In addition, a tailored support vector regression algorithm designed to produce a robust (measurement error-insensitive) calibration model is developed, tested, and compared against these established regression algorithms.
In this paper, specific statistical tools were employed to establish regression models for the COP and the capacity of a water-lithium bromide single-effect absorption chiller.
We evaluated modeled evapotranspiration with an eddy covariance (EC) result from the established regression equation, and a scatter correlation plot for the measured and estimated transpiration indicated that the model estimate is within acceptable limits, with a correlation coefficient of R2 = 0.796.
Hence, measured data in this interval is suitable to establish regression relation of density with other available curves and thereby in construction of a synthetic density curve.
In the following experiments, we wanted to assess the quality of the encodings implemented in jCompoundMapper for several established regression and classification benchmark problems.
We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies.
As in Hoeksema and Thompson [ 41], we then used a previously established regression equation of total dry mass (in grams) on needle-bearing stem length (green length, in millimeters) to estimate total dry mass of each plant at the time of inoculation (ln(mass) = ln(green length) × 1.97 – 7.823).
This approach consists of generating experimental design matrix and intermediate operation matrix, establishing fitted regression equations, and deriving thermal-hydraulic correlations for heat transfer equipment, which is applicable to obtain correlations with multiple independent variables with interactive impacts.
To show the efficiency of these combinations suggested in this paper, DEA analysis is again conducted on them as well as the results of the experiments of 34 times used for establishing multiple regression equations.
Second, we investigated the moderation effects of age by establishing latent regression models with the TEDDICS factors as outcome variables, teachers' age, the CSE factors, and their interaction (Age × CSE) as predictors.
