An Analytical Approach to the Best and Worst Poasters
Comments
-
NeitherFenderbender123 said:Is it better to make 10 posts that get 10 chincredibles each, or 2 posts that get 15 chincredibles each?
It's better to LEAVE! -
Apparently making 6000 that get 3 chins and a flag from boobs each gets you 56th place.Fenderbender123 said:Is it better to make 10 posts that get 10 chincredibles each, or 2 posts that get 15 chincredibles each?
-
Fenderbender123 said:
Is it better to make 10 posts that get 10 chincredibles each, or 2 posts that get 15 chincredibles each?
UW_Doog_Bot said:
Best Poasters (Combined Chin/WTF Ratio)
Worst Poasters
Most Rec'd (Per Poast)
Most Anti-Rec'd (Per Poast)
Most Controversial(Most Rec's & Anti-Rec's) -
Woohoo1!!!1! We're all winners!UW_Doog_Bot said:
I updated the sheet to include @ExtraChrisB @PurpleReign @chuck @uw2010 @DoubleJDawg @BennyBeaver @ThomasFremont and @dflea . Congratulations!!!!!UW_Doog_Bot said:
YOU ARE ALL
With the exception of @uw2010 who snuck into the top five but also had the ignominious distinction of also being the most benign poaster. -
The only real flaw in this analysis is that chin's haven't always existed (nor have their predecessors "loves" I believe). There was a tim long ago on this here bored where the best response you could give to a poast you appreciated was an upvote. This almost assuredly suppresses the more tenured members of the bored's ratios.
Of course, none of this matters so I wouldn't waste 53 seconds trying to figure out a workaround, just thought it was chintriguing and chinteresting chinformation. -
This website would go bankrupt without my annual $25 donation.dnc said:
It's better to LEAVE!Fenderbender123 said:Is it better to make 10 posts that get 10 chincredibles each, or 2 posts that get 15 chincredibles each?
-
Hthdnc said:none of this matters
-
Nothing special.chuck said:
I'm not an alt:UW_Doog_Bot said:
1600 posts.
2600 upvotes
1100 Chins
80 down
55 wtf
I think that could have gotten me on the list, but fuck off. -
This flipside to this coin is that it takes a while to build a brand, so the old guard has more name brand recognition which leads to more chins and up votes.dnc said:The only real flaw in this analysis is that chin's haven't always existed (nor have their predecessors "loves" I believe). There was a tim long ago on this here bored where the best response you could give to a poast you appreciated was an upvote. This almost assuredly suppresses the more tenured members of the bored's ratios.
Of course, none of this matters so I wouldn't waste 53 seconds trying to figure out a workaround, just thought it was chintriguing and chinteresting chinformation. -
In statistics given a large enough sample we can assume that white noise is zero.YellowSnow said:
This flipside to this coin is that it takes a while to build a brand, so the old guard has more name brand recognition which leads to more chins and up votes.dnc said:The only real flaw in this analysis is that chin's haven't always existed (nor have their predecessors "loves" I believe). There was a tim long ago on this here bored where the best response you could give to a poast you appreciated was an upvote. This almost assuredly suppresses the more tenured members of the bored's ratios.
Of course, none of this matters so I wouldn't waste 53 seconds trying to figure out a workaround, just thought it was chintriguing and chinteresting chinformation.
ISSUE: What is white noise?
WHITE NOISE: White noise is defined as the error term of a time series model distributed in the Gauss-Markov process in time series data set. Given a time series data where the Y is produced in a form of Yi: (y1, y2, …, y3) in a time series: ti:( (t1, t2, …, T); this time series event is denoted as yt or X(t). The model is given as:
(1) yt = Bo + B1Xt + ei
The focus of white noise is on the term ei in the equation. The ei is a set of ei: (e1, e2, …, eT) generated by each time series event. These elements of ei have the following three properties: identical, independent and mean zero distribution, i.e. N(0, var). In order to be white noise, the ei process must have the following characteristics:
(2) E(ei) = 0
(3) Var(ei) = sigma2
(4) Cov(et, et-s) = 0
TLDR The issues you bring up don't really matter because there are other issues that will probably cancel them out or drown them out over a large enough sample.
