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Using this newly created variable, regression results in Table 6 are not qualitatively changed.
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Comparing the actual simulation outputs and the results from the Eq. (10), it can be observed that the average error is around 5%%, indicating that the multi-variable regression results for the constants C1 and C2 are reliable.
With regards to the demographic variables, the regression results show that the male and female participants differ in their preferences for the four types of leadership.
Combining both amorphous components into a single component variable for regression resulted in lower error statistics and equally good predictions of crystalline components.
RESULTS: Multivariable logistic regression results showed 4 variables that differentiated between native and converted light smokers.
This paper presents the empirical correlations developed from multiple variable regression analysis from test results obtained from experimental investigation of soil sample taken from different locations of Gujarat region in India.
This paper presents the empirical correlations developed from multiple variable regression analysis from test results obtained from experimental investigation of soil sample taken from different locations of Gujarat region.
Table 3 Regression results Variable Model 1 Model 2 Coef.
However, we repeated the analyses that produced the key findings using continuous variable regression and obtained similar results.
The use of nominal values eliminated the need to apply a variance-stabilizing transformation to the dependent variable and subsequently retransform regression results to obtain dollar values.
The correlations among all research variables and the multivariate regression results are presented in Tables 1 and 2, respectively.
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