Calculate the standard deviation of Gaussian distribution ,thanks

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The discussion focuses on calculating the standard deviation of a one-dimensional Gaussian distribution defined by the function f(x) = exp(-x^2/(2q)) / (q√(2π)). It clarifies that 'q' represents the population standard deviation, contrasting it with the sample standard deviation. The calculation of the population standard deviation requires integration, which involves calculus. The thread concludes with a note that the original question lacked effort and is subsequently closed. Understanding the relationship between 'q' and standard deviation is essential for proper analysis of Gaussian distributions.
chener
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Given a one-dimensional Gaussian distribution, distributed as following:

f (x) = exp (-x ^ 2 / (2q)) / q / √ (2pi)

proof which q is the standard deviation

Thanks !The standard deviation is defined by:
http://www.mathsisfun.com/data/standard-deviation-formulas.html
 
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chener said:

That link defines the "sample standard deviation".

The number 'q' is the "population standard deviation". The definition of "population standard deviation" ( which is the "standard deviation of a random variable") involves doing an integration. Are you familiar with calculus?
 
Misplaced schoolwork-type question with no effort shown. Thread is closed.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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