Sentence examples for single regression analyses from inspiring English sources

Multiple imputation usually involves much more complicated statistical modelling than the single regression analyses commonly reported in medical research papers.

For single regression analyses with one predictor and four control variables, a medium effect size (R) of 0.15, a p value of 0.05, and a power of 0.80, a minimum of 92 cases are needed.

The factors that were found to have a significant correlation with serum SDMA and ADMA in the single regression analyses were used as explanatory variables to create the multiple regression models.

Single regression analyses were executed for each variable that showed a statistically significant difference in CHQ-PF28 means of rank scores using the Mann-Whitney U-test, or a statistically significant relationship with the CHQ-PF28 scores using the Spearman rank correlation coefficients.

Hence the main result is that for single-regression analyses, the peak Z-stat is quite similar across TRs, but for multiple-regression, the peak Z is 56% higher at the shortest TR compared with the longest.

Hence the main result is that for single-regression analyses, the sum-of-Z-stats (size × significance) is quite similar across TRs, but for multiple-regression, the sum-of-Zstats is 100% higher at the shortest TR, compared with the longest (with this improvement lessening at lower dimensionality).

In single predictor regression analyses, plasma CT-proET-1 (log10) correlated positively with age (r = 0.33), serum creatinine (log10, r = 0.40) and plasma NT-proBNP (log10, r = 0.44, all p<0.0001).

Single SNP regression analyses of each additive coded SNP (0/1/2) with each fatty acid in the combined study population of the KOALA and LISA studies at age 2 years revealed that all SNPs are highly significantly associated with all fatty acids except for ALA and EPA (natural log scale, Table 2).

In single model regression analyses the dichotomised biological (health) approach was also less sensitive to QoL outcomes.

Unadjusted ORs (p < 0.05, 95% CI) were calculated using single logistic regression analyses, with falls as the dependent variable.

Factors with a P value ≤ 0.05 by single variable regression analyses were included in a multivariable linear regression model, presented as adjusted coefficient (AC) (95% CI) [ 18].