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.
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 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.
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 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.
In statistics given a large enough sample we can assume that white noise is zero.
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.
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 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.
In statistics given a large enough sample we can assume that white noise is zero.
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 I'm a nerdy fag
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 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.
In statistics given a large enough sample we can assume that white noise is zero.
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 I'm a nerdy fag
I am 6'5" 260 lbs. Former Army Ranger and college basketball player. You really don't want any in real life. You are little more than a cowardly pussy.
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 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.
In statistics given a large enough sample we can assume that white noise is zero.
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 I'm a nerdy fag
I am 6'5" 260 lbs. Former Army Ranger and college basketball player. You really don't want any in real life. You are little more than a cowardly pussy.
What the fuck did you fucking say to me you little bitch?
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 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.
In statistics given a large enough sample we can assume that white noise is zero.
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 I'm a nerdy fag
I am 6'5" 260 lbs. Former Army Ranger and college basketball player. You really don't want any in real life. You are little more than a cowardly pussy.
What the fuck did you fucking say to me you little bitch?
What the fuck did you just fucking type about me, you little bitch? I’ll have you know I graduated top of my class at UW, and I’ve been involved in numerous secret raids with Anonymous, and I have over 300 confirmed DDoSes. I am trained in online trolling and I’m the top hacker in the entire world. You are nothing to me but just another virus host. I will wipe you the fuck out with precision the likes of which has never been seen before on the Internet, mark my fucking words. You think you can get away with typing that shit to me over the Internet? Think again, fucker. As we chat over IRC I am tracing your IP with my damn bare hands so you better prepare for the storm, maggot. The storm that wipes out the pathetic little thing you call your computer. You’re fucking dead, kid. I can be anywhere, anytime, and I can hack into your files in over seven hundred ways, and that’s just with my bare hands. Not only am I extensively trained in hacking, but I have access to the entire arsenal of every piece of malware ever created and I will use it to its full extent to wipe your miserable ass off the face of the world wide web, you little shit. If only you could have known what unholy retribution your little “clever” comment was about to bring down upon you, maybe you would have held your fucking fingers. But you couldn’t, you didn’t, and now you’re paying the price, you goddamn idiot. I will shit code all over you and you will drown in it. You’re fucking dead, kiddo.
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 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.
yeah. Yeah, that’s it. That’s what I’m doing here!
Comments
It's better to LEAVE!
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.