Mean, simplified from 3D to 1D

In summary, the conversation discusses simplifying the mean value of three variables, ax, ay, and az, to the absolute value of ax. The assumption is that the probability density functions for each variable are independent of each other. No further assumptions are needed for the calculation, although it may be messy due to taking the square root before integrating. The theory or property applied to this problem is a 3-D integral where the integrand is the product of the three density functions multiplied by the expression (square root, etc.). Finally, the question is clarified as whether the mean value of the square root of the sum of the squared variables must be equal to the mean value of the absolute value of one of the variables.
  • #1
pangyatou
4
0
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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  • #2
No further assumptions are needed to carry out the calculation. It is messy because you are taking the square root before calculating the integral.
 
  • #3
Thanks Mathman!
What theory or property can be applied to this problem? I don't even have a clue.

Really appreciate.
 
  • #4
It is a 3-d integral where the integrand is the product of the 3 density functions multiplied by the expression (square root etc.).
 
  • #5
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 [itex] r = \sqrt{a_x^2 + a_y^2 + a_z^2} [/itex] must be equal to the mean value of the absolute value of [itex] a_x [/itex] ?
 

Related to Mean, simplified from 3D to 1D

1. What is "Mean, simplified from 3D to 1D"?

"Mean, simplified from 3D to 1D" refers to the process of reducing a three-dimensional (3D) dataset into a one-dimensional (1D) dataset by taking the mean value of each dimension. This allows for a simpler representation of the data, which can be useful for certain analyses and visualizations.

2. How is the mean calculated in this simplification process?

In this process, the mean is calculated by taking the average value of each dimension in the 3D dataset. For example, if the 3D dataset has values of (x,y,z) = (2,4,6) and (1,3,5), the mean values for x, y, and z would be 1.5, 3.5, and 5.5, respectively. These mean values would then be used to create the simplified 1D dataset.

3. What are the advantages of simplifying from 3D to 1D?

Simplifying from 3D to 1D can make complex datasets easier to understand and analyze. It can also reduce the amount of data that needs to be processed, making it more efficient. Additionally, 1D data can be easily plotted on a graph or chart, allowing for visualizations that can reveal patterns and trends in the data.

4. Are there any limitations to this simplification process?

Yes, there are limitations to this process. Simplifying from 3D to 1D can result in a loss of information, as the 1D dataset is a simplified representation of the original 3D dataset. This means that some details may be lost in the simplification process, which could affect the accuracy of certain analyses or conclusions drawn from the data.

5. In what situations would simplifying from 3D to 1D be useful?

Simplifying from 3D to 1D can be useful in situations where the 3D dataset is too complex to analyze or visualize easily. This could be the case in large datasets with many dimensions or in datasets with highly variable values. It can also be useful for reducing the computational burden when working with large datasets.

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