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Comparison study of statistical application of logistic regression with discriminant analysis in sex identification by using 3-dimension computed tomography image was done in the samples of 100 scapula (from 50 females and 50 males, with ages between 20-70 years), and the mentioned images happened from the process of autopsy of Central Institute of Forensic Science during the year 2016 to 2017. The process had measured sizes of right scapula in a total of 7 positions with Vitrea version 4.1.52 program (Vital Images, Inc.), and data were analyzed with statistics of discriminant analysis and logistic regression in order to calculate for equation and percentage accuracy for identifying sex of variables measurement. The study had applied univariate variable measurement and found that the ability in sex distinguish of each variable is slightly different. By statistic discrimination analysis, it gave accuracy in sex identification for 82-94 %, compared with statistic logistic regression that gave accuracy of 82-95 %. The variable that gives the most accuracy in sex identification is GL (length of the glenoid cavity). However, for the application of multivariate variable, it was found that statistic discriminant analysis gives better accuracy in sex identification. Using only 2 variables of length of the glenoid cavity (GL) and projection length of scapula spine (SS) with sex identification equation of -27.286 + 0.425GL + 0.09SS (cut value = 0) gives accuracy in sex identification in an average of 97 %.
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