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To specifically answer the last part of your question, the statistical test applied is testing whether or not its significant after controlling for the null-space of the contrast (e.g. where the contrast is 0). So if you test the first, it controls for the 2nd and 3rd. If you test the 2nd, it controls for the 1st and 3rd. Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Wed, Dec 22, 2010 at 11:33 AM, MCLAREN, Donald <[log in to unmask]>wrote: > Stasa, > > (1) multivariate regression is a regression analysis with multiple > dependent variables, not multiple independent variables. whne you have > multiple independent variables, the model is either a multiple regression or > ANCOVA. > > (2) Since you don't have any grouping variables, your example is a multiple > regression. If you added groups, then you'd have a ANCOVA model. > > (3) The order of the independent variables does not matter. Just make sure > you know the order you entered so that you know what you are testing. The > statistical tests that you run are the same as adding the term last in a > stepwise regression model. The equation for the estimation of the betas is: > pinv(X)*Y. Where X is your independent variable matrix and Y is your > dependent variable. > > Hope this helps. > > Best Regards, Donald McLaren > ================= > D.G. McLaren, Ph.D. > Postdoctoral Research Fellow, GRECC, Bedford VA > Research Fellow, Department of Neurology, Massachusetts General Hospital > and Harvard Medical School > Office: (773) 406-2464 > ===================== > This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED > HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is > intended only for the use of the individual or entity named above. If the > reader of the e-mail is not the intended recipient or the employee or agent > responsible for delivering it to the intended recipient, you are hereby > notified that you are in possession of confidential and privileged > information. Any unauthorized use, disclosure, copying or the taking of any > action in reliance on the contents of this information is strictly > prohibited and may be unlawful. If you have received this e-mail > unintentionally, please immediately notify the sender via telephone at (773) > 406-2464 or email. > > > On Wed, Dec 22, 2010 at 10:43 AM, Stasa Tadic <[log in to unmask]> wrote: > >> Hi all, >> I would like to analyze relation between two variables/measures (fMRI >> data as dependent variable, white matter hyperintensities counts as >> independent variable) while controling for other variables like age or >> cognitive function (or both). I am using SPM 5. >> If I use multivariate regression analysis in SPM5, does the way of >> ordering the variables assume that the first will be inferred and others >> (second or third ) will be fixed/controled for... >> Thanks, >> Stasa >> > >