Sentence examples for multiple variable regressions from inspiring English sources

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.

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.

Adjusted associations between SRDH, insurance and household income were assessed in a multiple variable regression model.

Even when controlled for other variables with a multiple variable regression analysis, AKI remains an independent risk factor for hospital mortality.

The standard method of accounting for potential alternative explanations in epidemiological studies (i.e. confounding) is to adjust for them in multiple variable regression models.