How is the normal distribution formula derived?

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SUMMARY

The normal distribution formula is derived through the application of the standard deviation definition and the Central Limit Theorem. As the number of trials in a binomial distribution increases, the histogram of outcomes approaches the shape of a normal distribution, confirming the accuracy of the exponential formula. This convergence illustrates that any function from R to R, with an integral of 1, can define a probability distribution, with normal distributions frequently modeling real-world phenomena.

PREREQUISITES
  • Understanding of standard deviation and its definition
  • Familiarity with the Central Limit Theorem
  • Knowledge of binomial distributions and their properties
  • Basic calculus, specifically integration concepts
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  • Research the Central Limit Theorem and its implications in statistics
  • Explore the derivation of the normal distribution formula in detail
  • Examine the properties of binomial distributions and their convergence to normal distributions
  • Learn about probability distributions and their applications in real-life scenarios
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Statisticians, data analysts, students studying probability and statistics, and anyone interested in understanding the foundations of normal distributions and their applications.

bomba923
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How do you derive the normal distribution formula??

How was it derived?

(mu=population mean,
sigma=std. deviation)

(see below thumbnail for formula)
 

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Have you attempted it?

I don't remember it off by heart but I do remember the proof on the board being quite simple once you apply the definition of S.D
 
To view the question slightly differently, have you plotted histograms of binomial distributions for a large number of trials? It approximates the normal distribution, ie the graphs agree, and it can be shown that as n goes to infinity that the exponential formula is "correct" (ie the error in using it goes to zero.

Note that ANY function from R to R whose integral over R is 1 defines a probability distribution, it is up to us to find real life situations for when to use them. It so happens that normal distributions appear to describe many real life phenomena.

Look up the Central Limit Theorem to see why it's so powerful.
 
matt grime said:
To view the question slightly differently, have you plotted histograms of binomial distributions for a large number of trials? It approximates the normal distribution, ie the graphs agree, and it can be shown that as n goes to infinity that the exponential formula is "correct" (ie the error in using it goes to zero.

Good idea-i'll try just that :smile:
 
If there are an infinite number of natural numbers, and an infinite number of fractions in between any two natural numbers, and an infinite number of fractions in between any two of those fractions, and an infinite number of fractions in between any two of those fractions, and an infinite number of fractions in between any two of those fractions, and... then that must mean that there are not only infinite infinities, but an infinite number of those infinities. and an infinite number of those...

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