N(μ, σ²) [Software]
❝ Since σ2 is unknown, it is estimated by the MSE from ANOVA. Then we can estimate \(CV = \sqrt{e^{MSE} - 1}\). Do you see \(\bar{x}_R\) in this derivation? I don’t. Remember that the normal distribution is described by two parameters, μ and σ2, which are independent. If you are interested in the variance component, please leave the mean(s) completely out of it (as it is correctly done in PHX/WNL for log-transformed data).
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Hi Helmut,
Good explanation. Now, I think you are right.

Complete thread:
- Lack of IntersubjectCV in PHX yicaoting 2017-09-01 11:09 [Software]
- CVs for untransformed data mittyri 2017-09-02 15:46
- CVs for untransformed data Helmut 2017-09-02 15:57
- CVs for untransformed data yicaoting 2017-09-02 17:50
- CVs for untransformed data Helmut 2017-09-02 19:20
- CVs for untransformed data yicaoting 2017-09-02 23:26
- N(μ, σ²) Helmut 2017-09-04 13:32
- N(μ, σ²)yicaoting 2017-09-05 02:46
- N(μ, σ²) Helmut 2017-09-04 13:32
- CVs for untransformed data yicaoting 2017-09-02 23:26
- CVs for untransformed data Helmut 2017-09-02 19:20
- CVs for untransformed data yicaoting 2017-09-02 17:50
- CVs for untransformed data yicaoting 2017-09-03 08:19
- CVs for untransformed data mittyri 2017-09-03 08:41
- CVs for untransformed data yicaoting 2017-09-04 11:35
- CVs for untransformed data mittyri 2017-09-03 08:41
- CVs for untransformed data Helmut 2017-09-02 15:57
- CVs for untransformed data mittyri 2017-09-02 15:46