Statistica v 6

The SE values for all theses were calculated. Statistical analyses of data were performed using the statistical package STATISTICA v. 6.0 software (StatSoft, Tulsa, OK, USA). A one-way ANOVA was used to test the hypothesis of treatment differentiation with respect to Fusarium of both seminal root systems and stems. The means were then compared using Tukey’s honestly significant difference (HSD) test, with a significance level (p) of 0.05.

The average values of all events of the same type were obtained for each subject enrolled, and these values were used for further analysis and for the calculation of the group mean values and their standard deviations as basic statistics.
We then checked for possible simultaneous effects of age, sex, and obstructive AHI (independent factors/predictors) on the duration of apnea and hypopnea during NREM and REM sleep separately (dependent variables) by means of the General Regression Models module offered by the commercially available software STATISTICA v.6, StatSoft Inc., Tulsa, OK, USA (this software was also used for all other statistical tests carried out in this study). This module allowed for the building of models for designs with categorical predictor variables, as well as with continuous predictor variables. For each dependent variable, three partial correlation coefficients were obtained, one for each independent factor, together with its statistical significance. Moreover, following the Cohen’s [11 ] indications, we considered correlation coefficients 0.10, 0.30, and 0.50 as corresponding to small, medium, and large sizes, respectively.

Since morphological features can depend on the size of the individual rather than species or population level characteristics within a particular location [31 ,63 ], all continuous, meristic, and ordinal categorical features were size-adjusted before any analysis using the method of Lleonart [64 (link)]. This procedure scales all individuals to the same size (i.e., bell diameter) and adjusts each morphological feature taking into account potential allometric differences among species or within species in different habitats using the formula:
$Y^{\mathrm{*}}=({{f1}_m/{f1}_i)}^b$
where Y* is the size-corrected morphological feature in the individual i_,Y_i is the measured morphological feature in individual i, f1_iis the bell diameter of the individual i, f1_m is the mean (arithmetic) bell diameter of all individuals in the dataset, and b is the within-location, within-lineage, slope of the regression between log₁₀(f1_i) and log₁₀(Y_i). Correlations were used to determine whether significant relationships existed between bell diameter and size-corrected continuous (Pearson correlations) and ordinal categorical (Spearman correlations) features using the software Statistica v.6 (Statsoft, Inc.). Nominal categorical features exhibiting a significant correlation with size were not included in subsequent analyses.

Statistical analysis was performed using the software Statistica v. 6 (Statsoft Inc., Tulsa, OK, USA). Data were first tested for normality with the Kolmogorov–Smirnov test and for homogeneity of variance with the Brown–Forsythe test. Comparisons between means in different inoculation and co-inoculation treatments at the end of the experiment were made by using generalizer linear models (GLM). Significant test results were followed by Fisher tests (LSD) for identification of important contrasts.