Sentence examples for varying linear regression from inspiring English sources

(2013), combines monitored and modelled concentrations by regressing the concentrations from the monitoring stations against the modelled concentrations via a spatially varying linear regression.

The aim is to track the varying parameters of linear regression models in situations where regressors have not only slowly changing parameters but also rapidly time varying ones.

Using a panel of maize inbred lines varying in carotenoid content, linear regression analysis revealed a statistically significant negative correlation between copy number of CCD1 and carotenoid content, an effect likely mediated through the resulting elevated levels of endosperm CCD1 transcripts in high copy number lines.

The average eGFR slope (mL/min/1.73 m/y) for each patient was calculated using linear regression with varying intercepts and varying slopes, but without covariates, for the estimation of annual changes in eGFR.

In this paper, we deal with the problem of tracking smoothly time-varying parameters of linear regression channel models occurring in mobile radio communications.

Columns 1 and 2 use local linear regression with slopes varying on either side of TTP cutoff and with bandwidths of 10 and 5 percentage points, respectively; robust standard errors in parentheses.

As a sensitivity analysis, the role of loss of residual renal function (calculated by GFR at relevant month – baseline value) on IL-6 concentrations over time was explored as a time-varying covariate in the multilevel linear regression model.

Station-wise linear regression analysis has shown spatially varying regression coefficients.

To determine the effect of varying amounts of regular physical activity on BMD, linear regression analysis was used.

Linear regression analyses showed that there were varying relationships between Russian wheat aphid density and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.91 to 0.01.

GWR is a spatial and local form of multiple linear regression that considers and models the spatially varying relationships between explanatory variables and the response variable [53]-[55] [53]-[55]