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The geometry of bivariate regression is studied from a sampling viewpoint. This leads to an intuitive, but rigorous proof that enhancement occurs in one half of the space of possible observations. In addition, we obtain specific results concerning the relative size of the spaces in which 'classical', 'net', and 'cooperative suppression' occur.

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With immunoassay or bioassay data, the assay standards often exhibit considerable inter-assay variability. However, the assay controls, which are used to monitor the assay performance and set acceptance criteria, should have no or less interassay variability. In this paper, we develop a mixed-effect calibration model for the assay controls to set new acceptance criteria and qualify the enzyme-linked immunosorbent assay (ELISA) data, which incorporates the interassay variation of assay standards and the nature of the assay controls, and overcomes the problems caused by traditional fixed-effect calibration model.

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