Re: Definition of SE (standard error) in LinearRegress and NonlinearRegress

*To*: mathgroup at smc.vnet.net*Subject*: [mg70296] Re: [mg70029] Definition of SE (standard error) in LinearRegress and NonlinearRegress*From*: Darren Glosemeyer <darreng at wolfram.com>*Date*: Thu, 12 Oct 2006 05:37:01 -0400 (EDT)*References*: <200609300913.FAA13235@smc.vnet.net>

The standard errors for parameter estimates in linear regression are the square roots of the diagonal elements of the parameter covariance matrix Inverse[Transpose[X].X] where X is the design matrix for the regression. The ith row of the design matrix contains the values of the basis functions evaluated at the ith data point. The standard errors for parameter estimates in nonlinear regression are the square roots of the diagonal elements of the asymptotic parameter covariance matrix Inverse[Transpose[approxX].approxX] where approxX is an approximate design matrix for the nonlinear model. The ith row of the approximate design matrix contains the values of the first derivatives of the model function with respect to each of the parameters evaluated at the ith data point. Darren Glosemeyer Wolfram Research Seo Ho Youn wrote: > Hello, all. > > > > Can I ask how SE (standard error) in LinearRegress and NonlinearRegress is > defined or calculated in Mathematica for a multi-parameter least-square fit? > Or, does anybody know about document (or definition) on SE in Mathematica? I > haven't been able to find any about how it is calculated in Mathematica. > > > > Thank you for your help and have a good day. > > > > Seo Ho > >