Sentence examples for multiple analysis were from inspiring English sources

Potential collinearity among variables selected for multiple analysis were tested and if associations were present one of the variables was excluded from the Cox model.

The variables selected (P ≤ 0.2) for the multiple analysis were as follows: herd size, methods of cleaning, source of water, management system, presence of cattle introduced from other farms, type of production, and veterinary service.

Multiple analysis was performed with LIMMA package in R computing environment (Smyth 2004).

Multiple analysis is a basic tool for developing a risk model [ 22– 22].

The multiple analysis was undertaken by taking into consideration all the prognostic factors examined in univariate analysis.

Single and multiple regression analysis were used to predict stature based on single and multiple parameters, respectively.

Linear regression analysis and multiple regression analysis were used for statistical analysis.

Standard multiple regression analysis were run to test the model.

Principal components analysis and multiple discriminate analysis were used to identify shelterbelt characters by species.

Exploratory factor analysis (EFA) and multiple regression analysis were conducted to find potential subtests.

Cluster analysis and multiple correspondence analysis were employed, after decomposition of the histological patterns into elementary lesions.