Sentence examples for variable regression analysis from inspiring English sources

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.

Instrumental variable regression analysis was performed with Stata, version 9.2 (Stata Institute, Texas, USA).

Although these results are not shown in tables, instrumental variables regression analysis indicates an IV estimate of γ of −2.36 with a corresponding p value of 0.26.

To quantify the relationship between two groups of variables, regression analysis is used to fit a model to the observed data.

The multi-variable regression analysis of the maximum score sequentially removed nurse experience (P = 0.82, r2 = 0.49), nurse patient ratio (P = 0.72, r2 = 0.49) and nurse rating of patient risk of near or actual cardiopulmonary arrest (P = 0.06, r2 = 0.51), leaving the case-control status (P < 0.0001, r2 = 0.49) as the only factor significantly associated with the maximum Bedside PEWS score.

In order to exclude confounding variables, regression analysis of clinical variables was performed.

To assess the individual influences of variables, regression analysis was performed using 3 consecutive models.