Programming the statistical procedures from SAS

Performing GLM/MANOVA, not getting all values

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Performing GLM/MANOVA, not getting all values

Trying to finish my Master's thesis and having trouble with the stats.  I'm researching habitat preferences of turtles.  So I have a number of sites where turtles were found, then several characteristics, such as how far away railroad tracks are, wetland types, size of the wetland, etc.  I ran a sample of two, then three characteristics using a GLM, but I don't seem to be getting all the values I need.  I get the partial correlation coefficients, that's it.  No F value, for example.  Shouldn't there be more?  (My advisor, who uses SAS often seems to think there should be more.)  Please help?

My code looks like this:

data TurtlePopDesignation;

  input TurtlePopDesignation $ Proxtorail DisttoInterstate Area  @@;

    datalines;

(lines of data removed)

;

proc GLM;

  class TurtlePopDesignation;

  model Proxtorail DisttoInterstate Area = TurtlePopDesignation / nouni;

  manova / printe;

run;

And yes, I've already been told my names are too long.  Smiley Happy

The turtles and I thank you in advance for your help!

Respected Advisor
Posts: 3,773

Re: Performing GLM/MANOVA, not getting all values

Do you need H options?

H=effects | INTERCEPT | _ALL_ specifies effects in the preceding model to use as hypothesis matrices. For each matrix (the SSCP matrix associated with an effect), the H= specification displays the characteristic roots and vectors of (where is the matrix associated with the error effect), along with the Hotelling-Lawley trace, Pillai’s trace, Wilks’ lambda, and Roy’s greatest root. By default, these statistics are tested with approximations based on the distribution. To test them with exact (but computationally intensive) calculations, use the MSTAT=EXACT option. Use the keyword INTERCEPT to produce tests for the intercept. To produce tests for all effects listed in the MODEL statement, use the keyword _ALL_ in place of a list of effects. For background and further details, see the section Multivariate Analysis of Variance.

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