well said!
well said!
If you don't like his picks, get the f^%$ out of his thread you little bi&%$
If you don't like his picks, get the f^%$ out of his thread you little bi&%$
If you have all the winners, why don't you post? Better yet, why don't we start a thread everyday and we will see who can pick more winners, you or pimp.
If you have all the winners, why don't you post? Better yet, why don't we start a thread everyday and we will see who can pick more winners, you or pimp.
Nicky Shavers = TurkeyTits = Butterball Boobs = redawg.
Hey Nicky Turkey Boobdawg, put down the paper bag full of glue, step away from the computer, and get the hell out of your mommie's basement for at least a few hours.
Nicky Shavers = TurkeyTits = Butterball Boobs = redawg.
Hey Nicky Turkey Boobdawg, put down the paper bag full of glue, step away from the computer, and get the hell out of your mommie's basement for at least a few hours.
In probability theory and statistics, the variance of a random variable, probability distribution, or sample is one measure of statistical dispersion, averaging the squared distance of its possible values from the expected value (mean). Whereas the mean is a way to describe the location of a distribution, the variance is a way to capture its scale or degree of being spread out. The unit of variance is the square of the unit of the original variable. The positive square root of the variance, called the standard deviation, has the same units as the original variable and can be easier to interpret for this reason.
The variance of a real-valued random variable is its second central moment, and it also happens to be its second cumulant. Just as some distributions do not have a mean, some do not have a variance. The mean exists whenever the variance exists, but not vice versa.
Nice copy and paste from Wikipedia
Loser.
In probability theory and statistics, the variance of a random variable, probability distribution, or sample is one measure of statistical dispersion, averaging the squared distance of its possible values from the expected value (mean). Whereas the mean is a way to describe the location of a distribution, the variance is a way to capture its scale or degree of being spread out. The unit of variance is the square of the unit of the original variable. The positive square root of the variance, called the standard deviation, has the same units as the original variable and can be easier to interpret for this reason.
The variance of a real-valued random variable is its second central moment, and it also happens to be its second cumulant. Just as some distributions do not have a mean, some do not have a variance. The mean exists whenever the variance exists, but not vice versa.
Nice copy and paste from Wikipedia
Loser.
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