Sentence examples for subsequent regression analysis from inspiring English sources

Lobar and total lesion load indexes were then used as covariates in subsequent regression analysis.

Furthermore, IMF has a zero-mean distribution, thereby reducing type I statistical error in the subsequent regression analysis.

Although there is limited seasonal variation in multiple job holding (Hipple 2010), our subsequent regression analysis includes month fixed effects, the magnitude of which are relatively small.

The table also includes summary data for variables used in the subsequent regression analysis of changes in AGI by slice of the income distribution, including capital gains shares and income shares, and changes in state tax revenues.

Subsequent regression analysis was carried out using the amount of lengthening as a covariate as this allowed the complication rate and need for additional surgery to be assessed against subgroups of lengthening.

Minitab 13.0 (Minitab Inc., PA, USA) was used for the experimental designs and subsequent regression analysis of the experimental data.

A subsequent logistic regression analysis suggests that fragmented landscape, large gaps in total GDP, and provincial boundaries will increase the likelihood of BITRs between city dyads.

In the current study, Δ-EVLWi was the only significant and meaningful variable associated with 28-day prognosis on ROC analysis, which was confirmed by the subsequent Cox regression analysis and log-rank test.

Subsequent multiple regression analysis with all variables accounted for 72% of the variability in walking speed (p < .0001), with age and rate of EMG rise as the dominant variables in the model.

In the present study, the voxels were selected based on anatomical rather than functional differences in DTI measures between groups because the DTI scan did not measure functional activation, and there was no behavioral task or behavioral activation involved with the selection of voxels for subsequent behavioral regression analysis.

Subsequent logistic regression analysis yielded significant odds ratio with a high statistical power of 92.8% suggesting that the PCOS women with CAG biallelic mean range of <18 and >20 are at a much higher risk to develop obesity and associated metabolic complications.