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A correlative equation between FA and DELH, V was well established by using the multiple linear regression (MLR).
The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 32 recently discovered 4-N-aryl-[1,4] diazepane ureas.
The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 65 polymer solvent combinations involving 10 different polymer.
Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods.
Using the fact that adsorption energies of electron donors to catalyst surface are linearly related to activities, the Quantitative Structure Property Relationship (QSPR) for adsorption energies was performed for a set of 24 compounds from 3 different groups, i.e., phthalates, 1,3-diethers and malonates using the multiple linear regression (MLR) analysis.
Cetane Number (CN) was calculated using the multiple linear regression equation.
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This paper used the multiple linear regression model and the particle filter model to decode the hand motion information in two and three dimensions from EEG signals and analyzed the decoding results and the factors that influenced it to compare the decoding features of the two methods and their usability.
Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network.
The factors used for the multiple linear analysis included gender, age, level of education, deployment, physical injuries, mental health problems, loss of family member, use of social support, forced to move, and listened to disaster radio.
The model was produced by using the stepwise multiple linear regression technique on a database that consists of 67 recently discovered 1,3,4-oxadiazole substituted naphthyridine derivatives.
Therefore, the least-square-based time delay estimator using the proposed multiple linear regression model becomes an ideal estimator that does not require a complicated phase unwrapping process.
More suggestions(15)
using the multiple indicator-dilution
using the multiple reaction
using the multiple computer
using the multiple time
using the multiple scale
using the multiple interacting
using the generalized linear
using the multiple column
using the simple linear
using the multiple attribute
using the multiple breath
using the multiple quantum
using the multiple access
using the multiple crystal
using the standard linear
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