one of my irc "friends" -- well, i thought they were my friend -- has me on ignore. tfw

You're a bit late to be in the sad 2010 facebook phase.

one of my irc "friends" -- well, i thought they were my friend -- has me on ignore. tfw

You're a bit late to be in the sad 2010 facebook phase.

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im drained again, i can't sleep..

i keep thinking, these are decent people! i'm sure if i just, if i just ARGUE on THEIR TERMS i can break through, help them understand!

but i can't. i won't ever. this is stuck in their mind i'm wasting my time. i'm so sad about this i wanna freakin die

i know i should walk away but it's so hard. it's always hard.

i think you need to chill out buddy

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Reason: yeah this isn't the thread for you to critique our sadbrain thoughts

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Reason: wrong thread

YOU GET OUT

FUCKING NORMIE

REEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

FUCKING NORMIE

REEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

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Reason: no you get out. stop harshing my vibe dude

i want to fuck these cars

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edit: resized image

idk if you read much bible in your Becoming A Christly Wife but proverbs 31 describes the ideal wife. She's smart, strong, a good manager, kind to the poor, and her children love her. "A proverbs 31 woman" is a christian meme, has been for years.

Basically, a Bad Bitch.

nice username

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Are you okay h? I hope you're not giving away your stuff before committing suicide.

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Reason: fuck u

google gives me 15gb

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Reason: google is a botnet

In mathematics, mean has several different definitions depending on the context.

In probability and statistics, population mean and expected value are used synonymously to refer to one measure of the central tendency either of a probability distribution or of the random variable characterized by that distribution.[1] In the case of a discrete probability distribution of a random variable X, the mean is equal to the sum over every possible value weighted by the probability of that value; that is, it is computed by taking the product of each possible value x of X and its probability P(x), and then adding all these products together, giving \mu =\sum xP(x).[2] An analogous formula applies to the case of a continuous probability distribution. Not every probability distribution has a defined mean; see the Cauchy distribution for an example. Moreover, for some distributions the mean is infinite: for example, when the probability of the value 2^{n} is {\tfrac {1}{2^{n}}} for n = 1, 2, 3, ....

For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers x1, x2, ..., xn is typically denoted by {\bar {x}}, pronounced "x bar". If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is termed the sample mean (denoted {\bar {x}}) to distinguish it from the population mean (denoted \mu or \mu _{x}).[3]

For a finite population, the population mean of a property is equal to the arithmetic mean of the given property while considering every member of the population. For example, the population mean height is equal to the sum of the heights of every individual divided by the total number of individuals. The sample mean may differ from the population mean, especially for small samples. The law of large numbers dictates that the larger the size of the sample, the more likely it is that the sample mean will be close to the population mean.[4]

In probability and statistics, population mean and expected value are used synonymously to refer to one measure of the central tendency either of a probability distribution or of the random variable characterized by that distribution.[1] In the case of a discrete probability distribution of a random variable X, the mean is equal to the sum over every possible value weighted by the probability of that value; that is, it is computed by taking the product of each possible value x of X and its probability P(x), and then adding all these products together, giving \mu =\sum xP(x).[2] An analogous formula applies to the case of a continuous probability distribution. Not every probability distribution has a defined mean; see the Cauchy distribution for an example. Moreover, for some distributions the mean is infinite: for example, when the probability of the value 2^{n} is {\tfrac {1}{2^{n}}} for n = 1, 2, 3, ....

For a data set, the terms arithmetic mean, mathematical expectation, and sometimes average are used synonymously to refer to a central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers x1, x2, ..., xn is typically denoted by {\bar {x}}, pronounced "x bar". If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is termed the sample mean (denoted {\bar {x}}) to distinguish it from the population mean (denoted \mu or \mu _{x}).[3]

For a finite population, the population mean of a property is equal to the arithmetic mean of the given property while considering every member of the population. For example, the population mean height is equal to the sum of the heights of every individual divided by the total number of individuals. The sample mean may differ from the population mean, especially for small samples. The law of large numbers dictates that the larger the size of the sample, the more likely it is that the sample mean will be close to the population mean.[4]

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Reason: You had one chance and you blew it

About a week ago I installed Debian on my laptop because I was tired of Arch.

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Reason: Please stay on topic

There is a permanent culture growing on my ballsack is that the same thing? It doesn't go away even after years of showers

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