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In addition, an analytical investigation based on a simple linear analysis was conducted to predict ultimate load.
Most design codes do not pay much attention to such deformations and adopt a simple linear analysis for the calculation of deflections.
Simple linear analysis would miss such an association.
The influences of variables on several factors including mean-CCA-IMT, max-CCA-IMT, total leukocyte count, monocyte count and neutrophil count were explored by simple linear analysis, using Pearson's method for continuous variables.
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The goodness of fit (R) between Tvol and age was better in nonlinear regression analysis than in simple linear regression analysis (boys, y=1.2273e0.8836 x R=0.8069; girls, y=11.31 x+11.841 x+11.84, R=0.944).
Simple linear regression analysis and multiple linear regression analysis were further performed to verify the results.
In the analysis of relationship between cognitive scales and HIS, K-MMSE, CDR, and CDR-SB showed significance relationship with HIS, which were further verified by simple linear regression analysis and multiple linear regression analysis.
However, history of hypertension, history of diabetes, body mass index (BMI), history of hyperlipidemia, and family history of stroke did not have any significance (Table 4), which were further verified by simple linear regression analysis and multiple linear regression analysis.
Associations between serum A-FABP and other parameters were first analyzed by simple linear regression analysis and then by multivariate logistic regression analysis.
The results were further improved by using multiple regression analysis as compared to those obtained by the simple linear regression analysis.
Simple linear regression analysis was performed using JMP IN version 5.1 statistical analysis program (SAS Institute Inc., Cary, NC) to determine the significance of the association between the variables of interest and create the graphics.
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