## 3 Results  
The ANOVAs revealed significant differences between the sets for the number of repetitions completed to failure, MV\({}_{\text{fastest}}\), MV\({}_{\text{last}}\), and %VL (F \(\geq\) 9.3; \(p<0.001\)). The number of repetitions completed to failure and MV\({}_{\text{fastest}}\) were progressively reduced when increasing the number of sets (all \(p<0.001\)). Set 1 revealed the greatest MV\({}_{\text{last}}\) (\(p\leq 0.011\)), while the greatest %VL was observed for set 4 (\(p\leq 0.043\)) (Table 1). The generalized %Rep-%VL relationship revealed an acceptable goodness-of-fit (R\({}^{2}\) = 0.82) and high errors (SEE = 12.4%Rep) (Figure 1). Individualized %Rep-%VL equations were generally stronger (R\({}^{2}\) = 0.92 \(\pm\) 0.04 [0.84-0.99]) and presented lower errors (SEE = 8.8 \(\pm\) 2.4%Rep [3.7-13.0%Rep]) compared to the generalized %Rep-%VL equation. The ANOVA conducted on the absolute errors revealed a significant reference repetition \(\times\) number of sets interaction (F = 18.86; \(p\) < 0.001) as the effect of the increase in the number of sets on the magnitude of the absolute errors was more accentuated using the MV\({}_{\text{fastest}}\) of the first set compared to using the MV\({}_{\text{fastest}}\) specific to each set. The remaining interactions did not reach statistical significance (F \(\leq\) 1.24; \(p\) \(\geq\) 0.298), and the magnitude of the absolute errors was always low (>10%). In addition, a significant main effect of the reference repetition (F = 93.5, \(p\) < 0.001; higher errors using the MV\({}_{\text{fastest}}\) of the 1 st set compared to using the MV\({}_{\text{fastest}}\) specific to each set) and number of sets was also observed (F = 22.9, \(p\) < 0.001; higher errors with increasing number of sets), but not for the regression model (F = 0.07, \(p\) = 0.795) (Figure 2). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable** & **ANOVA** & **Set 1** & **Set 2** & **Set 3** & **Set 4** \\ \hline \multirow{2}{*}{MNR} & F\({}_{3,84}\) = 232.6 & 13 \(\pm\) 1 & 10 \(\pm\) 1 & 7 \(\pm\) 1 & 6 \(\pm\) 1 \\  & \(p\) \(<\) 0.001 & (10–16) & (7–13) a  & (5–9) a,b  & (2–8) a,b,c ,c ,c  \\ \hline \multirow{2}{*}{MV\({}_{\text{fastest}}\) (m\(\cdot\)s\({}^{-1}\))} & F\({}_{3,84}\) = 114.1 & 0.55 \(\pm\) 0.02 & 0.51 \(\pm\) 0.03 & 0.47 \(\pm\) 0.05 & 0.42 \(\pm\) 0.04 (0.34–0.51) & 0.34–0.51 (0.34–0.51) & 0.34,b,c ,b  \\  & \(p\) \(<\) 0.001 & (0.50–0.58) & (0.45–0.58) a  & (0.34–0.53) a,b  & (0.34–0.53) a,b ,b  \\ \hline \multirow{2}{*}{MV\({}_{\text{last}}\)(m\(\cdot\)s\({}^{-1}\))} & F\({}_{3,84}\) = 9.6 & 0.30 \(\pm\) 0.05 & 0.26 \(\pm\) 0.04 & 0.25 \(\pm\) 0.05 & 0.26 \(\pm\) 0.05 (0.01–0.03) a  & (0.16–0.36) a  & (0.17–0.34) a  & (0.15–0.34) a  \\ \hline \multirow{2}{*}{\%VL} & F\({}_{3,84}\) = 9.3 & 46 \(\pm\) 9 & 49 \(\pm\) 8 & 46 \(\pm\) 9 & 39 \(\pm\) 11 \\  & \(p\) \(<\) 0.001 & (26–67) & (35–66) & (29–59) & (21–65) a,b,c ,c  & \\ \hline \end{tabular} Data are presented as mean \(\pm\) standard deviation [range]. ANOVA, analysis of variance; F, Snedecor’s F; \(p\), \(p\)-value. a, significantly different from set 1; b, significantly different from set 2;c, significantly different from set 3 (\(p\) < 0.05).  
\end{table} 
Table 1: Comparison between the four parallel back-squat sets of the maximal number of repetitions performed to failure (MNR), the fastest mean velocity (MV\({}_{\text{fastest}}\)), the mean velocity of the last repetition (MV\({}_{\text{last}}\)), and the magnitude of velocity loss from the fastest to the last repetition (%VL). Figure 1: Generalized relationship between the magnitude of velocity loss (%VL) and percentage of performed repetitions with respect to the maximum number of repetitions performed to failure (%Rep) during the first set of the parallel back-squat exercise. \(R^{2}\), coefficient of determination; SEE, standard error of the estimate; N, number of sets.