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Statistical Inference for Serial Dilution Assay Data
Author(s) -
Lee MeiLing Ting,
Whitmore G. A.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.01215.x
Subject(s) - count data , inference , dilution , statistical inference , poisson distribution , bernoulli's principle , computer science , serial dilution , statistics , chromatography , data mining , mathematics , chemistry , artificial intelligence , medicine , physics , alternative medicine , pathology , engineering , thermodynamics , aerospace engineering
Summary. Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson‐Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log‐normal model and illustrates these methods using a case application involving bacterial toxins.