How to handle standard errors

[Simon Speich]

We provide for all our metric dimension values a standard error. Let's say I have the concept tree volume which is estimated from a sample for different regions. So, for each volume value I have a corresponding standard error. Would you consider then the tree volume as a dimension of size 2 ?

[Xavier Badosa]

Yes, you should use the metric dimension to handle standard errors because it's the the same cube (same non-metric dimensions), only what you measure changes. The goal of the metric dimension is to be able to express in a single cube data in absolute values, as a ratio, as a variation, or even as sampling errors.

X.

On Tue, Feb 18, 2020 at 9:56 AM Simon Speich <simon....@gmail.com> wrote:

We provide for all our metric dimension values a standard error. Let's say I have the concept tree volume which is estimated from a sample for different regions. So, for each volume value I have a corresponding standard error. Would you consider then the tree volume as a dimension of size 2 ?

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[Simon Speich]

Thanks Xavier

On Tuesday, February 18, 2020 at 10:55:54 AM UTC+1, Xavier Badosa wrote:

Yes, you should use the metric dimension to handle standard errors because it's the the same cube (same non-metric dimensions), only what you measure changes. The goal of the metric dimension is to be able to express in a single cube data in absolute values, as a ratio, as a variation, or even as sampling errors.

X.

On Tue, Feb 18, 2020 at 9:56 AM Simon Speich <simon...@gmail.com> wrote:

We provide for all our metric dimension values a standard error. Let's say I have the concept tree volume which is estimated from a sample for different regions. So, for each volume value I have a corresponding standard error. Would you consider then the tree volume as a dimension of size 2 ?

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