Multiple linear regression-based physical fitness prediction models for Turkish secondary school students

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Mehmet Fatih Akay
Ozge Bozkurt
Ebru Cetin
Imdat Yarim

Abstract

Physical fitness is a necessary component for daily activities. Measurement of physical activity is essential for determining physical fitness rate. This study aims to develop new prediction models for predicting the physical fitness of Turkish secondary school students by using multiple linear regression (MLR). The datasets comprise data of various number of subjects according to the target variables including the test scores of the 30m speed, 20m stage run, balance and hand-grip (right/left). The predictor variables used to develop the prediction models are gender, age, body mass index (BMI), body fat, number of curl-up and push-ups in 30 seconds. Eight physical fitness prediction models for each target have been created with the predictor variables listed above. The performance of the prediction models has been calculated by using standard error of estimate (SEE). The results show that MLR-based prediction models can be safely used to predict the physical fitness of Turkish secondary school students.
Keywords: Physical fitness, multiple linear regression, machine learning, validation.

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