Spss package v22

The data analysis incorporated an initial descriptive analysis. For the categorical and discreet variables, the proportions were estimated with their corresponding 95% confidence intervals, according to the Wilson method and using the Pearson’s chi squared test to perform comparisons. Alternatively, the Fisher’s exact test was used when 20% of the fields presented an expected number of cases which was less, or equal to five. For the continuous variables, means were estimated with their standard deviation or medians and interquartile ranges in the case of asymmetric distributions. The Student’s t-test or ANOVA were used to analyze the relation between quantitative variables and categorical variables with two levels; or categorical variables with more than two levels, respectively. The condition of normality was previously confirmed via the Shapiro–Wilk test. All the statistical analyses were performed using the SPSS package v22.0 by IBM (Armonk, NY, USA).

The statistical analysis was performed by the SPSS package v.22.0 (IBM, Armonk, NY: IBM Corp). First, Shapiro–Wilk was carried out to assess the normality. Second, a descriptive analysis was employed for all the individuals and separately in the two groups. Finally, a comparative analysis between group0 and group1 was performed. Mean, standard deviation (SD) with the Student t test and median, interquartile range (IR) with Mann–Whitney U test were carried out for parametric and non-parametric data, respectively. In addition, Levene’s test was employed to assess the equality of variances. Moreover, a multivariate analysis was performed using a linear regression (stepwise selection method; Pin = 0.05; Pout = 0.10) to predict the influence of the dominant leg (right or left) data and group (indoor and outdoor football) on the statistically outcome measurements (showed in the prior described analyses). The dependent variable was the left Hoffa’s fat pad and the independent variables were the group and the dominant leg side (right or left). Additionally, a multivariate predictive analysis used linear regression model performed via stepwise selection method and the R² coefficient to stablish quality adjustments. Significant sociodemographic data variable, age, was used as independent variable.