Proof for Mean-Mode=3(Mean-Median)

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Discussion Overview

The discussion revolves around the relationship between the mean, median, and mode in statistics, specifically the equation Mean - Mode = 3(Mean - Median). Participants seek algebraic proof or derivation of this relationship, exploring its validity and context within statistical distributions.

Discussion Character

  • Debate/contested
  • Exploratory
  • Technical explanation

Main Points Raised

  • One participant requests an algebraic proof of the relationship between mean, median, and mode.
  • Another participant expresses skepticism about the general validity of the relationship, suggesting it may only hold for certain probability density functions (PDFs) expressed in terms of a lowest order Edgeworth series.
  • A participant corrects the initial formula, stating that the correct relationship is Mode = 3Median - 2Mean, and asks for its derivation.
  • Concerns are raised about the validity of the relationship, noting that some distributions may not have a mode while still having a median and mean.
  • References to Edgeworth expansion are mentioned as a potential method for deriving the relationship, including considerations of skewness and kurtosis.
  • Participants share links to discussions and resources that provide additional context on the empirical relationship between mean, median, and mode.
  • One participant acknowledges the relationship as empirical and seeks a derivation for this empirical formula.
  • A participant mentions that Karl Pearson developed the guideline from observations of skewed data sets, suggesting that the relationship should be viewed as approximately equal rather than exact.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the validity of the relationship, with multiple competing views regarding its applicability and derivation remaining unresolved.

Contextual Notes

Some participants note limitations in understanding the derivation and the context in which the relationship holds, particularly regarding specific distributions and the nature of empirical formulas.

Raghav Gupta
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Can anybody give proof of the above relationship algebraically?I have not seen the derivation of it.
 
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I don't think that this is a generally valid relationship. I would guess that it holds for a PDF expressed in terms of a lowest order Edgeworth series.
 
DrDu said:
I don't think that this is a generally valid relationship. I would guess that it holds for a PDF expressed in terms of a lowest order Edgeworth series.
I was in a hurry and when one is not familiar with the derivation ,often one messes the formula.
Yeah the correct relationship is Mode=3Median-2Mean.What is the derivation for it?
 
That's the same formula you wrote before, only solved for Mode. It is not a general valid equation. For example, there are distributions which don't even have a mode, but a median and a mean.
I suppose you can get it using an Edgeworth expansion including the skewness and the curtosis:
http://en.wikipedia.org/wiki/Edgeworth_series
 
I hardly know more about this relation than you. In the link I found there are all the references to original articles you need.
 
DrDu said:
I hardly know more about this relation than you. In the link I found there are all the references to original articles you need.
I have seen that link in Mathematics stack exchange before and I did't get it that's why I have posted it here.When you first posted that it is not a valid relationship I thought that modification in statistic may have came for this formula.
 
  • #10
No problem if you don't know but if you can help for some initial steps It would help me.
 
  • #11
Karl Pearson (I believe) developed that guideline (not a rule) from observations of many slightly to moderately skewed data sets and distributions. The equality you've written really should be taken as "approximately equal to", since the intent of the relationship was to have a quick way to approximate values. I don't know whether he published a derivation or simply mentioned it in an aside or lecture.
 

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