Thanks to all contributors for the fruitfulness of this discussion. I
am speculating about a simpler solution: to use a non-parametric
approach. To avoid the requirement of having normal residuals, Frank
Harrell has suggested here the following non-parametric procedure:
library(Design) # also req
CTED]>
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[R] Looking for transformation to overcome heterogeneity of variances
Dear All
My data consists in 96 groups, each one with 10 observations. Levene's
test suggests that the variances are
"Paul Smith" <[EMAIL PROTECTED]> writes:
> On 03 Aug 2006 15:45:10 +0200, Peter Dalgaard <[EMAIL PROTECTED]> wrote:
> > > My data consists in 96 groups, each one with 10 observations. Levene's
> > > test suggests that the variances are not equal, and therefore I have
> > > tried to apply the class
On 03 Aug 2006 16:32:38 +0200, Peter Dalgaard <[EMAIL PROTECTED]> wrote:
> > > > My data consists in 96 groups, each one with 10 observations. Levene's
> > > > test suggests that the variances are not equal, and therefore I have
> > > > tried to apply the classical transformations to have homocedas
On 03 Aug 2006 15:45:10 +0200, Peter Dalgaard <[EMAIL PROTECTED]> wrote:
> > My data consists in 96 groups, each one with 10 observations. Levene's
> > test suggests that the variances are not equal, and therefore I have
> > tried to apply the classical transformations to have homocedasticity
> > i
"Paul Smith" <[EMAIL PROTECTED]> writes:
> Dear All
>
> My data consists in 96 groups, each one with 10 observations. Levene's
> test suggests that the variances are not equal, and therefore I have
> tried to apply the classical transformations to have homocedasticity
> in order to be able to use
Dear All
My data consists in 96 groups, each one with 10 observations. Levene's
test suggests that the variances are not equal, and therefore I have
tried to apply the classical transformations to have homocedasticity
in order to be able to use ANOVA. Unfortunately, no transformation
that I have u
