Sentence examples for demand regression from inspiring English sources

This two-step approach takes advantage of the engineering estimate in analysis of physical factors that determine building energy consumption and uses demand regression to further correct the priori engineering estimates based on the observed energy consumption of plots.

The results of a case study shows that the two-step approach makes the standard deviation of the predicting factors within 0.001 kWh/(m2·a) compared with 6.186 kWh/(m2·a) of the engineering estimate or 53.020 kWh/(m2·a) of the demand regression.

Table 7 Bootstrapped regression results Dependent variable = total peak demand   Regression 1 Regression 2 Variables Coefficient z value Coefficient z value Exports (log) 0.08*** 4.15 0.08*** 4.00 Sales to Umeme (log) 0.35*** 3.66     Peak Sales Umeme (log)     0.13 0.95 Off-Peak Sales Umeme (log)     0.28 1.14 Shoulder Sales Umeme (log)     0.03 0.15 Sales to other dist.

The Poisson regression has been used in earlier studies to estimate demand regressions using various count variables.

Starting with the robustness of the results, Wald Chi Square results in Table 3 for both total peak demand and domestic peak demand regressions are robust and the models explain up to 79%% (Adjusted R2) of relationships between the total peak demand and the explanatory variables.

Using the first-stage estimators of demand regressions, the intercluster information is used to estimate price elasticities from the transformed regressions: 3 4 where the new variable are budget share and unit value, respectively, after netted out total expenditure and household characteristics.

Due to unacceptably significant errors from the damping correction factors in the estimation of ductility demands, regression equations for estimating ductility demand reductions achieved by supplemental viscous damping are proposed based on the mean response for the simulated ground motions.

Because married status was a trade-off of the status of having paid work between housewives and working women without family demands, logistic regression analyses were conducted with adjustment for confounding variables other than marital status.

Open image in new window Fig. 3 Electricity prices and demand 3) Nonlinear regression model  .

Trend and descriptive statistical analyses were used to examine the trends of peak demand; Bootstrapped linear regression models were used to assess the significance of electricity exports in total peak demand.

Open an Excel workbook and create 3 worksheets: Regression, Demand Curves and Saves.