Spss 16 statistics software

Enter type regression analyses were employed to constitute five different prediction models which use anthropometric measurement results, strength test results, anaerobic power test results, combination of the strength and anaerobic power and combination of all measurement categories. For all equations, the variables which resulted in a lowest possible standard error of estimate (SEE) were used. Reliability of the regression models was expressed by R² and the SEE. The adjusted R², as opposed to the sample R², was used to assess the proportion of variance that could be explained by the independent variables. According to Russell et al. (1998) (link), the adjusted R² considers both the number of predictor variables and the sample size.
This results in the R² being approximately corrected for the upward bias of the sample R², which subsequently provides a more accurate estimate of goodness of fit into the prediction model. The statistical power of the applied analysis ranged from 0.90 to 0.99 in all cases. To determine the relationships with performance of all measured parameters the Pearson product correlation coefficient was used. The level of significance was set at p < 0.05. All data was analysed using the SPSS 16 statistics software (Chicago, IL, USA).

The pharmacokinetic parameters, C_max (the maximum peak of plasma drug concentration), and the time point of peak concentration (t_max) were read from plasma concentration data. The trapezoidal rule was used to compute the area under the plasma concentration–time curve in the first 24 h (AUC_{0–24 h}). SPSS 16 Statistics software was used to run a one-way ANOVA (SPSS Inc., Chicago, IL, USA). A statistically significant p-value of 0.05 was used.