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From |
aapdm <aapdm_999@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Anova |

Date |
Fri, 28 Nov 2008 15:31:24 +0000 (GMT) |

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 <david.airey@vanderbilt.edu> wrote: > From: David Airey <david.airey@vanderbilt.edu> > Subject: Re: st: Anova > To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> > 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 > <aapdm_999@yahoo.co.uk> 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 > <david.airey@vanderbilt.edu> wrote: > > > >> From: David Airey > <david.airey@vanderbilt.edu> > >> Subject: Re: st: Anova > >> To: statalist@hsphsun2.harvard.edu > >> 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. > >>> > >>> > >>> > >>> > >>> > >>> > >>> * > >>> * For searches and help try: > >>> * http://www.stata.com/help.cgi?search > >>> * http://www.stata.com/support/statalist/faq > >>> * http://www.ats.ucla.edu/stat/stata/ > >> > >> * > >> * For searches and help try: > >> * http://www.stata.com/help.cgi?search > >> * http://www.stata.com/support/statalist/faq > >> * http://www.ats.ucla.edu/stat/stata/ > > > > > > > > > > * > > * For searches and help try: > > * http://www.stata.com/help.cgi?search > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Anova***From:*John Antonakis <John.Antonakis@unil.ch>

**Re: st: Anova***From:*David Airey <david.airey@vanderbilt.edu>

**References**:**Re: st: Anova***From:*David Airey <david.airey@vanderbilt.edu>

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