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Thus, relationships between the variable responses FSB and Δtads (defined as the difference between the breakthrough time and exhaustion time) and two input variables (H and u0) were developed.
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The determination coefficient (R2) value of 0.922 showed that the regression models adequately explain the data variation and represent the actual relationships between the variables and response.
A high regression coefficient between the variables and response (R2 = 0.9974) supported excellent evaluation of experimental data by second order polynomial regression model.
A high regression coefficient between the variables and response (r 2 = 0.9974) corroborate eminent evaluation of experimental data by second-order polynomial regression model.
The VIP indicator can describe correlations between the variable (X) and response (Y).
MLR is performed either to study the relationship between the response variable and predictor variables or to predict the response variable based on the predictor variables.
Such a relationship between the response variable and explanatory variables is called a profile.
Fischer's exact test and the chi square test were applied to ascertain the relationship between the response variable and explanatory variables.
The model validity (i.e. presence of chance-correlations between the variables and the responses) were determined via 100 permutation experiments [59], [60].
It was observed from the results that SRSM model is efficient for analyzing the stochastic relation between the response variable and uncertain explanatory variables.
Table 8 summarizes the results of the regression analysis which show the relationship between the response variable (safety indicators) and predictor variables (speed, geometry and roadside features) in the southbound direction.
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