Sentence examples for multiple regression corrected from inspiring English sources

Specifically, using a GLS multiple regression corrected for phylogeny, we found a significant association between the proportion of seminiferous tubule tissue in the testes and both CTM (t = 3.39, P = 0.019) and body mass (t = −3.88, P = 0.012).

Multiple regression analyses corrected for age, sex and disease duration revealed a significant trend toward decreasing sTNF-RI and sTNF-RII levels across the TNF-RII genotypes (TT > TG > GG) of patients with established disease (P for trend = 0.01 and P for trend = 0.03, respectively).

Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential.

To avoid the multiple regression models to correct the EV for structural herd effects (changes in distribution of parities and lactation stages) we used a single animal based indicator variable for each trait of interest, such as incidence rate of cow mortality 1 100 DIM in multiparous cows.

This result is in good agreement with the results obtained using ordinary complete case multiple regression analysis without correcting for the mercury effect [ 20].

Both specific gravity adjusted and unadjusted urinary BPA concentrations were reported in the univariate analyses, while log-transformed unadjusted BPA concentrations were used in the bivariate and multiple regression models to correct for departures from normality of the regression residuals.

By using multiple regression analyses, we can correct for possible confounders.

Stepwise multiple regression was used to correct for age at implantation, and the higher age in the ICD group.

Multiple regression models were used to correct for confounding factors to assess the association between mRNA expression levels of different biomarkers, risk factors or drugs.

To assess the association between IgG and IgM antioxLDL abs and drugs, multiple regression models were used to correct for confounding factors.

Multiple regression analyses of log transformed data corrected for age, sex and disease duration revealed a significant trend of decreasing soluble tumour necrosis factor receptor I (sTNF-RI) and sTNF-RII levels across the genotypes (order: TT > TG > GG) of patients with established disease (P for trend = 0.01 and P for trend = 0.03, respectively).