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The estimations from the multiple regression method under evaluate the dietary DE and DCP values, showing higher residues and as a consequence higher errors (3.9% for DE and 9.6% for DCP) than substitution, the reason of these high errors could be the correlation between some ingredients in the independent data sets used for multiple regression.
Statistical software NCSS11 was used for multiple regression analysis which was performed to calculate the combined effect of meteorological variables towards dust deposition density.
The Minitab software version 14 (Minitab Inc., USA) was used for multiple regression analysis, analysis of variance (ANOVA), and analysis of ridge maximum of data in the response surface regression (RSREG) procedure.
Quantitative SNP genotypes were subsequently used for multiple regression analysis.
Two methods were used for multiple regression analysis.
The Cox proportional hazard model was used for multiple regression analysis.
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The following characteristics, which were proven to be statistically significant with univariate analysis, were used for multiple logistic regression analysis: age ≥45 years, male, and the G1613A mutation.
The following characteristics, which were proven to be statistically significant with univariate analysis, were used for multiple logistic regression analysis: age ≥45, Plt (×10/μl) ≤14, ALT (≥70), and the presence of G1613A and C1653T.
The rock mass parameters and the grout take data were pre-processed and cleaned to be used as input for multiple regression modelling and Adaptive Neuro Fuzzy Inference System (ANFIS).
Analysis was conducted using SAS version 9.2 (SAS Institute, Inc., Cary, NC, USA) for analytic dataset construction, descriptive statistics, and logistic regression; while Stata SE version 11 (StataCorp LP, CollegeStation, TX, USA) was used for other multiple regression analyses.
Sixteen rainfall datasets were used for the multiple regression.
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