Sex estimation based on skeletal dimensions can be an important tool in the case of a missing pelvis, but is population specific. The aim is to present logistic regression models for sex estimation specifically for the Old Kingdom population (2700–2180 BCE) of ancient Egypt. The sample includes the skeletal remains of 162 adult individuals, 95 males and 67 females, from two burial sites (Abusir, Giza), whose sex could be estimated based on pelvic bone measurements and/or morphological features. A total of 38 cranial and 64 postcranial measurements were included. Sex estimation equations were developed using logistic regression to obtain probabilities for sex predictions. 26 final skeletal measurements were selected for use in the multivariate models. A total of 65 best performing models are presented. All are statistically significant at a 0.01 p-level and characterized by a combination of low error, high decisiveness, high accuracy, and sufficient coverage. Models incorporating lower limb measurements perform best, yielding almost no errors on the 95% certainty level. Sexual dimorphism in the lower limbs is best reflected by the physiological length of the talus, and in the upper limbs by the transversal head diameter of the humerus. The main advantages to logistic regression models are reliability and using probabilities to predict sex. Previous models used discriminant analysis and did not target the population of the Old Kingdom. Their comparison with the presented models shows population specificity over time, and the need for models designed specifically for Old Kingdom individuals.

Sex estimation based on skeletal dimensions can be an important tool in the case of a missing pelvis, but is population specific. The aim is to present logistic regression models for sex estimation specifically for the Old Kingdom population (2700–2180 BCE) of ancient Egypt. The sample includes the skeletal remains of 162 adult individuals, 95 males and 67 females, from two burial sites (Abusir, Giza), whose sex could be estimated based on pelvic bone measurements and/or morphological features. A total of 38 cranial and 64 postcranial measurements were included. Sex estimation equations were developed using logistic regression to obtain probabilities for sex predictions. 26 final skeletal measurements were selected for use in the multivariate models. A total of 65 best performing models are presented. All are statistically significant at a 0.01 p-level and characterized by a combination of low error, high decisiveness, high accuracy, and sufficient coverage. Models incorporating lower limb measurements perform best, yielding almost no errors on the 95% certainty level. Sexual dimorphism in the lower limbs is best reflected by the physiological length of the talus, and in the upper limbs by the transversal head diameter of the humerus. The main advantages to logistic regression models are reliability and using probabilities to predict sex. Previous models used discriminant analysis and did not target the population of the Old Kingdom. Their comparison with the presented models shows population specificity over time, and the need for models designed specifically for Old Kingdom individuals.

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