Abstract

Bivariate linear models, used to describe morphological and functional characteristics between two sets of observations, are examined both in concept and in application. This paper focuses on the underlying assumptions and statistics of the methods most frequently used: ordinary linear regression, principal axis and standard major axis. It is shown how the choice of method should depend on: (1) the purpose of the analysis and (2) the a priori assumptions regarding the residual variance. It appears that none of the methods has a universal application. Differences among the models discussed are illustrated by a bivariate morphometric analysis of cerebrocortical regions in primates.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.