A multivariable linear regression model was developed to identify the factors contributing to USMLE scores. Significant variables in the univariate analysis were considered to be included into the model. All statistical analyses were two-sided and performed using SAS software version 9.4 (SAS Institute ...
Linear regression con- firmed these results, with the years 2011 and 2012 showing significantly lower values than 2006 (Table 2 and Fig. 2). In terms of the explanatory variables, significant differ- ences in TCL among age groups have been found for the entire study population 2006-2012 (...
with PSLQ algorithm. Statistic: Mean, Variance, Multivariable Linear Regression, LRE, Probability distributions. Integration : Double Exponetial, Romberg , 2D Romberg, Complex integration, Newton-Cotes, Filon, FFT, DFT, 2D-FFT, Infinite integral. Numerical Series evaluation, real and complex serie....
has also grown. In this category, can be cited: multivariate statistics and regression models72; multivariate statistics, probability curves and GIS69; regression models73; fuzzy methodology and GIS74; artificial neural network—ANN and multiple linear regression—MLR75; and entropy information...
Spinal tumours were not included in this analysis due to their absence in the sub-cohort. 3.7. Clinical Examination Findings and Pain and Pathology Two binary logistic regression models incorporating RCS for both age and weight were developed to capture the potential non-linear relationships with ...
The significant variables were extracted and used in the multiple linear regression model to assess their predictive effect on treatment time. The stepwise method selected the independent variables to be included in the regression model, based on which an appropriate regression model was created. 3. ...