A statistical model using multiple regression analysis to predict equilibrium and sway index - Bendo, A.; Brovina, F. (Journal of Physical Education and Sport, 2024)
Balance and equilibrium refer to the ability to maintain an upright position and make the necessary adjustments. Achieving and maintaining postural balance requires intricate coordination and integration of various sensory-motor and biomechanical factors. The main objective of linear statistical analysis is to predict the relationship of a respondent variable in terms of predictor variables in a linear function or multiple linear functions. The purpose of this study is to estimate and select the best model on the variables that affect the balance, through the application of the multiple regression method in both 1L_EO and 1L_EC balance tests, to a sports team. This method allows for determining the overall fit and the relative contribution of each of the predictors to the total variance explained. The equation of the regression of a female volleyball player`s team emerged these parameters as the best predictor of biomechanical parameters for the balance tests: sway area is the only best predictor for the equilibrium according to this model for 1L_EO, any increase of sway area of 1cm², there is also an increase of sway index with 0.195 cm, followed by a decrease of equilibrium with 0.2%. Postural sway is increased when eyes are closed, due to the loss of the orientation on the base of support. The training technique strongly affect the mechanical output muscles, as the motors that generate explosion maximal force and also improving equilibrium. Application of this statistical model in a sport team such as volleyball, has confirmed that Multiple Linear Regression (MLR) method is very effective and it is highly recommended to estimate and to select the best model on the variables that affect the balance. Finally, all strength exercises have improved the biomechanical parameters, including the balance ability.