I don't hear a distinction between response variable(s) and potential
explanatory variable(s). The standard linear regression and parametric
ANOVA assumes that the response variable is a linear function of
parameters to be estimated plus normally distributed noise. I usually
judge this by making normal probability plots (qqnorm) of my response
variables.
Where are the 0's that concern you? Are they in the response
variable(s) or the potential explanatory variable(s)? If the latter,
it's not a problem. If the former, it is.
hope this helps. spencer graves
acovaleda at hotpop.com wrote:> Hi Sirs and Madams.
>
> My question is more statistical than related with the use of R software and
I
> hope it will not seems so silly and elemental. I'm analyzing a set of
data
> of some soil organism collected in diferent landscapes, soils taxa, and
> depths. The sample was performed thinking in a factorial structure with
four
> factors: Specie, Landscape, Soil and Depth. Because not all the species
> appear in each sample there are so many zeros in the matrix data.
>
> Checking the normal distribution I'm not sure If I must check it in the
> original sample data (without zeros) or in the big matrix with zeros. In
the
> first case there is a normal distribution (W = 0,85) but in the second it
is
> not (W = 0,45). In which data must I check distribution?, Can I proceed to
> perform a Parametric ANOVA?.
>
> Thanks
>
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