How to Simplify the Mean of 3D Variables to 1D?

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To simplify the mean value of the expression <(ax^2 + ay^2 + az^2)^(1/2)> to <|ax|>, it is necessary to assume that the statistical properties of ax, ay, and az are equal, specifically that <ax^2> = <ay^2> = <az^2>. Additionally, the independence of the probability density functions for ax, ay, and az is crucial, allowing for the joint probability to be expressed as the product of individual densities. The calculation involves integrating a 3D function, which can be complex due to the square root operation. The discussion raises the question of whether the mean of the resultant vector's magnitude equals the mean of the absolute value of ax, indicating a need for further theoretical exploration. Ultimately, no additional assumptions are required beyond those stated for the simplification process.
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Hi,

There are three variables ax, ay and az, my question is:
How to simplify the mean value <(ax^2+ay^2+az^2)^(1/2)> to <|ax|> ?
What assumptions are required during the simplification?

The statistical property of ax, ay and az is <ax^2>=<ay^2>=<az^2>.
The assumption of the propability is: pdf(ax), pdf(ay) and pdf(az) are independent to each other: p(ax,ay,az)=p(ax)p(ay)p(az)

Thanks
 
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No further assumptions are needed to carry out the calculation. It is messy because you are taking the square root before calculating the integral.
 
Thanks Mathman!
What theory or property can be applied to this problem? I don't even have a clue.

Really appreciate.
 
It is a 3-d integral where the integrand is the product of the 3 density functions multiplied by the expression (square root etc.).
 
pangyatou said:
Hi,

There are three variables ax, ay and az, my question is:
How to simplify the mean value <(ax^2+ay^2+az^2)^(1/2)> to <|ax|> ?
What assumptions are required during the simplification?

Are you asking if the mean value of r = \sqrt{a_x^2 + a_y^2 + a_z^2} must be equal to the mean value of the absolute value of a_x ?
 
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