In my opinion, the bump score is very great tool to reduce spam but by now there are only four boards that have bump scores in effects: - Service Announcements - Announcements (Altcoins) - Tokens (Altcoins) - Bounties (Altcoins)
There are lots of discussion boards (bitcoin, altcoin, gambling) that have thousands of spam posts and many spam mega threads. If bump score extends to those boards and spam mega threads, it will be another great step to fight spam. Bump score, by itself, can not solve spam because it depends on companies and managers in the way they count eligible posts from their participants. Some include posts in those boards, some exclude posts in those boards. I think of spam fighting and some potential solutions: - Whitelisting with manager-jails
- Spam score
- Community vote
1. Whitelisting with manager-jailsWhitelisting aims at managers. Some criteria for manager whitelisting: - Potential indicators of bought accounts: woke up and email changed recently
- Post history with focus on post quality
- Past campaigns
If one account woke up and changed email address recently, it highly means that account changed hands and woke up mostly to run their campaigns. This, is undeniable indicator of very potential spam campaign from potential scam project, too. If one user has very poor quality in post history, it is ridiculous to believe that user will manage campaigns smoothly from spam. If one user has very bad history with past campaigns, we have a foundation to think of potential new spam campaigns. It is not completely true because people can change and improve, I know. Newbie jails likely will not come back but I think manager jails are good for the forum. There are two results of Whitelisting: - Failure: it means that manager is unable to run a campaign at a specific point of time.
- Success: it means that manager is able to run a campaign at a specific point of time.
For type #1: there is nothing to discuss. If that manager want to manage a later campaign, s(he) should improve their contributions in the forum before trying another opportunity. For type #2: how long that manager will be allowed to manage that campaign (if company does want to stop it) will depend on two following factors. 2. Spam scoreSpam score will be calculated by some factors: - Percentage one-line posts with each signature.
- Percentage of posts deleted by moderators.
I know post length does not determine post quality in some cases but in my opinion only knowledgeable users can make good posts with one line. Most of one line posts are spam. From that point, if a company runs a signature campaign with dominant percentage of one line posts per total posts from their participants, it is very good indicator of a spam signature campaign. To support one line post factor, the second one, the percentage of posts deleted by moderators is needed. If a one line post is a good one, it will not be deleted by moderators. For deleted posts, they serves same role as the first factor. 3. Community voteEach user has vote right and vote power but voting is optional not mandatory. Vote power depends on their ranks, their total earned merits and their total earned merits in the last 120 days.
The second and third ones will be updated weekly. If one manager gets negative results for the second and third indicators, it is reasonable for admin to consider mandatorily stop their campaigns (there are more criteria of admin for his decision, I know), like what the forum did with Yobit campaign many months ago. How do you think, spam busters?
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Congratulations! According to my dataset, which presents for the last 20 weeks, you usually earn 8 merits weekly (in median - p50), and 50% of those 20 weeks, you earned from 2 to 12.5 merits, weekly (the interquartile range - p25 to p75) You are belong to the top 51-75 most merited users in the forum. < ... > - Period: 2019w23 - 2019w43
< ... > . tabstat meritchange , s(n mean sd p50 p25 p75 min max) by(username)
Summary for variables: meritchange by categories of: username (username)
username | N mean sd p50 p25 p75 min max -----------------+-------------------------------------------------------------------------------- Coolcryptovator | 20 8.65 7.073114 8 2 12.5 0 25
Over each users (outliers displayed with red circles): < ... >
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Today, I learned one more thing, nuked accounts can not be linked via url links. ![Smiley](https://bitcointalk.org/Smileys/default/smiley.gif) I have never seen that before. LoyceV check this! Really? You can check it now by clicking on the link to that account in OP. ![Wink](https://bitcointalk.org/Smileys/default/wink.gif) I am sorry, I checked and it seems I forgot to add url link in OP. It works now. ![Undecided](https://bitcointalk.org/Smileys/default/undecided.gif)
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Why not read those stories, then learn from them? Inspirational stories from self-made promoted usersTo earn merits, people have to do same things no matter which rank they temporarily stay at (Newbie or whatsoever): - Control their fingers and only type their posts if they have good ideas to discuss; avoid unnecessary posts I meant. - Search for many available answers before posting and asking. - Spend time to read, learn and increase their knowledge and experience in the forum and in crypto; just to be more knowledgeable that will do help increasing their post quality. - Save good posts or threads for later reference, just save them as good sources for their own libraries. - Being kindful and helpful in the forum (with good quality posts, of course). You should know that I started in the forum as same as you, zero merit and from the newbie rank with merit system. You can do it!
I wrote some words in the past, too. (1) Never begging for meritsReasons:- Doing this will highly result in Red Trust, it is just the matter of time. Sooner or later, if you keep begging for merits, you will get Red Trust, certainly. - Doing this don't help you to improve yourself. In long-term journey in the forum, it's not good for you, generally. (2) Spending most of your time to read and learnOnly start composing your posts when you have something extremely constructive to discussions, topics. Reasons:- You are noobs, newbies (lack of knowledge, and experience), so what you think are constructive most likely unconstructive in reality. - When you actually have reasons to start composing your posts, after finishing it, please re-read again and again to check that your posts are good in grammar, vocabularies, and good enough to express most of your ideas. I wrote most of your ideas because sometimes it is difficult for non-native English speakers to write down and completely express ideas by words. It's a challenge for non-native English speakers, who don't have English as their mother tongue. < ... > (4) Never mind of bounties, campaigns when you are noobsReasons:- Campaigns are there, open then close, then others open and close, from time to time. You should never worry that you miss this chance, that chance, this campaign, that campaign, something like this. It is the same as merits. Lots of users complain that merits are rare, I can not get merits due to it is rare. Nope, merits (more exactly, sMerits - sendable merits) are available, everywhere in the forum. There are so many merit sources, normal users, who have lots of sMerits readily to send out. They kept them partially due to quality posts are rare, not sMerits. - Instead of paying too much attention, and time on hunting for bounties, especially bounties that have easy rules and joinable for low rank users; you should start learning, reading, and build up your accounts. When you hit your finish lines, it's time for you to seriously think of joining bounties. (5) Learn from inspirational stories. Learn from merited users, topics, postsReading and learning from most merited users, and most merited topics, posts are one of the best way to improve your post quality, coherence and cohesion when you compose your posts, topics. I have a topic on: Inspirational stories from self-made promoted users(6) Spend your time to improve your English, especially Reading and Writing skillsReasons:- If you can not read posts or topic in English well, can not get ideas of posts' / topics' authors, it means that you have nothing to do in the forum. Remember that you have to get their ideas well enough to not misunderstand their core ideas. - Next, after reading good enough to catch authors' ideas, it is time for you to express your own ideas in case you have something to ask for help, something to discuss, or something meaningful to help others. This is the time you need to have good enough Writing skill. This is why I mentioned you should improve your English skills, step by step, from Reading to Writing. Of course, you can improve both skills simultaneously. The forum is the place almost solely for discussions via Reading and Writing. You can find available sources for English learning in the References at the end of this OP. (7) Don't pay your attention on post-length (Ideas that I took from theymos' Writing a welcome message) If you make ten thousand posts in a week, your activity will be capped and you will still be a Newbie. If you make ten thousand useless posts over any period of time, you will gain zero merit and you will still be a Newbie. You can rank up only by making good posts consistently. It's quality over quantity.
When trying to write quality posts, a lot of people act as though they're writing a book report for school: putting facts that we already know into their own words. Nobody wants to read that, and you will not get merit for it. Moreover, the length of your post and the quality of your English are only minor factors. In trying to write a quality post worthy of merit, you should offer new ideas, personal experiences, or perspectives that other forum users will actually find new and interesting.
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No one will complain if posts in those Mega threads are not counted for both post count and activity. It can be done by the forum (that I doubt the forum will not change its algorithm) or by managers of campaigns. In some threads, disable post counts for paid-campaigns in Mega threads will reduce total posts inside but in some special threads, like Wall Observer, there are still lots of posts made. People made posts in Wall Observer are truly WO's citizens and they love their 'WO house'. I know some of them made their posts in WO more than in other threads. There is an interesting thing about spam mega threads is OPs usually abandon their threads.
I would like to know the point of view of posting in such threads.
Don't post when you don't have valuable ideas to contribute and discuss as the core purpose of the forum, presented there: This forum exists to provide a platform for the free (but ordered) exchange of ideas. If you have an idea to express, then it is probably possible to do it here as long as you follow the rules.
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Enjoy the monthly update Update:ABSTRACT(1) Last two months, Bitcoin increased ~11% in price, while figures for total replies, total pages, total views between last two periods (31/8/2019-30-9/2019 and 30/9/2019-31/10/2019) fell 11.5%, 27.1%, and 11.6%, respectively. (Details, please see in the last table in the bottom of the post).(2) In medians, figures for monthly new replies, views, and pages are 6099, 52920, and 268, respectively Data source (from @VB1001): https://bitcointalk.org/index.php?topic=178336.msg52028093#msg52028093https://bitcointalk.org/index.php?topic=178336.msg52029308#msg52029308https://bitcointalk.org/index.php?topic=178336.msg52325977#msg52325977https://bitcointalk.org/index.php?topic=178336.msg52614459#msg52614459https://bitcointalk.org/index.php?topic=178336.msg52946959#msg52946959 Converted dataset:. list id day month2 year date rep repchange prep2 views viewchange pview2 pages pagechange ppages2 btc pbtc, abb(30)
+-----------------------------------------------------------------------------------------------------------------------------------------------------+ | id day month2 year date rep repchange prep2 views viewchange pview2 pages pagechange ppages2 btc pbtc | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 1. | 1 31 10 2017 31oct2017 361446 . . 19386138 . . 18703 . . 6380 . | 2. | 2 30 11 2017 30nov2017 368288 6842 . 19749672 363534 . 18415 -288 . 9786 53.39 | 3. | 3 31 12 2017 31dec2017 380800 12512 82.87 20076512 326840 -10.09 19041 626 . 13186 34.74 | 4. | 4 31 1 2018 31jan2018 386480 5680 -54.6 20118498 41986 -87.15 19325 284 -54.63 10138 -23.12 | 5. | 5 28 2 2018 28feb2018 396480 10000 76.06 20185277 66779 59.05 19825 500 76.06 10629 4.84 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 6. | 6 31 3 2018 31mar2018 402460 5980 -40.2 20222795 37518 -43.82 20109 284 -43.2 7119 -33.02 | 7. | 7 30 4 2018 30apr2018 407457 4997 -16.44 20247143 24348 -35.1 20288 179 -36.97 9275 30.29 | 8. | 8 31 5 2018 31may2018 410507 3050 -38.96 20280891 33748 38.61 20526 238 32.96 7559 -18.5 | 9. | 9 30 6 2018 30jun2018 416730 6223 104.03 20315481 34590 2.49 20787 261 9.66 6457 -14.58 | 10. | 10 31 7 2018 31jul2018 419443 2713 -56.4 20341078 25597 -26 20975 188 -27.97 7848 21.54 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 11. | 11 31 8 2018 31aug2018 424622 5179 90.9 20413924 72846 184.59 21132 157 -16.49 6935 -11.63 | 12. | 12 30 9 2018 30sep2018 426942 2320 -55.2 20474102 60178 -17.39 21288 156 -.64 6633 -4.35 | 13. | 13 30 10 2018 30oct2018 430939 3997 72.28 20650285 176183 192.77 21447 159 1.92 6642 .14 | 14. | 14 30 11 2018 30nov2018 437199 6260 56.62 20782977 132692 -24.69 21810 363 128.3 4015 -39.55 | 15. | 15 31 12 2018 31dec2018 446510 9311 48.74 20926425 143448 8.11 22296 486 33.88 3800 -5.35 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 16. | 16 31 1 2019 31jan2019 452916 6406 -31.2 21006158 79733 -44.42 22563 267 -45.06 3459 -8.97 | 17. | 17 28 2 2019 28feb2019 459605 6689 4.42 21042628 36470 -54.26 22974 411 53.93 3901 12.78 | 18. | 18 31 3 2019 31mar2019 465823 6218 -7.04 21079099 36471 0 23292 318 -22.63 4103 5.18 | 19. | 19 30 4 2019 30apr2019 473225 7402 19.04 21133357 54258 48.77 23662 370 16.35 5270 28.44 | 20. | 20 31 5 2019 31may2019 480783 7558 2.11 21193583 60226 11 24040 378 2.16 8501 61.31 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 21. | 21 30 6 2019 30jun2019 488192 7409 -1.97 21256108 62525 3.82 24410 370 -2.12 11262 32.48 | 22. | 22 31 7 2019 31jul2019 493567 5375 -27.45 21307690 51582 -17.5 24679 269 -27.3 10052 -10.74 | 23. | 23 31 8 2019 31aug2019 497611 4044 -24.76 21343127 35437 -31.3 24881 202 -24.91 9605 -4.45 | 24. | 24 30 9 2019 30sep2019 502612 5001 23.66 21386769 43642 23.15 25131 250 23.76 8247 -14.14 | 25. | 25 31 10 2019 31oct2019 507039 4427 -11.48 21418588 31819 -27.09 25352 221 -11.6 9154 11 | +-----------------------------------------------------------------------------------------------------------------------------------------------------+
Notes on variables:- rep: total replies - repchange: change of total replies between two months - prep2: percent of change in rep (replies) between two continuous months. - views: total views - viewchange: change of total views between two months - pview2: percent of change in total views (views) between two continuous months. - pages: total pages - pagechange: change of total pages between two months - ppages2: percent of change in total pages (pages) between two continuous months. - btc: BTC price - pbtc: percent of change in BTC price (btc) between two continuous months. Since this update, I use new variables, pview2, prep2, ppages2, because variables for percentage of changes between two months in OP look stupid, so I decided to change.Now, let I explain the formula for variable prep2: To make it simple, I have a simple dataset like: month rep repchange prep2 1 10 na na 2 50 40 na 3 80 30 x
How to calculate x? x = (80-50)/(50-10)*100 It means I calculate percentage of changes in two period: period 1 (from month 1 to month 2), period 2 (from month 2 to month 3) Results:Period of observations (24 months, Oct. 2017 - 31 th Oct. 2019) In medians, figures for monthly new replies, views, and pages are 6099 (repchange), 52920 (viewchange) and 268 (pagechange), respectively . tabstat repchange viewchange pagechange, s(n mean sd p50 p25 p75 min max) c(s)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- repchange | 24 6066.375 2318.993 6099 4712 7122 2320 12512 viewchange | 24 84685.42 89092.09 52920 35953.5 76289.5 24348 363534 pagechange | 24 277.0417 169.5245 268 195 370 -288 626 ----------------------------------------------------------------------------------------------
Plots ![](https://ip.bitcointalk.org/?u=https%3A%2F%2Fi.imgur.com%2FXKfw9xh.png&t=663&c=aoAZTZuvMdt1qg) . list id day month2 year date prep2 pview2 ppages2 pbtc, abb(30)
+---------------------------------------------------------------------------+ | id day month2 year date prep2 pview2 ppages2 pbtc | |---------------------------------------------------------------------------| 1. | 1 31 10 2017 31oct2017 . . . . | 2. | 2 30 11 2017 30nov2017 . . . 53.39 | 3. | 3 31 12 2017 31dec2017 82.87 -10.09 . 34.74 | 4. | 4 31 1 2018 31jan2018 -54.6 -87.15 -54.63 -23.12 | 5. | 5 28 2 2018 28feb2018 76.06 59.05 76.06 4.84 | |---------------------------------------------------------------------------| 6. | 6 31 3 2018 31mar2018 -40.2 -43.82 -43.2 -33.02 | 7. | 7 30 4 2018 30apr2018 -16.44 -35.1 -36.97 30.29 | 8. | 8 31 5 2018 31may2018 -38.96 38.61 32.96 -18.5 | 9. | 9 30 6 2018 30jun2018 104.03 2.49 9.66 -14.58 | 10. | 10 31 7 2018 31jul2018 -56.4 -26 -27.97 21.54 | |---------------------------------------------------------------------------| 11. | 11 31 8 2018 31aug2018 90.9 184.59 -16.49 -11.63 | 12. | 12 30 9 2018 30sep2018 -55.2 -17.39 -.64 -4.35 | 13. | 13 30 10 2018 30oct2018 72.28 192.77 1.92 .14 | 14. | 14 30 11 2018 30nov2018 56.62 -24.69 128.3 -39.55 | 15. | 15 31 12 2018 31dec2018 48.74 8.11 33.88 -5.35 | |---------------------------------------------------------------------------| 16. | 16 31 1 2019 31jan2019 -31.2 -44.42 -45.06 -8.97 | 17. | 17 28 2 2019 28feb2019 4.42 -54.26 53.93 12.78 | 18. | 18 31 3 2019 31mar2019 -7.04 0 -22.63 5.18 | 19. | 19 30 4 2019 30apr2019 19.04 48.77 16.35 28.44 | 20. | 20 31 5 2019 31may2019 2.11 11 2.16 61.31 | |---------------------------------------------------------------------------| 21. | 21 30 6 2019 30jun2019 -1.97 3.82 -2.12 32.48 | 22. | 22 31 7 2019 31jul2019 -27.45 -17.5 -27.3 -10.74 | 23. | 23 31 8 2019 31aug2019 -24.76 -31.3 -24.91 -4.45 | 24. | 24 30 9 2019 30sep2019 23.66 23.15 23.76 -14.14 | 25. | 25 31 10 2019 31oct2019 -11.48 -27.09 -11.6 11 | +---------------------------------------------------------------------------+
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Update:ABSTRACT(1) Last two months, Bitcoin increased ~11% in price, while figures for total replies, total pages, total views between last two periods (31/8/2019-30-9/2019 and 30/9/2019-31/10/2019) fell 11.5%, 27.1%, and 11.6%, respectively. (Details, please see in the last table in the bottom of the post).(2) In medians, figures for monthly new replies, views, and pages are 6099, 52920, and 268, respectively Data source (from @VB1001): https://bitcointalk.org/index.php?topic=178336.msg52028093#msg52028093https://bitcointalk.org/index.php?topic=178336.msg52029308#msg52029308https://bitcointalk.org/index.php?topic=178336.msg52325977#msg52325977https://bitcointalk.org/index.php?topic=178336.msg52614459#msg52614459https://bitcointalk.org/index.php?topic=178336.msg52946959#msg52946959 Converted dataset:. list id day month2 year date rep repchange prep2 views viewchange pview2 pages pagechange ppages2 btc pbtc, abb(30)
+-----------------------------------------------------------------------------------------------------------------------------------------------------+ | id day month2 year date rep repchange prep2 views viewchange pview2 pages pagechange ppages2 btc pbtc | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 1. | 1 31 10 2017 31oct2017 361446 . . 19386138 . . 18703 . . 6380 . | 2. | 2 30 11 2017 30nov2017 368288 6842 . 19749672 363534 . 18415 -288 . 9786 53.39 | 3. | 3 31 12 2017 31dec2017 380800 12512 82.87 20076512 326840 -10.09 19041 626 . 13186 34.74 | 4. | 4 31 1 2018 31jan2018 386480 5680 -54.6 20118498 41986 -87.15 19325 284 -54.63 10138 -23.12 | 5. | 5 28 2 2018 28feb2018 396480 10000 76.06 20185277 66779 59.05 19825 500 76.06 10629 4.84 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 6. | 6 31 3 2018 31mar2018 402460 5980 -40.2 20222795 37518 -43.82 20109 284 -43.2 7119 -33.02 | 7. | 7 30 4 2018 30apr2018 407457 4997 -16.44 20247143 24348 -35.1 20288 179 -36.97 9275 30.29 | 8. | 8 31 5 2018 31may2018 410507 3050 -38.96 20280891 33748 38.61 20526 238 32.96 7559 -18.5 | 9. | 9 30 6 2018 30jun2018 416730 6223 104.03 20315481 34590 2.49 20787 261 9.66 6457 -14.58 | 10. | 10 31 7 2018 31jul2018 419443 2713 -56.4 20341078 25597 -26 20975 188 -27.97 7848 21.54 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 11. | 11 31 8 2018 31aug2018 424622 5179 90.9 20413924 72846 184.59 21132 157 -16.49 6935 -11.63 | 12. | 12 30 9 2018 30sep2018 426942 2320 -55.2 20474102 60178 -17.39 21288 156 -.64 6633 -4.35 | 13. | 13 30 10 2018 30oct2018 430939 3997 72.28 20650285 176183 192.77 21447 159 1.92 6642 .14 | 14. | 14 30 11 2018 30nov2018 437199 6260 56.62 20782977 132692 -24.69 21810 363 128.3 4015 -39.55 | 15. | 15 31 12 2018 31dec2018 446510 9311 48.74 20926425 143448 8.11 22296 486 33.88 3800 -5.35 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 16. | 16 31 1 2019 31jan2019 452916 6406 -31.2 21006158 79733 -44.42 22563 267 -45.06 3459 -8.97 | 17. | 17 28 2 2019 28feb2019 459605 6689 4.42 21042628 36470 -54.26 22974 411 53.93 3901 12.78 | 18. | 18 31 3 2019 31mar2019 465823 6218 -7.04 21079099 36471 0 23292 318 -22.63 4103 5.18 | 19. | 19 30 4 2019 30apr2019 473225 7402 19.04 21133357 54258 48.77 23662 370 16.35 5270 28.44 | 20. | 20 31 5 2019 31may2019 480783 7558 2.11 21193583 60226 11 24040 378 2.16 8501 61.31 | |-----------------------------------------------------------------------------------------------------------------------------------------------------| 21. | 21 30 6 2019 30jun2019 488192 7409 -1.97 21256108 62525 3.82 24410 370 -2.12 11262 32.48 | 22. | 22 31 7 2019 31jul2019 493567 5375 -27.45 21307690 51582 -17.5 24679 269 -27.3 10052 -10.74 | 23. | 23 31 8 2019 31aug2019 497611 4044 -24.76 21343127 35437 -31.3 24881 202 -24.91 9605 -4.45 | 24. | 24 30 9 2019 30sep2019 502612 5001 23.66 21386769 43642 23.15 25131 250 23.76 8247 -14.14 | 25. | 25 31 10 2019 31oct2019 507039 4427 -11.48 21418588 31819 -27.09 25352 221 -11.6 9154 11 | +-----------------------------------------------------------------------------------------------------------------------------------------------------+
Notes on variables:- rep: total replies - repchange: change of total replies between two months - prep2: percent of change in rep (replies) between two continuous months. - views: total views - viewchange: change of total views between two months - pview2: percent of change in total views (views) between two continuous months. - pages: total pages - pagechange: change of total pages between two months - ppages2: percent of change in total pages (pages) between two continuous months. - btc: BTC price - pbtc: percent of change in BTC price (btc) between two continuous months. Since this update, I use new variables, pview2, prep2, ppages2, because variables for percentage of changes between two months in OP look stupid, so I decided to change.Now, let I explain the formula for variable prep2: To make it simple, I have a simple dataset like: month rep repchange prep2 1 10 na na 2 50 40 na 3 80 30 x
How to calculate x? x = (80-50)/(50-10)*100 It means I calculate percentage of changes in two period: period 1 (from month 1 to month 2), period 2 (from month 2 to month 3) Results:Period of observations (24 months, Oct. 2017 - 31 th Oct. 2019) In medians, figures for monthly new replies, views, and pages are 6099 (repchange), 52920 (viewchange) and 268 (pagechange), respectively . tabstat repchange viewchange pagechange, s(n mean sd p50 p25 p75 min max) c(s)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- repchange | 24 6066.375 2318.993 6099 4712 7122 2320 12512 viewchange | 24 84685.42 89092.09 52920 35953.5 76289.5 24348 363534 pagechange | 24 277.0417 169.5245 268 195 370 -288 626 ----------------------------------------------------------------------------------------------
Plots ![](https://ip.bitcointalk.org/?u=https%3A%2F%2Fi.imgur.com%2FXKfw9xh.png&t=663&c=aoAZTZuvMdt1qg) . list id day month2 year date prep2 pview2 ppages2 pbtc, abb(30)
+---------------------------------------------------------------------------+ | id day month2 year date prep2 pview2 ppages2 pbtc | |---------------------------------------------------------------------------| 1. | 1 31 10 2017 31oct2017 . . . . | 2. | 2 30 11 2017 30nov2017 . . . 53.39 | 3. | 3 31 12 2017 31dec2017 82.87 -10.09 . 34.74 | 4. | 4 31 1 2018 31jan2018 -54.6 -87.15 -54.63 -23.12 | 5. | 5 28 2 2018 28feb2018 76.06 59.05 76.06 4.84 | |---------------------------------------------------------------------------| 6. | 6 31 3 2018 31mar2018 -40.2 -43.82 -43.2 -33.02 | 7. | 7 30 4 2018 30apr2018 -16.44 -35.1 -36.97 30.29 | 8. | 8 31 5 2018 31may2018 -38.96 38.61 32.96 -18.5 | 9. | 9 30 6 2018 30jun2018 104.03 2.49 9.66 -14.58 | 10. | 10 31 7 2018 31jul2018 -56.4 -26 -27.97 21.54 | |---------------------------------------------------------------------------| 11. | 11 31 8 2018 31aug2018 90.9 184.59 -16.49 -11.63 | 12. | 12 30 9 2018 30sep2018 -55.2 -17.39 -.64 -4.35 | 13. | 13 30 10 2018 30oct2018 72.28 192.77 1.92 .14 | 14. | 14 30 11 2018 30nov2018 56.62 -24.69 128.3 -39.55 | 15. | 15 31 12 2018 31dec2018 48.74 8.11 33.88 -5.35 | |---------------------------------------------------------------------------| 16. | 16 31 1 2019 31jan2019 -31.2 -44.42 -45.06 -8.97 | 17. | 17 28 2 2019 28feb2019 4.42 -54.26 53.93 12.78 | 18. | 18 31 3 2019 31mar2019 -7.04 0 -22.63 5.18 | 19. | 19 30 4 2019 30apr2019 19.04 48.77 16.35 28.44 | 20. | 20 31 5 2019 31may2019 2.11 11 2.16 61.31 | |---------------------------------------------------------------------------| 21. | 21 30 6 2019 30jun2019 -1.97 3.82 -2.12 32.48 | 22. | 22 31 7 2019 31jul2019 -27.45 -17.5 -27.3 -10.74 | 23. | 23 31 8 2019 31aug2019 -24.76 -31.3 -24.91 -4.45 | 24. | 24 30 9 2019 30sep2019 23.66 23.15 23.76 -14.14 | 25. | 25 31 10 2019 31oct2019 -11.48 -27.09 -11.6 11 | +---------------------------------------------------------------------------+
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The account - - ElectrumWallet Support - created yesterday and claimed to be an official account used to support Electrum wallet's community. If you are curious, you have to see the post history via the archive link below. Today, I learned one more thing, nuked accounts can not be linked via url links. ![Smiley](https://bitcointalk.org/Smileys/default/smiley.gif) According to modlog, the account was nuked yesterday. Nuke user: N/A in topic #0 by member #2707241
Both moderator, DT members (two as I saw in the trust page), and maybe normal users acted fastly to report that account to forum moderators. Their decent fastly efforts help lots of users in the community be safe from that scammer. Unfortunately, such scammers will pop up every single day. If you use the search function of the forum, with the keyword: Electrum, you will get 2-paged results, that is significant in my opinion. I don't say all or most of those accounts with Electrum in usernames are scammers and I have not yet checked. The important reminder I want to say to you is: Be careful with any accounts that includes the word Electrum in their usernames and other key words if you have intention to ask for support from other coins or services. Especially if whenever you ask for help, opinion from community, one newbie account with 'potential' trapped key words in their usernames appear and claim that they come from core team members, want to give their hands and quick supports, Stay Away from them!You can spend your more time to check official websites to verify those accounts (see the below quote) but in my opinion you don't have to do this. Most of such cases are scammers. For questions if you need, please search for available answers in those channels: https://electrum.org/#communityFrom the official website of Electrum, you can easily see there is no contact emails, they only guide that if users have questions, ask for help from (Community > Support) : https://bitcointalk.org/index.php?board=98.0https://www.reddit.com/r/Electrum/From that, be very careful with the above account that claims to be Electrum Support (a newbie account). The age of that account is serious thing because how long Electrum wallet has been here? Years, but their support account just created recently (about 2 hours ago) and has still been a Newbie. ![Smiley](https://bitcointalk.org/Smileys/default/smiley.gif) Now, let's look at the archived posts of that scammer to learn something and to be safe later. https://archive.is/OKnsX (by @o_e_l_e_o) Please report those scammers, their posts to forum moderators.How to report? I want to recommend you to make a short report content (in fact it has to be short due to character limits) in order to help moderators who received your reports fastly catch what you report. So please read my guides too: Those guides are not for reports, but you can retrieve some points for your report contents from general posting techniques.
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Speculation is kind of a spammy section but not nearly as bad as some of the others, and I like reading some of the threads there just to get the feel of the market. You can tell when things are bearish or bullish by the feel of the threads and posts therein.
Thank you so much for giving valuable personal experience with Speculation board, that I has never visited. You opened my mind a little bit with that post, and I will spend some of my time to read around that board. It is good to have a board from which I can get general emotional and psychological aspect of most investors. It is very helpful for my decisions.
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What you on about 'only'? Dude you get paid to be stupid? To pretend to not understand? Stop scamming everyone of their time at least all you signature fuckers. He's clearly capable of speaking English and it's also great that his merits would go to non-English speakers if he was anointed. None of which conflicts with what I asked.
Why so arrogant, fella? Maybe the word I used (only) is incorrect, but it does not cause serious issues. You are right that the OP is good in English, but he emphasized that most of Turkish do not know English. I agree that it is good (but not necessary) if he can point out under-merited posts (in English) but as the OP aims to be a merit source in his/ her local board (Turkish), I doubt that locals will use English to discuss their opinions, especially in non-proficient English board, like Turkish as OP wrote (I don't know Turkish too).
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For questions if you need, please search for available answers in those channels: https://electrum.org/#communityFrom the official website of Electrum, you can easily see there is no contact emails, they only guide that if users have questions, ask for help from (Community > Support) : https://bitcointalk.org/index.php?board=98.0https://www.reddit.com/r/Electrum/From that, be very careful with the above account that claims to be Electrum Support (a newbie account). The age of that account is serious thing because how long Electrum wallet has been here? Years, but their support account just created recently (about 2 hours ago) and has still been a Newbie. ![Smiley](https://bitcointalk.org/Smileys/default/smiley.gif)
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I don't know any Turkish so I can't support or oppose your application yet. Is there any chance you could find 10 under-merited recent English-language posts that would help your application along?
I think it is one of reason and OP applied to be a merit source in Turkish local board only. Another reason that I apply to become a merit source is that there are not a lot of people who know English in Turkey. As a result, even if they deserve, they can't get merit from other sources.
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Update:Time series plot Dataset for median, interquartile range of intraday merits. list week median q1 q3 merit
+------------------------------------------+ | week median q1 q3 merit | |------------------------------------------| 1. | 2018w26 733 609 991 4465 | 2. | 2018w27 715 598 979 4278 | 3. | 2018w28 707 592 963 4247 | 4. | 2018w29 693 589 922 4167 | 5. | 2018w30 684 577 902 3661 | |------------------------------------------| 6. | 2018w31 682 575 891 3863 | 7. | 2018w32 675 567 880 4011 | 8. | 2018w33 667 559 867 3631 | 9. | 2018w34 652 555 848 3805 | 10. | 2018w35 642 537 844 3072 | |------------------------------------------| 11. | 2018w36 639 528 838 3590 | 12. | 2018w37 634 528 829 5644 | 13. | 2018w38 641 530 846 7837 | 14. | 2018w39 640 531 839 4395 | 15. | 2018w40 639 528 829 4310 | |------------------------------------------| 16. | 2018w41 637 528 808 3816 | 17. | 2018w42 639 530 807 4829 | 18. | 2018w43 639 528 801 3953 | 19. | 2018w44 628 521 796 3347 | 20. | 2018w45 630 522 789 4525 | |------------------------------------------| 21. | 2018w46 628 523 788 3747 | 22. | 2018w47 628 522.5 783.5 4575 | 23. | 2018w48 627 522 778 3765 | 24. | 2018w49 623.5 520 775 3571 | 25. | 2018w50 622 520 774 3805 | |------------------------------------------| 26. | 2018w51 621.5 517.5 770 3769 | 27. | 2018w52 617.5 514 764 3338 | 28. | 2019w1 617 514 769 4803 | 29. | 2019w2 621.5 515 775 6632 | 30. | 2019w3 623 517 777 5317 | |------------------------------------------| 31. | 2019w4 623.5 518.5 775 4667 | 32. | 2019w5 622 518 775 4491 | 33. | 2019w6 622 520 775 4332 | 34. | 2019w7 621 522 771 4221 | 35. | 2019w8 621.5 521 770 4521 | |------------------------------------------| 36. | 2019w9 622 520 769 4638 | 37. | 2019w10 624 522 766 4913 | 38. | 2019w11 624 522 762 4326 | 39. | 2019w12 626.5 523 761 4609 | 40. | 2019w13 628 525 766 6130 | |------------------------------------------| 41. | 2019w14 627.5 529 761 4526 | 42. | 2019w15 629 530 762 5271 | 43. | 2019w16 632.5 530.5 764 4688 | 44. | 2019w17 629 530 762 4448 | 45. | 2019w18 629 531 762 4764 | |------------------------------------------| 46. | 2019w19 636 532 762 5454 | 47. | 2019w20 638.5 532.5 767.5 5214 | 48. | 2019w21 639 533 766 4580 | 49. | 2019w22 639 535 761 4445 | 50. | 2019w23 639 535 761 4687 | |------------------------------------------| 51. | 2019w24 640 536 764 5354 | 52. | 2019w25 640 537 762 4726 | 53. | 2019w26 640 535 762 4367 | 54. | 2019w27 640 535 761 4225 | 55. | 2019w28 639 532.5 761 4119 | |------------------------------------------| 56. | 2019w29 639 532 761 4277 | 57. | 2019w30 636.5 533 760 4176 | 58. | 2019w31 629 532 760 3549 | 59. | 2019w32 628 530 757 3207 | 60. | 2019w33 628 530 755 4236 | |------------------------------------------| 61. | 2019w34 627 529 752 3622 | 62. | 2019w35 627 528 750 3540 | 63. | 2019w36 625.5 526.5 742 3809 | 64. | 2019w37 625 525 742 4043 | 65. | 2019w38 624 528 738 4520 | |------------------------------------------| 66. | 2019w39 624 528 737 4318 | 67. | 2019w40 624 525 737 4357 | 68. | 2019w41 624 525 737 4565 | 69. | 2019w42 626 528 742 5542 | 70. | 2019w43 627 529 742 4975 |
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 617 514 769 4803 | 2. | 2018w52 617.5 514 764 3338 | 3. | 2019w7 621 522 771 4221 | 4. | 2018w51 621.5 517.5 770 3769 | 5. | 2019w8 621.5 521 770 4521 | |------------------------------------------| 6. | 2019w2 621.5 515 775 6632 | 7. | 2018w50 622 520 774 3805 | 8. | 2019w5 622 518 775 4491 | 9. | 2019w6 622 520 775 4332 | 10. | 2019w9 622 520 769 4638 | |------------------------------------------| 11. | 2019w3 623 517 777 5317 | 12. | 2018w49 623.5 520 775 3571 | 13. | 2019w4 623.5 518.5 775 4667 | 14. | 2019w38 624 528 738 4520 | 15. | 2019w10 624 522 766 4913 | |------------------------------------------| 16. | 2019w41 624 525 737 4565 | 17. | 2019w39 624 528 737 4318 | 18. | 2019w11 624 522 762 4326 | 19. | 2019w40 624 525 737 4357 | 20. | 2019w37 625 525 742 4043 | |------------------------------------------| 21. | 2019w36 625.5 526.5 742 3809 | 22. | 2019w42 626 528 742 5542 | 23. | 2019w12 626.5 523 761 4609 | 24. | 2018w48 627 522 778 3765 | 25. | 2019w43 627 529 742 4975 | |------------------------------------------| 26. | 2019w35 627 528 750 3540 | 27. | 2019w34 627 529 752 3622 | 28. | 2019w14 627.5 529 761 4526 | 29. | 2019w13 628 525 766 6130 | 30. | 2019w33 628 530 755 4236 | |------------------------------------------| 31. | 2019w32 628 530 757 3207 | 32. | 2018w46 628 523 788 3747 | 33. | 2018w44 628 521 796 3347 | 34. | 2018w47 628 522.5 783.5 4575 | 35. | 2019w18 629 531 762 4764 | |------------------------------------------| 36. | 2019w15 629 530 762 5271 | 37. | 2019w17 629 530 762 4448 | 38. | 2019w31 629 532 760 3549 | 39. | 2018w45 630 522 789 4525 | 40. | 2019w16 632.5 530.5 764 4688 | |------------------------------------------| 41. | 2018w37 634 528 829 5644 | 42. | 2019w19 636 532 762 5454 | 43. | 2019w30 636.5 533 760 4176 | 44. | 2018w41 637 528 808 3816 | 45. | 2019w20 638.5 532.5 767.5 5214 | |------------------------------------------| 46. | 2018w36 639 528 838 3590 | 47. | 2018w40 639 528 829 4310 | 48. | 2019w22 639 535 761 4445 | 49. | 2019w28 639 532.5 761 4119 | 50. | 2018w43 639 528 801 3953 | |------------------------------------------| 51. | 2018w42 639 530 807 4829 | 52. | 2019w21 639 533 766 4580 | 53. | 2019w23 639 535 761 4687 | 54. | 2019w29 639 532 761 4277 | 55. | 2019w25 640 537 762 4726 | |------------------------------------------| 56. | 2018w39 640 531 839 4395 | 57. | 2019w26 640 535 762 4367 | 58. | 2019w27 640 535 761 4225 | 59. | 2019w24 640 536 764 5354 | 60. | 2018w38 641 530 846 7837 | |------------------------------------------| 61. | 2018w35 642 537 844 3072 | 62. | 2018w34 652 555 848 3805 | 63. | 2018w33 667 559 867 3631 | 64. | 2018w32 675 567 880 4011 | 65. | 2018w31 682 575 891 3863 | |------------------------------------------| 66. | 2018w30 684 577 902 3661 | 67. | 2018w29 693 589 922 4167 | 68. | 2018w28 707 592 963 4247 | 69. | 2018w27 715 598 979 4278 | 70. | 2018w26 733 609 991 4465 |
Now, let's take a look at the variations of intra-day medians over weeks. Method: I took medians of intraday merits over weeks (since 2018w46, from id 293 - 299, here). The median of intraday merits at the end of 2018w46 will be calculated from intraday merits started from days with id #26 - # 299; days before id #26 truncated due to extremely outliers. For later weeks, just moving forwards with each 7-day-time-frame to calculate next medians of intradays over weeks. Results:Since 2018w48 to 2019w43, the dataset has: - 67 weeks in total. - Median of median of intraday merits over weeks is 628. - Interquartile range of median of median of intraday merits over weeks ranges from 624 to 639. . tabstat median, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- median | 67.0 634.0 15.3 628.0 624.0 639.0 617.0 693.0 ----------------------------------------------------------------------------------------------
Data source:- 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 28/10/2019 (truncated dataset); - Days from 24/1/2018 to 18/2/2018 truncated due to highly potential outliers; and days after 28/10/2019 truncated as well due to incomplete week (the 2019w44); - Statistics presented in the post are for truncated dataset
(1) Potential outliers are days that have intraday total merits beyond 210 or 1062; (2) Median of intraday merits over the period is 627; (3) 50% of observed days have their intra-day merits range from 529 to 742 (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 586, and 610, respectively. (5) Monday [in GTM time] is the day over weeks has highest intraday merits in terms of median and mean, at 679, and 728. (6) There are 36 potential outliers (beyond 207 or 1063) in total, and only seven of them occured in 2019, 04/01/2019 (1083) , 09/1/2019 (1162), 14/01/2019 (1128) , 27/3/2019 (1250), 13/5/2019 (1151), 11/6/2019 (1188), and 21/10/2019 (1082) - the newest outlier. (7) Minimum and maximum of intraday merits (full dataset) are 295, and 13018, on 03/8/2019 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 (2019w44).
(1) The median of intra-week merits is 4506; (2) 50% of observed weeks (92 weeeks in total), have total merits in the range from 4027 to 4970 (the interquaritle range of intra-week merits). (3) Minimum and maximum of intra-week merits are 3072 and 30960, in 2018w35, and 2018w4, respectively; (4) Thirteen potential outliers [beyond 2580 or 6396], only one of them occurred in the year 2019, in 2019w2 at 6632.
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Intra-week merits (from 24/1/2018 to 28/10/2019)Last two days dropped due to incomplete weeks (2019w44)Converted dataset:. list merit week
+-----------------+ | merit week | |-----------------| 1. | 30960 2018w4 | 2. | 19979 2018w5 | 3. | 13313 2018w6 | 4. | 11745 2018w7 | 5. | 8767 2018w8 | |-----------------| 6. | 8833 2018w9 | 7. | 7261 2018w10 | 8. | 7317 2018w11 | 9. | 6952 2018w12 | 10. | 6744 2018w13 | |-----------------| 11. | 6423 2018w14 | 12. | 5494 2018w15 | 13. | 4742 2018w16 | 14. | 4612 2018w17 | 15. | 4965 2018w18 | |-----------------| 16. | 4766 2018w19 | 17. | 4353 2018w20 | 18. | 3864 2018w21 | 19. | 4194 2018w22 | 20. | 4538 2018w23 | |-----------------| 21. | 3839 2018w24 | 22. | 4929 2018w25 | 23. | 4465 2018w26 | 24. | 4278 2018w27 | 25. | 4247 2018w28 | |-----------------| 26. | 4167 2018w29 | 27. | 3661 2018w30 | 28. | 3863 2018w31 | 29. | 4011 2018w32 | 30. | 3631 2018w33 | |-----------------| 31. | 3805 2018w34 | 32. | 3072 2018w35 | 33. | 3590 2018w36 | 34. | 5644 2018w37 | 35. | 7837 2018w38 | |-----------------| 36. | 4395 2018w39 | 37. | 4310 2018w40 | 38. | 3816 2018w41 | 39. | 4829 2018w42 | 40. | 3953 2018w43 | |-----------------| 41. | 3347 2018w44 | 42. | 4525 2018w45 | 43. | 3747 2018w46 | 44. | 4575 2018w47 | 45. | 3765 2018w48 | |-----------------| 46. | 3571 2018w49 | 47. | 3805 2018w50 | 48. | 3769 2018w51 | 49. | 3338 2018w52 | 50. | 4803 2019w1 | |-----------------| 51. | 6632 2019w2 | 52. | 5317 2019w3 | 53. | 4667 2019w4 | 54. | 4491 2019w5 | 55. | 4332 2019w6 | |-----------------| 56. | 4221 2019w7 | 57. | 4521 2019w8 | 58. | 4638 2019w9 | 59. | 4913 2019w10 | 60. | 4326 2019w11 | |-----------------| 61. | 4609 2019w12 | 62. | 6130 2019w13 | 63. | 4526 2019w14 | 64. | 5271 2019w15 | 65. | 4688 2019w16 | |-----------------| 66. | 4448 2019w17 | 67. | 4764 2019w18 | 68. | 5454 2019w19 | 69. | 5214 2019w20 | 70. | 4580 2019w21 | |-----------------| 71. | 4445 2019w22 | 72. | 4687 2019w23 | 73. | 5354 2019w24 | 74. | 4726 2019w25 | 75. | 4367 2019w26 | |-----------------| 76. | 4225 2019w27 | 77. | 4119 2019w28 | 78. | 4277 2019w29 | 79. | 4176 2019w30 | 80. | 3549 2019w31 | |-----------------| 81. | 3207 2019w32 | 82. | 4236 2019w33 | 83. | 3622 2019w34 | 84. | 3540 2019w35 | 85. | 3809 2019w36 | |-----------------| 86. | 4043 2019w37 | 87. | 4520 2019w38 | 88. | 4318 2019w39 | 89. | 4357 2019w40 | 90. | 4565 2019w41 | |-----------------| 91. | 5542 2019w42 | 92. | 4975 2019w43 | +-----------------+
Time series plotBasic statistics:- 50% of observed weeks ( 92 weeks) have total intra-week merits above 4506, whilst the rest 50% of them have total intra-week merits below 4506. 4506 is the median - p50. - 50% of observed weeks have total intra-week merits fluctuated in the range from 4027 to 4970 (the interquartile range, from p25 to p75, in raw statistics below). - Min - max: 3072 - 30960. . tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- merit | 92.0 5302.3 3513.3 4505.5 4027.0 4970.0 3072.0 30960.0 ----------------------------------------------------------------------------------------------
Potential outliers:. di 4970-4027 943
. di 943*1.5 1414.5
. di 4970+1414.5 6384.5
. di 4027-1414.5 2612.5
It means that potential outliers are weeks that have intra-week merits beyond 2613 or 6385. How many weeks are potential outliers? . count if (merit >= 6385 | merit < 2613) & merit != . 13
13 weeks are outliers, in total. List of those thirteen weeks: . list merit week if merit >= 6385 | merit <= 2613
+-----------------+ | merit week | |-----------------| 1. | 30960 2018w4 | 2. | 19979 2018w5 | 3. | 13313 2018w6 | 4. | 11745 2018w7 | 5. | 8767 2018w8 | |-----------------| 6. | 8833 2018w9 | 7. | 7261 2018w10 | 8. | 7317 2018w11 | 9. | 6952 2018w12 | 10. | 6744 2018w13 | |-----------------| 11. | 6423 2018w14 | 35. | 7837 2018w38 | 51. | 6632 2019w2 | +-----------------+
Most of them occured in the year 2018, and there is only one outlier week occured in 2019, in 2019w2 at 6632. ![Grin](https://bitcointalk.org/Smileys/default/grin.gif)
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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 679, 654, and 652, respectively; whislt the lowest days are Friday, Saturday, and Sunday at 586, 591, and 609, respectively. - In means, the highest days are Monday, Wednesday, and Tuesday, at 728, 696, and 685, respectively; whilst the lowest days are Friday, Saturday, and Sunday, at 610, 611, and 665, 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. Basic statistics:. 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 | 88.0 664.6 269.9 608.5 508.5 749.5 394.0 2464.0 Monday | 89.0 727.5 255.5 679.0 566.0 797.0 313.0 1863.0 Tuesday | 88.0 684.4 195.5 639.5 583.5 733.0 384.0 1327.0 Wednesday | 88.0 695.9 197.5 654.0 558.5 761.0 394.0 1271.0 Thursday | 88.0 678.4 189.9 651.5 537.0 767.5 348.0 1335.0 Friday | 88.0 609.7 187.4 585.5 500.0 663.5 349.0 1706.0 Saturday | 88.0 610.9 195.2 590.5 476.0 685.5 295.0 1410.0 ----------+-------------------------------------------------------------------------------- Total | 617.0 667.4 218.1 627.0 529.0 742.0 295.0 2464.0 -------------------------------------------------------------------------------------------
Box plotsOutliers displayed as red circles. Outliers non-displayed.
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During the period from 24/1/2018 to 28/10/2019 (last two days dropped due to incomplete week), the minimum and maximum of intra-day merit are 295 and 13018, on 03/8/2019 and 24/1/2018, respectively. List of the top 50-highest day in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+ | merit id date dofw day month2 year week month | |-------------------------------------------------------------------------------| 1. | 13018 1 24jan2018 Wednesday 24 1 2018 2018w4 2018m1 | 2. | 6762 2 25jan2018 Thursday 25 1 2018 2018w4 2018m1 | 3. | 4500 3 26jan2018 Friday 26 1 2018 2018w4 2018m1 | 4. | 4193 7 30jan2018 Tuesday 30 1 2018 2018w5 2018m1 | 5. | 3800 6 29jan2018 Monday 29 1 2018 2018w5 2018m1 | |-------------------------------------------------------------------------------| 6. | 3490 4 27jan2018 Saturday 27 1 2018 2018w4 2018m1 | 7. | 3190 5 28jan2018 Sunday 28 1 2018 2018w4 2018m1 | 8. | 2821 8 31jan2018 Wednesday 31 1 2018 2018w5 2018m1 | 9. | 2569 10 02feb2018 Friday 2 2 2018 2018w5 2018m2 | 10. | 2546 9 01feb2018 Thursday 1 2 2018 2018w5 2018m2 | |-------------------------------------------------------------------------------| 11. | 2514 22 14feb2018 Wednesday 14 2 2018 2018w7 2018m2 | 12. | 2464 236 16sep2018 Sunday 16 9 2018 2018w37 2018m9 | 13. | 2310 14 06feb2018 Tuesday 6 2 2018 2018w6 2018m2 | 14. | 2182 12 04feb2018 Sunday 4 2 2018 2018w5 2018m2 | 15. | 2143 16 08feb2018 Thursday 8 2 2018 2018w6 2018m2 | |-------------------------------------------------------------------------------| 16. | 2142 15 07feb2018 Wednesday 7 2 2018 2018w6 2018m2 | 17. | 2078 13 05feb2018 Monday 5 2 2018 2018w6 2018m2 | 18. | 1992 23 15feb2018 Thursday 15 2 2018 2018w7 2018m2 | 19. | 1868 11 03feb2018 Saturday 3 2 2018 2018w5 2018m2 | 20. | 1863 237 17sep2018 Monday 17 9 2018 2018w38 2018m9 | |-------------------------------------------------------------------------------| 21. | 1748 18 10feb2018 Saturday 10 2 2018 2018w6 2018m2 | 22. | 1706 38 02mar2018 Friday 2 3 2018 2018w9 2018m3 | 23. | 1618 25 17feb2018 Saturday 17 2 2018 2018w7 2018m2 | 24. | 1580 21 13feb2018 Tuesday 13 2 2018 2018w7 2018m2 | 25. | 1449 17 09feb2018 Friday 9 2 2018 2018w6 2018m2 | |-------------------------------------------------------------------------------| 26. | 1443 19 11feb2018 Sunday 11 2 2018 2018w6 2018m2 | 27. | 1416 24 16feb2018 Friday 16 2 2018 2018w7 2018m2 | 28. | 1410 32 24feb2018 Saturday 24 2 2018 2018w8 2018m2 | 29. | 1404 27 19feb2018 Monday 19 2 2018 2018w8 2018m2 | 30. | 1392 34 26feb2018 Monday 26 2 2018 2018w9 2018m2 | |-------------------------------------------------------------------------------| 31. | 1355 48 12mar2018 Monday 12 3 2018 2018w11 2018m3 | 32. | 1335 37 01mar2018 Thursday 1 3 2018 2018w9 2018m3 | 33. | 1332 20 12feb2018 Monday 12 2 2018 2018w7 2018m2 | 34. | 1327 35 27feb2018 Tuesday 27 2 2018 2018w9 2018m2 | 35. | 1324 56 20mar2018 Tuesday 20 3 2018 2018w12 2018m3 | |-------------------------------------------------------------------------------| 36. | 1295 238 18sep2018 Tuesday 18 9 2018 2018w38 2018m9 | 37. | 1293 26 18feb2018 Sunday 18 2 2018 2018w7 2018m2 | 38. | 1280 30 22feb2018 Thursday 22 2 2018 2018w8 2018m2 | 39. | 1271 239 19sep2018 Wednesday 19 9 2018 2018w38 2018m9 | 40. | 1268 29 21feb2018 Wednesday 21 2 2018 2018w8 2018m2 | |-------------------------------------------------------------------------------| 41. | 1258 68 01apr2018 Sunday 1 4 2018 2018w13 2018m4 | 42. | 1250 428 27mar2019 Wednesday 27 3 2019 2019w13 2019m3 | 43. | 1246 41 05mar2018 Monday 5 3 2018 2018w10 2018m3 | 44. | 1229 57 21mar2018 Wednesday 21 3 2018 2018w12 2018m3 | 45. | 1188 504 11jun2019 Tuesday 11 6 2019 2019w24 2019m6 | |-------------------------------------------------------------------------------| 46. | 1187 33 25feb2018 Sunday 25 2 2018 2018w8 2018m2 | 47. | 1170 28 20feb2018 Tuesday 20 2 2018 2018w8 2018m2 | 48. | 1162 351 09jan2019 Wednesday 9 1 2019 2019w2 2019m1 | 49. | 1160 50 14mar2018 Wednesday 14 3 2018 2018w11 2018m3 | 50. | 1151 475 13may2019 Monday 13 5 2019 2019w19 2019m5 | |-------------------------------------------------------------------------------|
List of the top 50-lowest days in terms of intra-day merits: . list merit id date dofw day month2 year week month
+-------------------------------------------------------------------------------+ | merit id date dofw day month2 year week month | |-------------------------------------------------------------------------------| 1. | 295 557 03aug2019 Saturday 3 8 2019 2019w31 2019m8 | 2. | 313 335 24dec2018 Monday 24 12 2018 2018w52 2018m12 | 3. | 317 333 22dec2018 Saturday 22 12 2018 2018w51 2018m12 | 4. | 328 564 10aug2019 Saturday 10 8 2019 2019w32 2019m8 | 5. | 344 340 29dec2018 Saturday 29 12 2018 2018w52 2018m12 | |-------------------------------------------------------------------------------| 6. | 348 298 17nov2018 Saturday 17 11 2018 2018w46 2018m11 | 7. | 348 338 27dec2018 Thursday 27 12 2018 2018w52 2018m12 | 8. | 349 304 23nov2018 Friday 23 11 2018 2018w47 2018m11 | 9. | 368 566 12aug2019 Monday 12 8 2019 2019w32 2019m8 | 10. | 371 122 25may2018 Friday 25 5 2018 2018w21 2018m5 | |-------------------------------------------------------------------------------| 11. | 377 191 02aug2018 Thursday 2 8 2018 2018w31 2018m8 | 12. | 377 342 31dec2018 Monday 31 12 2018 2018w52 2018m12 | 13. | 379 326 15dec2018 Saturday 15 12 2018 2018w50 2018m12 | 14. | 380 220 31aug2018 Friday 31 8 2018 2018w35 2018m8 | 15. | 382 599 14sep2019 Saturday 14 9 2019 2019w37 2019m9 | |-------------------------------------------------------------------------------| 16. | 384 217 28aug2018 Tuesday 28 8 2018 2018w35 2018m8 | 17. | 386 214 25aug2018 Saturday 25 8 2018 2018w34 2018m8 | 18. | 387 339 28dec2018 Friday 28 12 2018 2018w52 2018m12 | 19. | 394 341 30dec2018 Sunday 30 12 2018 2018w52 2018m12 | 20. | 394 568 14aug2019 Wednesday 14 8 2019 2019w33 2019m8 | |-------------------------------------------------------------------------------| 21. | 395 529 06jul2019 Saturday 6 7 2019 2019w27 2019m7 | 22. | 395 345 03jan2019 Thursday 3 1 2019 2019w1 2019m1 | 23. | 396 228 08sep2018 Saturday 8 9 2018 2018w36 2018m9 | 24. | 398 320 09dec2018 Sunday 9 12 2018 2018w49 2018m12 | 25. | 399 558 04aug2019 Sunday 4 8 2019 2019w31 2019m8 | |-------------------------------------------------------------------------------| 26. | 400 262 12oct2018 Friday 12 10 2018 2018w41 2018m10 | 27. | 403 329 18dec2018 Tuesday 18 12 2018 2018w51 2018m12 | 28. | 406 287 06nov2018 Tuesday 6 11 2018 2018w45 2018m11 | 29. | 407 556 02aug2019 Friday 2 8 2019 2019w31 2019m8 | 30. | 411 565 11aug2019 Sunday 11 8 2019 2019w32 2019m8 | |-------------------------------------------------------------------------------| 31. | 413 222 02sep2018 Sunday 2 9 2018 2018w35 2018m9 | 32. | 413 403 02mar2019 Saturday 2 3 2019 2019w9 2019m3 | 33. | 414 527 04jul2019 Thursday 4 7 2019 2019w27 2019m7 | 34. | 416 278 28oct2018 Sunday 28 10 2018 2018w43 2018m10 | 35. | 416 588 03sep2019 Tuesday 3 9 2019 2019w36 2019m9 | |-------------------------------------------------------------------------------| 36. | 416 533 10jul2019 Wednesday 10 7 2019 2019w28 2019m7 | 37. | 417 109 12may2018 Saturday 12 5 2018 2018w19 2018m5 | 38. | 417 587 02sep2019 Monday 2 9 2019 2019w35 2019m9 | 39. | 417 592 07sep2019 Saturday 7 9 2019 2019w36 2019m9 | 40. | 419 186 28jul2018 Saturday 28 7 2018 2018w30 2018m7 | |-------------------------------------------------------------------------------| 41. | 421 187 29jul2018 Sunday 29 7 2018 2018w30 2018m7 | 42. | 422 192 03aug2018 Friday 3 8 2018 2018w31 2018m8 | 43. | 425 276 26oct2018 Friday 26 10 2018 2018w43 2018m10 | 44. | 427 277 27oct2018 Saturday 27 10 2018 2018w43 2018m10 | 45. | 427 140 12jun2018 Tuesday 12 6 2018 2018w24 2018m6 | |-------------------------------------------------------------------------------| 46. | 429 313 02dec2018 Sunday 2 12 2018 2018w48 2018m12 | 47. | 429 418 17mar2019 Sunday 17 3 2019 2019w11 2019m3 | 48. | 431 284 03nov2018 Saturday 3 11 2018 2018w44 2018m11 | 49. | 431 264 14oct2018 Sunday 14 10 2018 2018w41 2018m10 | 50. | 433 208 19aug2018 Sunday 19 8 2018 2018w33 2018m8 | |-------------------------------------------------------------------------------|
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Time-series plots:Full dataset:Truncated dataset: Basic statistics:- Last two days dropped due to incomple week (2019w44)Full dataset (only dropped first three days):. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- merit | 641.0 730.2 420.7 634.0 532.0 766.0 295.0 4500.0 ----------------------------------------------------------------------------------------------
Applied formulas in previous weeks, potential outliers are days have intra-day merits beyond 179 or 1119. . di 766-532 234
. di 234*1.5 351
. di 766+351 1117
. di 532-351 181
There are 52 outliers (beyond 1117 or 181) in full dataset, in total. . count if (merit >= 1117 | merit <= 181) & merit != . 52
Those days are: . list id merit date if (merit >= 1117 | merit <= 181) & merit != .
+-------------------------+ | id merit date | |-------------------------| 1. | 3 4500 26jan2018 | 2. | 4 3490 27jan2018 | 3. | 5 3190 28jan2018 | 4. | 6 3800 29jan2018 | 5. | 7 4193 30jan2018 | |-------------------------| 6. | 8 2821 31jan2018 | 7. | 9 2546 01feb2018 | 8. | 10 2569 02feb2018 | 9. | 11 1868 03feb2018 | 10. | 12 2182 04feb2018 | |-------------------------| 11. | 13 2078 05feb2018 | 12. | 14 2310 06feb2018 | 13. | 15 2142 07feb2018 | 14. | 16 2143 08feb2018 | 15. | 17 1449 09feb2018 | |-------------------------| 16. | 18 1748 10feb2018 | 17. | 19 1443 11feb2018 | 18. | 20 1332 12feb2018 | 19. | 21 1580 13feb2018 | 20. | 22 2514 14feb2018 | |-------------------------| 21. | 23 1992 15feb2018 | 22. | 24 1416 16feb2018 | 23. | 25 1618 17feb2018 | 24. | 26 1293 18feb2018 | 25. | 27 1404 19feb2018 | |-------------------------| 26. | 28 1170 20feb2018 | 27. | 29 1268 21feb2018 | 28. | 30 1280 22feb2018 | 30. | 32 1410 24feb2018 | 31. | 33 1187 25feb2018 | |-------------------------| 32. | 34 1392 26feb2018 | 33. | 35 1327 27feb2018 | 35. | 37 1335 01mar2018 | 36. | 38 1706 02mar2018 | 39. | 41 1246 05mar2018 | |-------------------------| 46. | 48 1355 12mar2018 | 48. | 50 1160 14mar2018 | 49. | 51 1131 15mar2018 | 54. | 56 1324 20mar2018 | 55. | 57 1229 21mar2018 | |-------------------------| 66. | 68 1258 01apr2018 | 67. | 69 1147 02apr2018 | 151. | 153 1139 25jun2018 | 234. | 236 2464 16sep2018 | 235. | 237 1863 17sep2018 | |-------------------------| 236. | 238 1295 18sep2018 | 237. | 239 1271 19sep2018 | 349. | 351 1162 09jan2019 | 354. | 356 1128 14jan2019 | 426. | 428 1250 27mar2019 | |-------------------------| 473. | 475 1151 13may2019 | 502. | 504 1188 11jun2019 | +-------------------------+
Only five of them occured in 2019, on 09/1/2019 (1162), 14/1/2019 (1128), 27/3/2019 (1250), 13/5/2019 (1151), and 11/6/2019 (1188). Note that the latest spike on 21/10/2019 at 1082 merits changed hands is closely to range of potential outliers. Truncated dataset (first 25 days dropped):. tabstat merit, s(n mean sd p50 p25 p75 min max) format(%9.1f)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- merit | 617.0 667.4 218.1 627.0 529.0 742.0 295.0 2464.0 ----------------------------------------------------------------------------------------------
Applied same formulas I used in earlier analyses, potential outliers are days have intra-day merits beyond 210 or 1062. . di 742-529 213
. di 213*1.5 319.5
. di 742+319.5 1061.5
. di 529-319.5 209.5
There are 36 outliers in total, only seven of them occured in 2019, on 04/01/2019 (1083) , 09/1/2019 (1162), 14/01/2019 (1128) , 27/3/2019 (1250), 13/5/2019 (1151), 11/6/2019 (1188), and 21/10/2019 (1082). . count if (merit >= 1062 | merit <= 210) & merit != . 36
List of those 36 outliers in truncated dataset . list id merit date if (merit >= 1062 | merit <= 210) & merit != .
+-------------------------+ | id merit date | |-------------------------| 1. | 27 1404 19feb2018 | 2. | 28 1170 20feb2018 | 3. | 29 1268 21feb2018 | 4. | 30 1280 22feb2018 | 6. | 32 1410 24feb2018 | |-------------------------| 7. | 33 1187 25feb2018 | 8. | 34 1392 26feb2018 | 9. | 35 1327 27feb2018 | 11. | 37 1335 01mar2018 | 12. | 38 1706 02mar2018 | |-------------------------| 13. | 39 1090 03mar2018 | 15. | 41 1246 05mar2018 | 16. | 42 1075 06mar2018 | 17. | 43 1111 07mar2018 | 21. | 47 1092 11mar2018 | |-------------------------| 22. | 48 1355 12mar2018 | 24. | 50 1160 14mar2018 | 25. | 51 1131 15mar2018 | 30. | 56 1324 20mar2018 | 31. | 57 1229 21mar2018 | |-------------------------| 42. | 68 1258 01apr2018 | 43. | 69 1147 02apr2018 | 44. | 70 1081 03apr2018 | 45. | 71 1062 04apr2018 | 127. | 153 1139 25jun2018 | |-------------------------| 210. | 236 2464 16sep2018 | 211. | 237 1863 17sep2018 | 212. | 238 1295 18sep2018 | 213. | 239 1271 19sep2018 | 320. | 346 1083 04jan2019 | |-------------------------| 325. | 351 1162 09jan2019 | 330. | 356 1128 14jan2019 | 402. | 428 1250 27mar2019 | 449. | 475 1151 13may2019 | 478. | 504 1188 11jun2019 | |-------------------------| 610. | 636 1082 21oct2019 | +-------------------------+
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Update:ABSTRACT- Median (interquartile range) of weekly earned merits for all 100 top merited profiles is 8 (2 - 18). It means they usually earn around 1 merit per day.
- Median (interquartile range) of weekly earned merits for the group of 1-25 is 12 (4 - 28), that is significantly higher than the figures of last 3 groups: 26-50 ( 8 ), 51-75 (7), and 76-100 (7).
- Min - max: 0 and 129, respectively.
- Period: 2019w23 - 2019w43
- Weekly earned merits: are not exacly weekly in Calendar day; and come from theymos' merit data dumps and LoyceV's data update. See
- All users in the top 100: 8 merits per week, with interquartile range is 2 to 18. It means if someone can manage to earn more than 7 merits per week, over months, they might move to the top months later.
- The top 1-25: 12 merits per week, that is 50-percent higher than the the second group (top 26-50) at 8. The rest two groups (top 51-75, and top 76-100) have same median at 7. (see details below)
- In median, top 26-100 merited users earn a little more than1 merit per day
![Cheesy](https://bitcointalk.org/Smileys/default/cheesy.gif)
Data for last week (2019w43) Source: https://bitcointalk.org/index.php?topic=5115154.msg52956801#msg52956801. list id username m43 c43
+-------------------------------------------+ | id username m43 c43 | |-------------------------------------------| 1. | 1 theymos 5582 98 | 2. | 2 LoyceV 3961 26 | 3. | 3 suchmoon 3222 36 | 4. | 4 DdmrDdmr 2797 11 | 5. | 5 o_e_l_e_o 2823 12 | |-------------------------------------------| 6. | 6 micgoossens 2692 40 | 7. | 7 The Pharmacist 2142 14 | 8. | 8 satoshi 2040 43 | 9. | 9 Last of the V8s 1916 16 | 10. | 10 achow101 1881 7 | |-------------------------------------------| 11. | 11 hilariousetc 1748 1 | 12. | 12 gmaxwell 1591 5 | 13. | 13 1miau 1423 4 | 14. | 14 abhiseshakana 1379 3 | 15. | 15 HairyMaclairy 1394 4 | |-------------------------------------------| 16. | 16 Vod 1313 2 | 17. | 17 HCP 1341 2 | 18. | 18 xhomerx10 1374 1 | 19. | 19 nutildah 1421 35 | 20. | 20 bob123 1320 2 | |-------------------------------------------| 21. | 21 xtraelv 1288 18 | 22. | 22 Jet Cash 1280 1 | 23. | 23 krogothmanhattan 1280 11 | 24. | 24 Hhampuz 1236 12 | 25. | 25 iasenko 1275 14 | |-------------------------------------------| 26. | 26 mikeywith 1375 22 | 27. | 27 qwk 1174 3 | 28. | 28 marlboroza 1151 0 | 29. | 29 Piggy 1135 0 | 30. | 30 Lauda 1180 17 | |-------------------------------------------| 31. | 31 fillippone 1517 49 | 32. | 32 joniboni 1147 2 | 33. | 33 Steamtyme 1319 52 | 34. | 34 LFC_Bitcoin 1223 34 | 35. | 35 TMAN 1056 2 | |-------------------------------------------| 36. | 36 coinlocket$ 1099 0 | 37. | 37 ETFbitcoin 1065 7 | 38. | 38 bitmover 1079 6 | 39. | 39 BitCryptex 1058 15 | 40. | 40 JayJuanGee 1074 4 | |-------------------------------------------| 41. | 41 BobLawblaw 1056 8 | 42. | 42 roycilik 996 0 | 43. | 43 Toxic2040 985 0 | 44. | 44 DarkStar_ 1044 4 | 45. | 45 TryNinja 1085 10 | |-------------------------------------------| 46. | 46 gentlemand 1064 8 | 47. | 47 SaltySpitoon 990 1 | 48. | 48 mu_enrico 1012 4 | 49. | 49 theyoungmillionaire 957 25 | 50. | 50 VB1001 1068 9 | |-------------------------------------------| 51. | 51 Alex_Sr 898 0 | 52. | 52 taikuri13 987 5 | 53. | 53 morillz7z 986 8 | 54. | 54 philipma1957 947 2 | 55. | 55 ICOEthics 877 0 | |-------------------------------------------| 56. | 56 Carlton Banks 1015 11 | 57. | 57 pooya87 999 11 | 58. | 58 Husna QA 892 7 | 59. | 59 minerjones 896 3 | 60. | 60 jojo69 906 8 | |-------------------------------------------| 61. | 61 kenzawak 841 0 | 62. | 62 Veleor 1077 21 | 63. | 63 pandukelana2712 861 6 | 64. | 64 Lutpin 817 1 | 65. | 65 Xal0lex 935 20 | |-------------------------------------------| 66. | 66 PHI16168 795 0 | 67. | 67 Coolcryptovator 897 20 | 68. | 68 nullius 783 0 | 69. | 69 TheNewAnon135246 794 1 | 70. | 70 yogg 909 61 | |-------------------------------------------| 71. | 71 infofront 853 3 | 72. | 72 Coding Enthusiast 885 3 | 73. | 73 CryptopreneurBrainboss 843 13 | 74. | 74 Quickseller 760 1 | 75. | 75 DireWolfM14 821 14 | |-------------------------------------------| 76. | 76 loyvesmayfamilis 855 3 | 77. | 77 actmyame 813 17 | 78. | 78 mocacino 807 0 | 79. | 79 OgNasty 728 0 | 80. | 80 asche 767 17 | |-------------------------------------------| 81. | 81 Goran_ 776 10 | 82. | 82 Flying Hellfish 705 0 | 83. | 83 witcher_sense 837 27 | 84. | 84 bones261 706 0 | 85. | 85 mole0815 727 0 | |-------------------------------------------| 86. | 86 LeGaulois 698 4 | 87. | 87 Lafu 704 1 | 88. | 88 yahoo62278 926 35 | 89. | 89 hilariousandco 677 7 | 90. | 90 Pmalek 662 2 | |-------------------------------------------| 91. | 91 tvplus006 729 6 | 92. | 92 HeRetiK 632 3 | 93. | 93 wwzsocki 788 6 | 94. | 94 stompix 680 1 | 95. | 95 Artemis3 643 0 | |-------------------------------------------| 96. | 96 tranthidung 769 14 | 97. | 97 TheFuzzStone 681 15 | 98. | 98 chimk 677 7 | 99. | 99 Coin-1 630 5 | 100. | 100 mjglqw 649 5 | +-------------------------------------------+
Statistics:- For all 100 users: - Mean +/- standard deviation: 12.8 +/- 15.3
- Median (interquartile range): 8 (2 - 18)
Details: . tabstat meritchange , s(n mean sd p50 p25 p75 min max)
variable | N mean sd p50 p25 p75 min max -------------+-------------------------------------------------------------------------------- meritchange | 2000 12.754 15.30273 8 2 18 0 129 ----------------------------------------------------------------------------------------------
For each users (see details in below table). In median(interquartile range): the top 5 (just for last 20 weeks) are: - LoyceV: 48 (25 - 61)
- fillippone: 48 (33 - 64)
- o_e_l_e_o: 46 (30 - 56)
- suchmoon: 36 (22 - 49)
- nutildah: 34 (24 - 44)
There is no outliers found in the top 5. It means their weekly earned merits are very very stable. ![Shocked](https://bitcointalk.org/Smileys/default/shocked.gif) . tabstat meritchange , s(n mean sd p50 p25 p75 min max) by(username)
Summary for variables: meritchange by categories of: username (username)
username | N mean sd p50 p25 p75 min max -----------------+-------------------------------------------------------------------------------- 1miau | 20 21.85 22.07231 13.5 9.5 27 4 100 Alex_Sr | 20 4.4 5.144235 2.5 0 7.5 0 18 Artemis3 | 20 6.4 5.305409 5.5 2 9.5 0 19 BitCryptex | 20 15.15 8.821475 15.5 7 20.5 2 30 BobLawblaw | 20 6.3 6.283646 4.5 2 9 0 27 Carlton Banks | 20 16.7 12.72006 12 9.5 18.5 7 60 Coding Enthusias | 20 12.95 11.76737 9.5 4.5 20.5 0 45 Coin-1 | 20 7.2 5.763771 5.5 4 9 0 23 Coolcryptovator | 20 8.65 7.073114 8 2 12.5 0 25 CryptopreneurBra | 20 16.4 15.73263 13.5 10 16.5 3 79 DarkStar_ | 20 11.35 10.81556 8 4 15 2 45 DdmrDdmr | 20 28.2 16.12321 23 18 35 5 67 DireWolfM14 | 20 11.6 10.51515 9 5 16 0 43 ETFbitcoin | 20 13.35 10.73742 10 7 16.5 1 47 Flying Hellfish | 20 4.9 8.490862 1 0 4.5 0 26 Goran_ | 20 9.1 5.543132 9 5.5 10.5 0 21 HCP | 20 11.45 7.823547 10 6 17.5 1 26 HairyMaclairy | 20 12.55 9.236512 10.5 8 16 0 42 HeRetiK | 20 3.9 4.506136 2.5 1.5 4.5 0 20 Hhampuz | 20 5.85 8.530287 3 1 6 0 36 Husna QA | 20 5.3 4.268612 4.5 2.5 7 0 19 ICOEthics | 20 1.25 2.291288 0 0 1 0 8 JayJuanGee | 20 14.35 11.65863 13 7 18 3 57 Jet Cash | 20 6.1 4.435503 6 2.5 9 0 16 LFC_Bitcoin | 20 23.3 9.370165 21.5 16 28.5 10 48 Lafu | 20 7 5.321258 6 2.5 9 1 19 Last of the V8s | 20 11.15 11.41225 7 .5 21.5 0 31 Lauda | 20 5.55 6.244787 3.5 .5 9 0 21 LeGaulois | 20 4.05 3.953346 3.5 2 4.5 0 18 LoyceV | 20 45.5 23.8471 47.5 25 60.5 16 110 Lutpin | 20 1.95 2.665076 1 0 4 0 7 OgNasty | 20 1.9 1.803505 1.5 .5 3 0 7 PHI16168 | 20 2.65 4.965937 .5 0 2.5 0 17 Piggy | 20 1.9 2.268781 1 0 3 0 7 Pmalek | 20 5.75 4.940435 3.5 2 10 0 17 Quickseller | 20 4.55 5.595816 2.5 0 6.5 0 18 SaltySpitoon | 20 8.1 8.01249 4.5 1.5 13 0 27 Steamtyme | 20 18.4 28.7684 8 5 16 2 129 TMAN | 20 1.4 1.759186 1 0 2 0 6 The Pharmacist | 20 9 7.326951 8 4.5 10 0 29 TheFuzzStone | 20 14.05 14.31405 11.5 7 14.5 0 53 TheNewAnon135246 | 20 4.25 4.011497 3 1 7.5 0 15 Toxic2040 | 20 6.3 10.14163 1 0 8.5 0 36 TryNinja | 20 14.6 8.635301 13 9.5 20 0 32 VB1001 | 20 24.25 12.78105 23.5 16 33 5 54 Veleor | 20 22.25 20.49358 19 11 23.5 2 81 Vod | 20 3.05 3.69174 2 1 4 0 14 Xal0lex | 20 13.9 10.93473 9 6 20.5 2 38 abhiseshakana | 20 9.4 6.012268 8 5 14.5 0 20 achow101 | 20 21.3 15.11047 17 9.5 29.5 1 60 actmyame | 20 12.2 8.587629 10 7 17.5 0 32 asche | 20 7.9 7.122241 6.5 3.5 9.5 1 33 bitmover | 20 17.45 13.80875 17 8 21 4 68 bob123 | 20 24.9 15.85759 24 12 37 0 56 bones261 | 20 5.8 7.501579 3.5 1 8 0 27 chimk | 20 11.55 7.141244 10.5 5.5 15 3 30 coinlocket$ | 20 3.15 5.733328 2 0 2.5 0 25 fillippone | 20 51.35 21.80361 47.5 32.5 63.5 23 113 gentlemand | 20 16.95 7.258208 15 11 23 7 31 gmaxwell | 20 14.55 25.43614 5.5 1.5 16.5 0 113 hilariousandco | 20 4.35 4.847951 2 1 6.5 0 18 hilariousetc | 20 7.45 9.242209 3.5 1 12 0 30 iasenko | 20 12.6 12.1022 9 2 20 0 40 infofront | 20 7.05 16.6843 2 0 5 0 72 jojo69 | 20 7.4 4.946024 6.5 4 10 1 18 joniboni | 20 5.35 4.704701 5 1 9 0 17 kenzawak | 20 3.5 6.755115 0 0 4 0 23 krogothmanhattan | 20 8.7 7.602631 6.5 3 10.5 0 28 loyvesmayfamilis | 20 19.2 16.03811 21 3.5 30 0 49 marlboroza | 20 11.45 13.31985 6 2.5 15 0 48 micgoossens | 20 31.95 12.87378 28 23.5 39 16 62 mikeywith | 20 23.85 9.443879 26.5 17.5 29.5 8 45 minerjones | 20 9.55 6.954552 10.5 2.5 14.5 1 23 mjglqw | 20 10.95 7.570545 11.5 4.5 16.5 0 24 mocacino | 20 10 7.840516 7.5 5 15.5 0 28 mole0815 | 20 5.25 8.607677 2 1 6 0 34 morillz7z | 20 16.8 9.844047 15 9.5 21 4 39 mu_enrico | 20 9.3 5.9921 7 4.5 15 1 23 nullius | 20 .35 .7451598 0 0 .5 0 3 nutildah | 20 34.4 15.91887 34 23.5 43.5 4 66 o_e_l_e_o | 20 43.05 18.42331 46 29.5 55.5 12 82 pandukelana2712 | 20 6.15 6.417698 6 1.5 7 0 25 philipma1957 | 20 9.95 6.476476 9 5.5 12.5 2 26 pooya87 | 20 15.8 6.444908 14 11 19.5 7 29 qwk | 20 2.9 2.845125 2 1 4 0 10 roycilik | 20 5.9 9.256008 1.5 1 7 0 35 satoshi | 20 13.15 19.71714 3 1 18.5 0 71 stompix | 20 7.1 5.802903 5.5 2 11.5 1 20 suchmoon | 20 38.85 21.0645 36 21.5 49 10 87 taikuri13 | 20 15.55 7.910519 15 9 20.5 5 34 theymos | 20 34.05 32.67137 22.5 10.5 42.5 1 119 theyoungmilliona | 20 4.25 7.253856 .5 0 4.5 0 25 tranthidung | 20 21.9 22.20692 13.5 10.5 28 5 104 tvplus006 | 20 13.5 10.64004 10.5 6.5 21.5 0 37 witcher_sense | 20 24.35 15.84888 21.5 13.5 31.5 3 61 wwzsocki | 20 19.2 18.4151 14 5 23 0 78 xhomerx10 | 20 10.15 8.69528 9 4 13.5 0 36 xtraelv | 20 10.5 9.621795 8.5 2.5 17.5 0 32 yahoo62278 | 20 19.65 25.14647 11.5 7 23 0 112 yogg | 20 13.45 13.49259 11 4 17.5 0 61 -----------------+-------------------------------------------------------------------------------- Total | 2000 12.754 15.30273 8 2 18 0 129 --------------------------------------------------------------------------------------------------
For four groups of the top 100 merited users:. tabstat meritchange , s(n mean sd p50 p25 p75 min max) by(group)
Summary for variables: meritchange by categories of: group
group | N mean sd p50 p25 p75 min max -------+-------------------------------------------------------------------------------- 1-25 | 500 18.788 19.6145 12 4 28 0 119 26-50 | 500 12.648 15.0405 8 2 18 0 129 51-75 | 500 9.294 10.92952 7 1 13 0 81 76-100 | 500 10.286 12.37378 7 2 13 0 112 -------+-------------------------------------------------------------------------------- Total | 2000 12.754 15.30273 8 2 18 0 129 ----------------------------------------------------------------------------------------
Box plots:For all top 100 merited users: For 4 groups of top 100 merited users: Over each users (outliers displayed with red circles):
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