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Such designs could bring mixture of gain and loss of power, according to the multiple correlation levels considered.
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Bonferroni correction was used to avoid type I error with multiple correlation testing; hence, significance level was set at P ≤ 0.01.
Assuming at least three manifest indicators per latent predictor, a squared multiple correlation of 0.20, a 0.05 α level, and a two-tailed test, a sample size of 166 would be needed to achieve a power of 0.95, which would be satisfactory for the proposed analyses.
Multiple correlation analysis (Spearman rank correlation coefficient r; significance level <0.05, Bonferroni corrected) was used to investigate relationships among all investigated parameters (Table S2).
Assumptions for the power calculations include a significance level of 5% and a multiple correlation coefficient of 0.2 relating PFOA exposure to potential confounders.
Multiple correlation analysis showed a significant negative correlation between PB inhibin A levels and gestation (r = −0.45, P<0.05) and PB PaO2 (r = −0.5, P = 0.008) and also between sFLT-1 and PB PaO2 (P = 0.03).
To estimate correlation, multiple correlation coefficients were applied.
Linear multiple correlation, rank correlation, circular linear correlation, circular rank correlation, and multiple regression were used as statistical analyses.
Table 1 Sets of selected TPC-H queries and their correlation levels Set number TPC-H Query name Correlation level 1 Q11,Q12 none 2 Q17,Q22 none 3 Q1,Q17 partial 4 Q1,Q18 partial 5 Q6,Q17 partial 6 Q1,Q6 full 7 Q14,Q19 full 8 Multiple Q1s full 9 Multiple Q3s full 10 Q1,Q14,Q18 mixed 11 Q1,Q3,Q11,Q14,Q17,Q19 mixed.
The detail signals have weak multiple correlation, while the approximate signal has strong multiple correlation.
Note: SMC squared multiple correlation.
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