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The model was built using linear regression analysis.
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Body weights (D1, D20 and D40), average daily gain (ADG) (overall, pre- and post-weaning), number of IFNγ secreting PBMC, cytokine concentrations in supernatants, serum Mhyo antibody levels, and serum porcine IgG concentration (all D40) were compared between groups using linear regression built by Generalized Estimation Equations (GEE) accounting for clustering of litters.
Each landmark acted as a CSP and all the others sent 50 requests from which only the minimum RTT is kept to build a delay-to-distance function using linear regression.
Using linear regression analyses, a multivariate model was built containing the combination of factors best associated with at-work productivity loss.
Quantitation was done using linear regression analysis, as described previously29, 36.
Data were analysed using linear regression and logistic regression models.
DESIGN:: Secondary data analysis using linear regression analyses.
An alternative may be using linear regression.
Concentrations were calculated using linear regression analysis.
Multicollinearity was examined using linear regression.
Differences using linear regression models.
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