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Panel B is based on the annual proxy variable regression.
The first two parts are deterministic parts and are modeled separately with dummy variable regression models.
The data were then fitted by a multiple variable regression model using the maximum likelihood method.
Using this newly created variable, regression results in Table 6 are not qualitatively changed.
The results for the dummy variable regression model in (1) are reported in Table 2.
A wavelet-based latent variable regression (WLVR) method was developed to perform simultaneous quantitative analysis of overlapping spectrophotometric signals.
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Buckingham-π theorem along with multi-variable regression technique is applied to establish the correlation.
This ANN model performs better when compared to linear multi-variable regression.
We used dummy-variable regression model structure for analyzing the winter weather impact on truck traffic.
In this study, single and double-variable regression models (UPV and RN) were used in predicting concrete compressive strength.
Open image in new window Fig. 2 Results of dummy-variable regression models of weekdays for trucks.
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