How is Standard Deviation Derived and What is Chebychev's Rule?

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Discussion Overview

The discussion revolves around the derivation of the standard deviation and the proof of Chebychev's rule within the context of statistics. Participants explore the mathematical foundations, definitions, and implications of these concepts, touching on both theoretical and practical aspects.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant questions the derivation of the standard deviation, specifically why deviations are squared instead of using absolute values and why it is divided by (n-1) instead of n.
  • Another participant mentions that the standard deviation formula can be derived and that dividing by (n-1) provides an unbiased estimate for sample data, although they do not provide the proof.
  • There is a request for a mathematical proof of Chebychev's rule, indicating a desire for deeper understanding.
  • Discussion includes the observation that statistics often rely on normal distributions, which allows for certain fractions of results to be defined within standard deviations of the mean.
  • One participant raises a question about the necessity of using a specific definition of standard deviation and whether other definitions could yield different fractions.
  • A historical perspective is provided, noting that earlier statistical methods used absolute values, but these are considered more complex than squaring deviations.
  • Another participant discusses the concept of moments in probability distributions, explaining that the mean and variance are the first and second moments, respectively, and raises a question about distributions that lack moments.

Areas of Agreement / Disagreement

Participants express varying levels of understanding and agreement regarding the derivation of standard deviation and the use of moments in probability distributions. There is no consensus on the proof of Chebychev's rule or the necessity of specific definitions of standard deviation, indicating multiple competing views and unresolved questions.

Contextual Notes

Some participants express uncertainty about the proofs and derivations discussed, and there are references to the complexity of using absolute values in statistical calculations. The discussion also highlights the dependence on definitions and the historical evolution of statistical methods.

Moose352
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It seems to me that a lot the concepts in statistics are rather arbitrary and don't seem be mathematically derived. For example, how is the equation for standard deviation derived? The textbook says that the standard deviation is the mean of all of the deviations of the values in the sample and since all the deviations add up to zero, the values are sqaured to get rid of the negative. I understand that, but why doesn't it just take the absolute value? Why is the square-root taken only after everything has been summed? Furthermore, why is it divided by (n-1) and not n?

Also, can anyone explain the proof for Chebychev's rule?

Thanks very much.
 
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Hello Moose,

The standard deviation formula can be derived, my Maths teacher showed me. Unfortunately I am unable to derive it so I will not be much help there. The reason we divide by n-1 sometimes is to give as a most accurate or unbiased estimate for sample data. There is also proof for that, which I am also unable to do. Hope this helped.

Regards,

Daniel
 
Thanks repugno. It's good to know that there is a proof, but I will not be convinced until i see it.
 
Lol .. tried to give you some Mathematical proof, seems that I can't get the Latex code right. You're on your own now. :D
 
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Ah! Please try again. I can't find any other proof. I think the real problem is I haven't yet found a concept (of course, granted that I haven't learned much) that the current definition of SD exclusively works for.
 
A large amount of statistics is based on the notion of normal distributions.

For normal distributions it is possible to, for example, show that a certain fraction of the results are within a standard deviation of the peak.
 
I completely understand. But why does it have to be based on that specific definition standard distribution. Can not those fractions be recalculated based on another definition of the standard deviation?
 
Very early in the history of statistics they did use absolute values. But the math of those is difficult: they are not "analytic functions". Squares on the other hand are polynomials, easy to work with. In fact the real number here is the variance, the square of the standard deviation (or rather, the standard deviation is the square root of the variance).

Any probability distribution that has moments has the mean as its first moment, and the variance as its second moment (essentially and with some tech fiddles). What are moments? well in one sense they are the parameters that determine the equation of the probability curve. The normal curve is distinguished because it has only those two moments; it is a two parameter curve. You tell me where the mean is and what the standard deviation is and I will give you the formula for that notmal curve and be able to draw it. Any other probability distribution that has moments at all - some don't - will be determined if you specify all of its moments, which may be an infinite number.
 
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Originally posted by selfAdjoint
Any other probability distribution that has moments at all - some don't - will be determined if you specify all of its moments, which may be an infinite number.
Wow, that sounds interesting. Is this like a Taylor expansion of a function? If the distribution has no moments, is it trivial?
 

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