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Statistical analyses were based on univariate tests and multivariate linear regression.
To compare BayesB, POCRE, and POCRE0, we chose 2000 SNPs based on univariate tests performed for each SNP separately.
Results comparing the predictive performance of our proposed method with that of models based on univariate tests of significance and models fitted using HyperLasso regression are presented in Table 3.
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The select_univariate module works by selecting k best features based on univariate statistical tests (e.g., Chi square or F tests).
Pairwise comparisons based on univariate F tests revealed no significant difference between the number of fixations on the picture and the product title information (p = 0.96).
Pairwise comparisons based on univariate F tests revealed that pictures were looked at more than any other region (p < 0.001), apart from the contents of the basket (p = 0.128).
*p = 0.01 and **p = 0.04: difference between adults with unsuccessful clinical outcome and the control group (based on univariate F-tests according to MANOVA by group, gender, age).
There was a negative and significant (P<0.01) correlation between TST and the digit span test, digit symbol test, clock drawing test, matrix reasoning, RAVLT, and MMSE based on univariate Pearson's correlation test, with a correlation coefficient ranging from −0.315 to −0.441 (Table 2).
Adjustments for multiple test comparisons were based on univariate and multivariate permutation test.
Statistical significance and adjustments for multiple test comparisons were based on univariate and multivariate permutation test as previously described.
Based on univariate Poisson regression models and likelihood ratio tests (p < 0.2), seven candidate village-level variables were selected for inclusion as candidate variables in initial multilevel analyses together with household-level variables.
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