OK, I see what you mean...
All my explanatory variables are significant but I am interested in knowing how much the variation in my dependent variable is explained by each of the explanatory variables. I am interested in the quantitative effects, i.e. variance decomposition...
So I am on the right track, right?
Thanks, Alice.
--- On Fri, 28/11/08, David Airey <[email protected]> wrote:
> From: David Airey <[email protected]>
> Subject: Re: st: Anova
> To: "[email protected]" <[email protected]>
> Date: Friday, 28 November, 2008, 3:04 PM
> No, I meant the difference in R-squared between the model
> with all
> variables vs the one with those of interest removed. But
> are you
> really interested in percent variation explained or a
> measure of
> effect size? It might also be worth interpreting the
> coefficients in
> terms of change in Y with a standardized change in X.
>
> Sent from my iPhone
>
> On Nov 28, 2008, at 8:35 AM, aapdm
> <[email protected]> wrote:
>
> > Dear David,
> >
> > Thanks - so are you suggesting that I should regress Y
> on each of
> > the explanatory variables separately and look at the
> R2 in each case?
> >
> > Thanks, Alice.
> >
> >
> > --- On Fri, 28/11/08, David Airey
> <[email protected]> wrote:
> >
> >> From: David Airey
> <[email protected]>
> >> Subject: Re: st: Anova
> >> To: [email protected]
> >> Date: Friday, 28 November, 2008, 2:22 PM
> >> This is true for balanced factorial ANOVA, but
> probably not
> >> in your complicated model.
> >>
> >> For a given variable, why not look at adjusted R^2
> with
> >> that variable (or group of dummies if categorical)
> in an out
> >> of a regression model?
> >>
> >> -Dave
> >>
> >> On Nov 28, 2008, at 7:08 AM, aapdm wrote:
> >>
> >>> Hi,
> >>>
> >>> I am trying to use the anova command but I am
> not sure
> >> I am doing the right thing.
> >>>
> >>> I have a dependent variable Y which I explain
> by 10
> >> explanatory variables, half of which are
> categorical while
> >> the others are continuous.
> >>>
> >>> When I use the anova command and specify which
> >> variables are continuous, then I get a table with
> the
> >> Partial SS for each of the explanatory variables.
> >>>
> >>> If I sum the Partial SS for all variables then
> this is
> >> much smaller than the value reported for the Model
> SS. How
> >> is that the case? What am I missing here?
> >>>
> >>> What I want to is to find to what extent each
> of the
> >> different explanatory variables explains the
> variance of the
> >> dependent variable, which should be given by the
> ratio
> >> between the Partial SS of each variable and the
> total SS. Am
> >> I right?
> >>>
> >>> Many thanks!
> >>>
> >>> Alice.
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> *
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