Dear SPMers,
I am sure that there hasn been quite a bit of
discussion on multiple regression with and without
constant. After archive search, i couldn't find a
conclusive answer to the following
a) When do i use multiple regression with constant and
when do i use without constant.
b) do i need to mean center the covariate (regressor)
in these two cases?
For example, when i run a simple regression
(correlation) analysis, (with constant term by
default), i get around 693 voxels, but when i omit the
constant term the number of activated voxels reduces
to 495 and finally when i mean center the covariate
(without the constant term), the number of active
voxels is 731.
This example above is a simple test case and i would
want to extend this logic to multiple regresion,
because i have more than one measurement per subject.
i do think that including the constant term is a sort
of a good way to control the intercept and test for
the slope/gradient of regression.
On other thoughts, including a constant term is going
to absorb activations due to the main effect of the
contrast, hence there would essentially be no overlap
between the results from a one sample t-test and a
correlation analysis.
Many Thanks in advance for any advice
-krishna
Krishna P. Miyapuram
Dept of Physiology, Development and Neuroscience
Univ of Cambridge
URL : http://people.pwf.cam.ac.uk/kpm23/index.html
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