# regression

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Related to regression coefficient: correlation coefficient
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Ordinary least square method was used to estimate regression coefficients as all variables are stationary at level (table-I).
Higher regression coefficient values, closeness among the calculated and experimental qe values in all the cases showed the suitability of pseudo second order kinetics model for explaining the reaction kinetics.
The regression coefficients for Wall film formation are shown in Table 9.
In the regression analysis, when multi collinearity phenomenon between variables, often will seriously affect the expansion of parameter estimation, model error, and damage the robustness of model, thus eliminating multi collinearity has become an important part in the estimation of regression coefficients.
This new set of regression coefficients comes from a statistical method known as "seemingly unrelated regression" technique (SUR), originally proposed by Zellner (1962).
It shows that the genotypes that have a regression coefficient is not significantly different from 1 (one) or the deviation of the regression is not significantly different from 0 (zero) or a value of KT regression (KT -reg) is not significantly different from 0 (zero) can be categorized as a stable genotype.
The normalised regression coefficients (Table 7) showed that the thickness of the workpiece and the diameter of the tool have the highest influence on the axial drilling force; the tool diameter and the feed rate have the highest influence on the drilling torque, while the sheet thickness has the highest influence on the tapping torque.
The variance for an ordinary least square regression coefficient derived from Fay's BRR is:
The direction of the regression coefficients suggests that HA increases logarithmically with increasing crowd size, increases linearly with increasing crowd density, and is less in stadia where crowds are further from the playing field.
power consumption Subscripts 1-5 = regression coefficient a = air adp = apparatus dew point amb = ambient air data = based on heat pump performance data db = dry bulb evap = evaporator in = indoor return air m = mass flow rate max = maximum predicted = value calculated from model rat = at rating condition wb = wet bulb
The null hypothesis was that the regression coefficient for that variable was zero.

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