit |
     |-----------------|
  1. |  2018m1   41760 |
  2. |  2018m2   46630 |
  3. |  2018m3   32084 |
  4. |  2018m4   23173 |
  5. |  2018m5   19597 |
     |-----------------|
  6. |  2018m6   18752 |
  7. |  2018m7   18047 |
  8. |  2018m8   16317 |
  9. |  2018m9   22261 |
 10. | 2018m10   18395 |
     |-----------------|
 11. | 2018m11   17370 |
 12. | 2018m12   15327 |
 13. |  2019m1   22954 |
     +-----------------+

Time-series plot

Basic statistics:

Code:

.         tabstat merit, s(n mean sd p50 p25 p75 min max)

    variable |         N      mean        sd       p50       p25       p75       min       max
-------------+--------------------------------------------------------------------------------
       merit |        13  24051.31  9956.748     19597     18047     23173     15327     46630
----------------------------------------------------------------------------------------------

- Mean +/- sd: 24052 +/- 9957
- Median (Interquartile range): 19597 (18047 - 23173).
- Min - max: 15327 - 46630

Notes:
The 2018m1 (Jan. 2018) is incomplete month.

8349

Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN] Ethereum: Welcome to the Beginning

on: February 11, 2019, 06:24:49 AM

The long journey takes a lot of time to finish. I believe that some day Ethereum will set its new all time high, but not in coming days, or even till the end of 2019. Ethereum has to rally partially over time, with corrections along its pathway like bumps on the road. Additionally, Ethereum can not reach its new ATH if bitcoin won't rally to around $20.000. Bitcoin is a backbone of crypto world, and Ethereum is a backbone of startup ecosystem, by now, but they have a mutual relationship. Quote from: Natalim on February 11, 2019, 01:25:06 AM $200 is not something to be happy, we deserved to reach that price again after being down more than 10 times. I wouldn't call that high also, I know we are going their and now that we have reach $120 already, I think the roll will continue.

8350

Other / Meta / Re: ⭐ Forum chronicle - UP Rank List - Congratulations! (BPIP Merit stat, Trust) ⭐

on: February 11, 2019, 06:18:10 AM

Congratulations, logfiles got promotion to be a Full Member!

	Rank	User name	BPIP profile	BPIP Merit RECEIVED	BPIP Merit SEND	Trust	Status	Personal comment about yourself
		logfiles	logfiles	logfiles	logfiles	0: -0 / +0	active	*Information expected*

8351

Alternate cryptocurrencies / Announcements (Altcoins) / Re: [ANN] WorldCryptoForum Coin! Cryptocurrency Forum Project [WCF][Masternode/POS]

on: February 11, 2019, 06:13:47 AM

Quote from: Plissken on February 10, 2019, 10:07:21 PM Voting for the new reward structure. I don't know that what will happen next after the voting on new reward structure of World Crypto Forum coin, but for now, the WCF coin rallied considerably to above 300 satoshis. It has still been too far away to 1000 or 2000 satoshis, but it is a good recovery after teribble falls.

8352

Other / Beginners & Help / Re: Guide on avoid red tags by supporting already known scam projects

on: February 11, 2019, 06:00:02 AM

Happy to see that my topic has been translated in to Russian language. There is a link to Russian version of my topic, translated by madnessteat Покраска за пиар мошеннических проектов I added the topic into the OP.

8353

Economy / Trading Discussion / Re: List Exchanges Hacked 2011 / 2019

on: February 11, 2019, 05:54:39 AM

The list of hacked exchanges remind me that I never should store my coins on exchanges. Only send them to exchanges to sell at the right time to take profits. After sell orders filled all, I should (I actually usually do this) withdraw all money to my own wallet. I strongly think that it is much safer approach, even the Binance CEO stated that he suggested to store coins/ money on prestigious exchanges.

8354

Other / Meta / Re: DefaultTrust changes

on: February 10, 2019, 04:18:00 PM

What I saw is the users who run Escrow service or manage campaigns with long prestigious histories tend to get the highest trust in the forum. Another kind of user is who desgin signatures with BBCodes. It seems that people tend to leave Green Trust for someone who are trustworthy with their past works (and current ones, of course) on Escrow, bounties, and graphics or BBcode designs).

8355

Other / Meta / Re: Merit & new rank requirements

on: February 10, 2019, 12:41:01 PM

Update for today. From what happened after two significant changes in the forum (demotion of Junior Members in September 2018) and Default Trust Change simultaneously with new added merit sources & reallocation of merits to active merit sources, their effects do not last for too long (several weeks). 1) Intra-week merits: Quote from: tranthidung on February 10, 2019, 12:21:06 PM Intra-week merits ABSTRACT (for truncated dataset) (1) 50% of observed weeks have total intra-week merits above 4423 (median), whilst 50% of them have total intra-week merits below 4423. (2) 50% of observed weeks have total intra-week merits in the range from 3798 to 4953 (the interquartile range). (3) There are 7 potential outlier weeks in total, and the latest one occured in September (2018w38) 2) Tracking the difference of merit circulations with Default Trust Changes (4 weeks later) Quote from: tranthidung on February 10, 2019, 11:58:34 AM 4 weeks later update: Median: +15.6% Mean: +11.1% ABSTRACT (1) Both median and mean of four-week-later period are higher then the before period, with cut-off day is 09/01/2019, at 15.6% and 11.1%, respectively. (2) 50 percent of days in the four weeks later period have intraday merits in the range from 614 to 879 (the interquartile range), whilst the figures of the before period are 511 to 767. < ...> Outliers, non-displayed < ... > Enjoy it, everyone!

8356

Other / Meta / Re: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)

on: February 10, 2019, 12:21:06 PM

Update:

Intra-week merits

ABSTRACT (for truncated dataset)
(1) 50% of observed weeks have total intra-week merits above 4423 (median), whilst 50% of them have total intra-week merits below 4423.
(2) 50% of observed weeks have total intra-week merits in the range from 3798 to 4953 (the interquartile range).
(3) There are 7 potential outlier weeks in total, and the latest one occured in September (2018w38)

Code:

.         list

     +-----------------+
     |    week   merit |
     |-----------------|
  1. |  2018w4   30949 |
  2. |  2018w5   19958 |
  3. |  2018w6   13304 |
  4. |  2018w7   11722 |
  5. |  2018w8    8758 |
     |-----------------|
  6. |  2018w9    8806 |
  7. | 2018w10    7253 |
  8. | 2018w11    7309 |
  9. | 2018w12    6941 |
 10. | 2018w13    6707 |
     |-----------------|
 11. | 2018w14    6415 |
 12. | 2018w15    5487 |
 13. | 2018w16    4631 |
 14. | 2018w17    4585 |
 15. | 2018w18    4953 |
     |-----------------|
 16. | 2018w19    4753 |
 17. | 2018w20    4346 |
 18. | 2018w21    3854 |
 19. | 2018w22    4183 |
 20. | 2018w23    4527 |
     |-----------------|
 21. | 2018w24    3818 |
 22. | 2018w25    4921 |
 23. | 2018w26    4457 |
 24. | 2018w27    4253 |
 25. | 2018w28    4239 |
     |-----------------|
 26. | 2018w29    4159 |
 27. | 2018w30    3652 |
 28. | 2018w31    3798 |
 29. | 2018w32    3994 |
 30. | 2018w33    3618 |
     |-----------------|
 31. | 2018w34    3789 |
 32. | 2018w35    3065 |
 33. | 2018w36    3574 |
 34. | 2018w37    5630 |
 35. | 2018w38    7825 |
     |-----------------|
 36. | 2018w39    4388 |
 37. | 2018w40    4271 |
 38. | 2018w41    3800 |
 39. | 2018w42    4821 |
 40. | 2018w43    3945 |
     |-----------------|
 41. | 2018w44    3339 |
 42. | 2018w45    4513 |
 43. | 2018w46    3722 |
 44. | 2018w47    4558 |
 45. | 2018w48    3750 |
     |-----------------|
 46. | 2018w49    3560 |
 47. | 2018w50    3782 |
 48. | 2018w51    3753 |
 49. | 2018w52    3278 |
 50. |  2019w1    4793 |
     |-----------------|
 51. |  2019w2    6624 |
 52. |  2019w3    5306 |
 53. |  2019w4    4659 |
 54. |  2019w5    4474 |
     +-----------------+

Time-series plot

Basic statistics:
Full dataset

Code:

.         tabstat merit, s(n mean sd p50 p25 p75 min max)

    variable |         N      mean        sd       p50       p25       p75       min       max
-------------+--------------------------------------------------------------------------------
       merit |        54   5843.87  4493.582    4493.5      3800      5630      3065     30949
----------------------------------------------------------------------------------------------

Mean +/- sd: 5844 +/- 4494
Median (interquartile range): 4494 (3800 - 5630)
Min - Max: 3065 - 30949

Potential outliers:
IQR = Q3 - Q1 = 5630 - 3800 =1830
1.5*IQR = 1.5*1830 = 2745
Q3 +1.5*IQR = 5630 + 2745 = 8375
Q1 - 1.5*IQR = 3800 - 2745 = 1055
Potential outliers are weeks have total intra-week merits beyond 1055 or 8375.
How many potential outliers are there?

Code:

. count if (merit >= 8375| merit < 1055) & merit != .
  6

6 weeks in total, and none of them occurred in 2019.

Code:

. list merit week if merit >=8375 | merit <=1055

     +----------------+
     | merit     week |
     |----------------|
  1. | 30949   2018w4 |
  2. | 19958   2018w5 |
  3. | 13304   2018w6 |
  4. | 11722   2018w7 |
  5. |  8758   2018w8 |
     |----------------|
  6. |  8806   2018w9 |
     +----------------+

Truncated dataset

Code:

. tabstat merit, s(n mean sd p50 p25 p75 min max)

    variable |         N      mean        sd       p50       p25       p75       min       max
-------------+--------------------------------------------------------------------------------
       merit |        50   4792.72  1394.324    4422.5      3798      4953      3065      8806
----------------------------------------------------------------------------------------------

Mean +/- sd: 4793 +/- 1395
Median (interquartile range): 4423 (3798 - 4953)
Min - Max: 3065 - 8806
Let's identify potential outliers with the same formula of above calculation
Potential outliers are weeks that have total intra-week merits beyond 2065 or 6686

Code:

. count if merit >=6686 | merit <=2065
  7

7 weeks in total, and the latest one occured in September (2018w38)

Code:

. list merit week if merit >=6686 | merit <=2065

     +-----------------+
     | merit      week |
     |-----------------|
  1. |  8758    2018w8 |
  2. |  8806    2018w9 |
  3. |  7253   2018w10 |
  4. |  7309   2018w11 |
  5. |  6941   2018w12 |
     |-----------------|
  6. |  6707   2018w13 |
 31. |  7825   2018w38 |
     +-----------------+

Notes:
One merit source removed by theymos days ago.

8357

Other / Meta / Re: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)

on: February 10, 2019, 12:15:42 PM

I also made a analysis on the percent changes between before and after period (cut-off day chosen is 09/01/2019) in another topic Tracking the difference of merit circulations with Default Trust Changes In short Quote from: tranthidung on February 10, 2019, 11:58:34 AM 4 weeks later update: Median: +15.6% Mean: +11.1% ABSTRACT (1) Both median and mean of four-week-later period are higher then the before period, with cut-off day is 09/01/2019, at 15.6% and 11.1%, respectively. (2) 50 percent of days in the four weeks later period have intraday merits in the range from 614 to 879 (the interquartile range), whilst the figures of the before period are 511 to 767. Effects of Default Trust Change (if effects actually occured) likely decreased or tailed off over time. Both Percent changes in median and mean dropped over weeks.

8358

Other / Meta / Re: Tracking the difference of merit circulations with Default Trust Changes

on: February 10, 2019, 11:58:34 AM

4 weeks later update:
Median: +15.6%
Mean: +11.1%

ABSTRACT

(1) Both median and mean of four-week-later period are higher then the before period, with cut-off day is 09/01/2019, at 15.6% and 11.1%, respectively.
(2) 50 percent of days in the four weeks later period have intraday merits in the range from 614 to 879 (the interquartile range), whilst the figures of the before period are 511 to 767.

Box plots:
Outliers displayed with red circles

Outliers, non-displayed

Basic statistics:

Code:

. tabstat before090119 wkslater_2 wkslater_3 wkslater_4, s(n mean sd p50 p25 p75 min max) format(%9.1f) c(s)

    variable |         N      mean        sd       p50       p25       p75       min       max
-------------+--------------------------------------------------------------------------------
before090119 |     324.0     677.0     263.0     616.5     510.5     766.5     312.0    2463.0
  wkslater_2 |      14.0     840.4     191.4     845.5     658.0     987.0     611.0    1161.0
  wkslater_3 |      21.0     781.9     179.5     715.0     643.0     880.0     587.0    1161.0
  wkslater_4 |      28.0     752.0     183.4     713.0     613.5     879.0     450.0    1161.0
----------------------------------------------------------------------------------------------

Percent changes:

Code:

* For means
. di (752-677)*100/677
11.078287

* For medians
. di (713-617)*100/617
15.559157

8359

Other / Meta / Re: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)

on: February 10, 2019, 11:34:31 AM

Update:

ABSTRACT
Notes:
- The asbstract describes figures of intraday merits over the period from 19/2/2018 to 04/02/2019 (truncated dataset);
- Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 04/02/2019 truncated as well due to incomplete week (the sixth week of 2019, 2019w6);
- Statistics presented in the post are for truncated dataset.

(1) Potential outliers are days that have intraday total merits beyond 131 or 1159;
(2) Median of intraday merits over the period is 620, whilst the interquartile range is from 516 to 773;
(3) Friday [in GTM time] is the day over weeks has lowest intraday merits in terms of both median and mean, at 548, and 618, respectively.
(4) Monday [in GMT time] is the day over weeks has highest intraday merits in terms of both median and mean, at 674, and 747, respectively.
(5) There are 21 potential outliers in total, and there is only one potential outlier day happened in early weeks of 2019, on 09 Jan. 2019, at 1161.

1) Daily merits' time series plot
Full dataset:
I used full dataset, since 24/1/2018 to 06/02/2019 to draw the below plot

Truncated dataset:

2) Basic statistics

* For full dataset (only first 2 days dropped due to extremely high merits distributed during those days)
- Mean +/- sd: 789 +/- 528
- Median (interquartile range): 638 (521 – 835)
- Min - max: 312 - 4493.

From the full dataset, we can easily identify potential outliers via the following formula:
IQR = Q3 – Q1 = 835 – 521 = 314;
1.5*IQR = 1.5*314 = 471
Potential outliers:
- Above Q3 +1.5*IQR = 835 + 471 = 1306
- Below Q1 – 1.5*IQR = 521 – 471 = 50
Let's see how many days that have total intraday merits beyond 50 or 1306?

Code:

. count if (merit >= 1306 | merit <= 50) & merit != .
  33

In total, 33 days are extremely potential outliers, and none of them occured recent weeks (only days before 18/9/2018).

Code:

 . list id merit date if (merit >= 1306 | merit <= 50) & 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 |
 25. |  27    1403   19feb2018 |
 30. |  32    1409   24feb2018 |
     |-------------------------|
 32. |  34    1382   26feb2018 |
 33. |  35    1326   27feb2018 |
 35. |  37    1333   01mar2018 |
 36. |  38    1696   02mar2018 |
 46. |  48    1354   12mar2018 |
     |-------------------------|
 54. |  56    1322   20mar2018 |
234. | 236    2463   16sep2018 |
235. | 237    1862   17sep2018 |
     +-------------------------+

Let's identify potential outliers of the truncated dataset (from 19/2/2018 to 04/02/2019):
- Mean +/- sd: 683 +/- 259
- Median (interquartile range): 620 (516 – 773)
- Min - max: 312 - 2463.
IQR = Q3 – Q1 = 773 – 516 = 257
1.5*IQR = 1.5*257 = 385.5
Potential outliers:
- Above Q3 +1.5*IQR = 773 + 385.5 = 1158.5 ~ 1159
- Below Q1 – 1.5*IQR = 516 – 385.5 = 130.5 ~ 131
Potential outliers are days that have intraday merits beyond 131 or 1159
Let's see how many days that have total intraday merits beyond 131 or 1159

Code:

 . count if (merit >= 1159 | merit <= 131) & merit != .
  21

There are 21 days in total, and there is only one potential outlier day happened in early weeks of 2019, on 09 Jan. 2019, at 1161.

Code:

 . list id merit date if (merit >= 1159 | merit <= 131) & 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 |
 30. |  56    1322   20mar2018 |
 31. |  57    1227   21mar2018 |
     |-------------------------|
 42. |  68    1233   01apr2018 |
210. | 236    2463   16sep2018 |
211. | 237    1862   17sep2018 |
212. | 238    1294   18sep2018 |
213. | 239    1268   19sep2018 |
     |-------------------------|
325. | 351    1161   09jan2019 |
     +-------------------------+

2) The 50 lowest day in terms of daily merits

Code:

 . 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. |   415   109   12may2018    Saturday    12        5   2018   2018w19    2018m5 |
 24. |   415   278   28oct2018      Sunday    28       10   2018   2018w43   2018m10 |
 25. |   418   186   28jul2018    Saturday    28        7   2018   2018w30    2018m7 |
     |-------------------------------------------------------------------------------|
 26. |   420   187   29jul2018      Sunday    29        7   2018   2018w30    2018m7 |
 27. |   421   192   03aug2018      Friday     3        8   2018   2018w31    2018m8 |
 28. |   422   140   12jun2018     Tuesday    12        6   2018   2018w24    2018m6 |
 29. |   424   313   02dec2018      Sunday     2       12   2018   2018w48   2018m12 |
 30. |   424   276   26oct2018      Friday    26       10   2018   2018w43   2018m10 |
     |-------------------------------------------------------------------------------|
 31. |   426   277   27oct2018    Saturday    27       10   2018   2018w43   2018m10 |
 32. |   430   264   14oct2018      Sunday    14       10   2018   2018w41   2018m10 |
 33. |   430   284   03nov2018    Saturday     3       11   2018   2018w44   2018m11 |
 34. |   432   221   01sep2018    Saturday     1        9   2018   2018w35    2018m9 |
 35. |   432   208   19aug2018      Sunday    19        8   2018   2018w33    2018m8 |
     |-------------------------------------------------------------------------------|
 36. |   433   282   01nov2018    Thursday     1       11   2018   2018w44   2018m11 |
 37. |   435   154   26jun2018     Tuesday    26        6   2018   2018w26    2018m6 |
 38. |   435   190   01aug2018   Wednesday     1        8   2018   2018w31    2018m8 |
 39. |   444   182   24jul2018     Tuesday    24        7   2018   2018w30    2018m7 |
 40. |   445   143   15jun2018      Friday    15        6   2018   2018w24    2018m6 |
     |-------------------------------------------------------------------------------|
 41. |   450   373   31jan2019    Thursday    31        1   2019    2019w5    2019m1 |
 42. |   451   206   17aug2018      Friday    17        8   2018   2018w33    2018m8 |
 43. |   454   283   02nov2018      Friday     2       11   2018   2018w44   2018m11 |
 44. |   455   167   09jul2018      Monday     9        7   2018   2018w28    2018m7 |
 45. |   455   229   09sep2018      Sunday     9        9   2018   2018w36    2018m9 |
     |-------------------------------------------------------------------------------|
 46. |   457   216   27aug2018      Monday    27        8   2018   2018w35    2018m8 |
 47. |   458   324   13dec2018    Thursday    13       12   2018   2018w50   2018m12 |
 48. |   458   227   07sep2018      Friday     7        9   2018   2018w36    2018m9 |
 49. |   460   263   13oct2018    Saturday    13       10   2018   2018w41   2018m10 |
 50. |   461   130   02jun2018    Saturday     2        6   2018   2018w22    2018m6 |
     |-------------------------------------------------------------------------------|

3) The 50 highest day in terms of daily merits

Code:

 . 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. |  1245    41   05mar2018      Monday     5        3   2018   2018w10    2018m3 |
 42. |  1233    68   01apr2018      Sunday     1        4   2018   2018w13    2018m4 |
 43. |  1227    57   21mar2018   Wednesday    21        3   2018   2018w12    2018m3 |
 44. |  1186    33   25feb2018      Sunday    25        2   2018    2018w8    2018m2 |
 45. |  1169    28   20feb2018     Tuesday    20        2   2018    2018w8    2018m2 |
     |-------------------------------------------------------------------------------|
 46. |  1161   351   09jan2019   Wednesday     9        1   2019    2019w2    2019m1 |
 47. |  1159    50   14mar2018   Wednesday    14        3   2018   2018w11    2018m3 |
 48. |  1146    69   02apr2018      Monday     2        4   2018   2018w14    2018m4 |
 49. |  1138   153   25jun2018      Monday    25        6   2018   2018w26    2018m6 |
 50. |  1130    51   15mar2018    Thursday    15        3   2018   2018w11    2018m3 |
     |-------------------------------------------------------------------------------|

4) Intraday merits over days of week
- In medians of intraday merits, and GMT time, lowest days are Friday, Saturday and Sunday, at 548, 601 and 610, respectively.
- In means of intraday merits, and GMT time, lowest days are Friday, Saturday, and Thursday at 616, 629, and 673.
- The highest day in terms of medians and means re both Monday, at 674 and 747, respectively.
More details in the raw results below.

Code:

 

. 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 |      50.0     699.9     336.0     610.0     476.0     804.0     389.0    2463.0
   Monday |      51.0     746.6     301.8     674.0     536.0     822.0     312.0    1862.0
  Tuesday |      50.0     695.6     232.2     626.0     578.0     767.0     383.0    1326.0
Wednesday |      50.0     718.3     222.8     653.0     558.0     761.0     435.0    1268.0
 Thursday |      50.0     673.0     225.7     639.5     509.0     774.0     347.0    1333.0
   Friday |      50.0     616.0     232.1     548.0     475.0     698.0     348.0    1696.0
 Saturday |      50.0     628.4     222.5     600.5     461.0     695.0     316.0    1409.0
----------+--------------------------------------------------------------------------------
    Total |     351.0     682.7     258.5     620.0     516.0     773.0     312.0    2463.0
-------------------------------------------------------------------------------------------

Plots:
- Outliers displayed with red circles.

- Outliers non-displayed.

8360

Other / Meta / Re: Time Series Analysis on Distributed Merits in the forum (daily, weekly, monthly)

on: February 10, 2019, 10:57:25 AM

Once again, I thank LoyceV for another weekly data dump in my topic

Update:
Intra-day merits (converted dataset)
From 01/01/2019 to 06/02/2019.

Code:

. 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 |
     +-----------------------------------------------------------------------------+

For part of dataset in the year 2018, please get it in previous page.

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