56 Forensic Sci Med Pathol (2011) 7:53–62 Fig. 8 Molding cut-off blade kerf Fig. 10 Meat saw blade kerf Fig. 9 Meat saw blade this study [11, 12]. In statistics, logit regression is pro- posed for prediction of the probabilities of events. Logit models provide estimated probabilities given the realiza- tion of covariates compared with statistical descriptive analysis such as mean, median, standard deviation, interquartile range and range. Therefore, the logit models were used to indicate the likelihood of similar categories of saw types. A baseline-category logit model was not used for three reasons. First, as suggested by Fig. 1, the average kerf width is an indicator of the saw type, and incorporating the order of kerf widths improves the accuracy of the model. Second, there is one slope for each covariate which facilitates interpreting the effect of the covariate and provides the proportional odds property needed for analysis. Lastly, in analyzing the kerf data, the quasi-complete separation problem for the data was avoided. 123 Fig. 11 Jig saw blade The ﬁrst logit model considered kerf width only and no other saw information. Model #1 is log it½PðY jÞ ¼ aj þ bxj; where the covariate x is the kerf width and j is the ascending order of the 10 saw kerf widths (Table 3). The y axis in Model #1 represents the probability of saws based on kerf widths. The SAS results in Table 3 suggest the average kerf width is signiﬁcant compared to the kerf width of other saws with a P-value less than 0.01%. The second logit model considered the kerf widths and saw teeth widths. Model #2 is log it½PðY jÞ ¼ aj þ b1x1 þ b2x2; where x1 is the kerf width and x2 is the saw tooth width. The y axis in model 2 represents the probability of kerf widths for the middle sections. The SAS results in Table 4