In order to assess whether the differences between the school tracks were statistically significant, we conducted a regression with interaction effects for the complete model.
After conducting a regression analysis on the material forces with respect to different tensile strength values, a parabolic relationship emerges between the material forces and tensile strengths.
Then we conducted a regression analysis with disclosure predicting depression to test path c.
Further, we conducted a regression analysis with distance as the dependent variable, and all cluster B disorders as predictors, to assess which cluster B disorders were responsible for the positive association between cluster B and distance.
In the second step, we conducted a regression analysis with helpfulness as the dependent variable, and all cluster C disorders as predictors, to assess which cluster C disorders were responsible for the positive association between cluster C and helpfulness.
Having conducted a regression analysis with valence as an independent variable and arousal as a dependent variable, we confirmed that this relationship between valence and arousal was best characterized by quadratic function for both males and females (y = 0.23 x – 0.03 x + 2.11, R =.34, and y = 0.24 x – 0.05 x + 1.97, R =.49, respectively; see Fig. 3).
Multivariate outliers were screened separately for each planned analysis by conducting a linear regression with participant ID as the independent variable (IV) and all primary outcome dependent variables (DVs) for the planned analysis as the regression DVs.
Heritability estimates were calculated by conducting a linear regression with each child and parent/grandparent pair.
Third, we conducted a logistic regression with blocking of variables.
The PROC REG procedure was used to conduct a linear regression with the mean annual prevalence of autism spectrum disorder (symptom phenotype and clinical diagnosis separately) as the dependent variable, and year of birth as the independent variable.
Due to the heterogeneity which is commonly observed in meta-analysis concerning sensitive groups such as MSM, we conducted a meta-regression with 10,000 permutations in a Monte Carlo simulation to explore the sources of between-study heterogeneity with the following covariates: economy status, sampling methods, sample size, and published year.
