Abstract
The technique of general weighted least squares is shown to be a useful tool in the formulation and evaluation of certain models appropriate to data in the form of multidimensional contingency tables. This is demonstrated for the Ries-Smith [1963] data by fitting a series of linear regression models which are directed at determining that model which explains almost all of the statistically significant variation among the cell frequencies in terms of a minimum number of parameters. The spirit of this approach is analogous to that used in stepwise regression for continuous data. The test criteria are Wald statistics or minimum-Neyman-chi-square as described by Bhapkar and Koch [1968].
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 callDisclaimer: 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.
