The literature indicates that relatively little research is available to describe the relationship between functional running tasks and characteristics of individuals who perform these tasks. As a main purpose, the present work is to define the computational modeling for anthropometric characteristics of athletes. Thus the dynamic model presented by this 100-meter running test can play an important role in talent and coaching. The research question was formed by classification and comparison of statures of sportswomen with other anthropometric classes. On the other hand, the present work compares the anthropometric data for runner velocity (running time) against runner weight. The method of research is regression statistical analysis method. In this work, the regression method is based on the univariable ANOVA variance with repeated measures and -test for independent samples. Data analysis was performed by using the software SPSS13. The results of the 100-meter running test of sportswomen showed good correlation between the parameters. As a dynamic modeling selection, the logarithmic function showed suitable correlation on scatter diagram. Consequently, the results of this work will help to reduce the risk of sportswomen activities. Therefore it can be recommended for medical professionals and athletic talent. 1. Introduction Historical anthropometrics is the study of patterns in human body size and their correlates over time. While social researchers, public health specialists, and physical anthropologists have long utilized anthropometric measures as indicators of well being, only within the past three decades have historians begun to use such data extensively. Adult stature is a cumulative indicator of net nutritional status over the growth year and thus reflects command over food and access to healthful surroundings. Since expenditures for these items comprised such a high percentage of family income for historical communities, mean stature can be used to examine changes in a population’s economic circumstances over time and to compare the well being of different groups with similar genetic height potential. Anthropometric measures are available for portions of many national populations as far back as the early 1700s. While these data often serve as complements to standard economic indicators, in some cases, they provide the only means of assessing historical economic well being, as “conventional” measures such as per capita GDP, wage, and price indices, and income inequality measures have been notoriously spotty and problematic to develop.
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