Sentence examples for multiple variable regression from inspiring English sources

The data were then fitted by a multiple variable regression model using the maximum likelihood method.

density (DEN), porosity (PORO) and compressional wave velocity (P-WV) using multiple variable regression analysis (MVRA) and adaptive neuro-fuzzy inference system (ANFIS).

This paper presents the empirical correlations developed from multiple variable regression analysis from test results obtained from experimental investigation of soil sample taken from different locations of Gujarat region in India.

This paper presents the empirical correlations developed from multiple variable regression analysis from test results obtained from experimental investigation of soil sample taken from different locations of Gujarat region.

This study considers the use of multiple variable regression analysis (MLR) to predict the California Bearing Ratio (CBR), Coefficient of subgrade reaction K-Value, unconfined compressive strength, Dynamic Cone Penetration (DCP) from maximum dry density (MDD) and optimum moisture content (OMC) of subgrade.

This study considers the use of multiple variable regression analysis (MLR) to predict the California Bearing Ratio (CBR), Coefficient of subgrade reaction K-Value, unconfined compressive strength (UCS), Field dry density from Dynamic Cone Penetrometer (DCP), modified liquid limit and moisture content of subgrade.

Binary descriptive statistics and multiple variable regressions were done.

Multiple variable regressions in these retrospective data modelled the effects on each socio-demographic variable of the others.

Multiple variables regression (MR) was performed to control potential confounding factors and determine the independent contribution of variables to outcomes (T-score or OP).

Reliability was analysed using kappa coefficients, while discriminative power of the best-fitting tests for back- and lower-extremity pain was assessed using a multiple-variable regression model.

Candidate models for multiple-variable regression were those in which individual factors demonstrated an association that was statistically significant or approaching statistical significance, and the individual factors were not highly collinear.