Deriving the Gaussian density probability equation

AI Thread Summary
The discussion revolves around the derivation of the coefficient (x-μ)^2 in the Gaussian density probability equation. A user initially presents a formula that contains an error, mistaking σ^3 for σ^2. The correct expression relates to the variance, which is the second moment of the distribution centered at the mean. Clarification is provided that the coefficient is essential for understanding the variance in the context of Gaussian distributions. The conversation highlights the challenges students face when encountering complex mathematical equations without adequate explanations.
CuriousQuazim
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Hey ^^, new here but I already have a question haha

Does anyone here know how the coefficient (x-μ)^2 was derived in the following equation:

σ^3=(1/√2∏)∫(1/σ)*(x-μ)^2*exp((x-μ)^2)/(2σ^2))

I know the general equation for density probability is (1/σ)*exp((x-μ)^2)/(2σ^2))
but in this case I can't quite see how the coefficient came about... any help?

Thanks in advance!
 
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Your expression looks wrong to me. Could you check it for accuracy?
 
It looks like it should be σ2. The expression is essentially the definition of the variance, the second moment of the distribution centered at the mean.
 
Oh I'm sorry that was an error on my part, it is indeed σ^2

σ^2=(1/√2∏)∫(1/σ)*(x-μ)^2*exp((x-μ)^2)/(2σ^2))

Ah thank you so much mathman ^^, that's what I was looking for! I'm studying engineering so sometimes they just throw mathematical equations at us with no explanation ¬_¬.
 
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