Normal Distribution: Understanding the Formula & Terms

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Discussion Overview

The discussion centers around the Gaussian normal distribution, specifically its formula, terms, and derivation. Participants explore its application in natural phenomena and the implications of the central limit theorem, seeking clarity on the mathematical formulation and underlying concepts.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses understanding of the normal distribution as a representation of probabilities distributed around an average, noting the significance of the e^-x^2 term in creating the bell shape.
  • Another participant suggests consulting Wikipedia articles for additional information on the normal distribution.
  • A participant discusses insights gained from the central limit theorem, mentioning how increasing sample size affects the average and standard deviation, and expresses interest in the derivation of the Gaussian PDF.
  • One participant proposes that the normal distribution can be derived by taking the limit of a binomial distribution.
  • Several participants request further elaboration on the derivation process and its connection to the normal distribution formula, expressing confusion about the relationship between the binomial distribution and the normal approximation.
  • A participant points out that the derivation of the normal approximation to the binomial distribution is commonly found in statistics textbooks and suggests checking libraries for resources.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the derivation of the normal distribution or its relationship to the binomial distribution. Multiple viewpoints and requests for clarification remain present throughout the discussion.

Contextual Notes

Some participants express uncertainty regarding the connection between the central limit theorem and the normal distribution formula, as well as the details of the derivation process. There are references to approximations and convergence that are not fully resolved.

O.J.
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Hello again
So we are studying the gaussian normal distribution and what I understand about it is that it helps picture many of the natural phenomena where the basica idea is probabilities are equally distributed around the average. I understand that the e^-x^2 term in the formula ensures the bell shape. But Can anyone give me some insight on how the expression was formulated and what each term means?
 
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Have you looked at the wikipedia articles for the http://en.wikipedia.org/wiki/Normal_Distribution" ?
 
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The article about the central limit theorem was helpful. From reading that and a couple of other articles about the central limit theorem I understood that increasing the sample size from a population sample makes the average of the sample or Xbar closer to the population average and drives the st. dev. to zero. I was inspecting the gaussian PDF today and what I also observed is that in the term e ^ -(x-xbar)^2/2sigma^2, the exponent is basically measuring how far the sample average is from the mean in term of the number of standard deviations squared. But, how this whole thing was put together, I would love to know. I am into little details and derivations. If anyone can help shed some light on this, please do.
 
You can derive it by taking the limit of a binomial distribution.
 
care to elaborate a bit, i really don't know where this is going? link probably?
 
Wikipedia never goes into details. I don't even know how that approximation is related to the normal distribution formula. Can you show me a link where the procedure for the derivation is clearly explained?
 
the derivation of normal approximation to binomial is in any statistics and probability textbook, so try a library if you can't find it on the internet
 

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