I have a batch of data in each line of data contains three values, calcium score, age, and sex. I would like to predict calcium scores as a function of age and sex, i.e. calcium=f(age,sex). Unfortunately the calcium scorers have a very "ugly distribution". There are multiple zeros, and multiple values between 300 and 600. There are no values between zero and 300. Needless to say, the calcium scores are not normally distributed, however, the values between 300 and 600 have a distribution that is log normal. As you might imagine, the residuals from the regression are not normally distributed and thus violates the basic assumption of regression analyses. Does anyone have a suggestion for a method (or a transformation) that will allow me predict calcium from age and sex without violating the assumptions of the model? Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC and University of Maryland School of Medicine Claude Pepper OAIC University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 410-605-7119 - NOTE NEW EMAIL ADDRESS: [EMAIL PROTECTED]
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