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tokens: in their targets. Hence, we can conclude that in stars belonging to the transitional branch, the cycle length and the rotation period are uncorrelated. 5.2. Relationship between <italic>P</italic><inline-formula specific-use="simple-math"><sub>cyc</sub></inline-formula> and global stellar parameters We investigated how the cycle lengths are related to the global stellar parameters of our targets. We evaluated the degree of the correlation between and a given parameter by computing the Spearman rank-order correlation coefficient () ( Press et al. 1992 ). This coefficient is a nonparametric measure of the monotonicity of the relationship between two datasets. A value of close to 0 implies that the two datasets are poorly correlated, whereas the closer is to 1 the stronger the monotonic relationship between the two variables. The statistical significance of a given value can be evaluated by computing the two-sided -value, i.e., the probability under the null hypothesys that two invesigated datasets are non-monotonically correlated ( Press et al. 1992 ). In Table 2 we reported the Spearman correlation coefficients between the length of the primary cycles and the various stellar parameters. The corresponding -values are also reported. The values of are very close to 0 in all the cases indicating a poor correlation between and the different variables. 5.3. Relationship between <inline-formula specific-use="simple-math">⟨ <italic>IQR</italic> ⟩</inline-formula> and global stellar parameters As remarked in Sect. 2, the index is proportional to the amplitude of the rotational modulation signature and is related to the non-axisymmetric part of the spot distribution. The amplitude of rotational modulation is a widely used activity index and can be regarded as a robust proxy of the surface magnetic activity. García et al. (2013) demonstrated that the variance of the TSI, computed on a 60-d sliding window, closely mimics the variations of the 10.7 cm radio flux that in turn is a good indicator of the solar magnetic activity (see Bruevich et al. 2014 , for details). We computed the Spearman coefficient between and different stellar parameters to investigate how the mean level of the stellar surface magnetic activity is linked to the stellar properties. In Table 3 we reported the values of for the different parameters. In this case, values are significantly higher then those computed for . A good correlation () is seen between and , , , and . The sign of correlation is positive for , and and negative for and . In Fig. 9 we reported versus the various stellar parameters for which a significant correlation is seen (). Despite the scatter in the data, the different plots clearly show that tends to increase with , , and and has a negative correlation with and . Table 2 Spearman correlation coefficient between and different stellar parameters. Notes. The parameters are ordered by decreasing . Table 3 Spearman correlation coefficient between and different stellar