Previous studies evidently actualized nonlinear regressions as a step forward in defining the true nature of the relationships between anthropometric and physical fitness (PF) variables in trained subjects. In this paper we have sampled 1176 nontrained boys aged 14-16 years and tested them on (1) five anthropometric predictors, including: body height, body weight, triceps skinfold, upper arm circumference, and body mass index (BMI); and (2) five PF criteria measuring: static (static strength) and dynamic muscle endurance (repetitive strength), aerobic endurance, explosive strength, and coordination. Linear (y = a + bx) and nonlinear (second-order polynomial: y = a + bx + cx2) regressions were calculated simultaneously. BMI is found to be the most significant anthropometric predictor of PF status. Although the calculation and interpretation of nonlinear regressions are far more complicated in comparison to those of linear regressions, the variance of the criteria are in some cases far better explained through a significant nonlinear model. Even more, we have found evidence that an exclusive discussion of the linear correlation model could lead to serious interpretative mistakes. This mostly relates to the fact that a linear regression model implies a continuous relationship (dependence) between the predictor and the criteria, while a nonlinear one effectively identifies possible breakpoints in the regression line and consequently highlights the real nature of the relationship between variables.