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Significance of correlation and regression and data normality test of dependent variables (characterization parameters of SPM) indicated that regression-based models can be used for prediction of physico-chemical characteristics of the SPM by using the physico-chemical characteristics of fine inert material at any other/new construction site.
The slope intercept form of a linear equation (y=mx+b) is the means by which to do this, where y is the predicted value of the dependent variable (net outlays), m is the slope of the line, x is the independent variable (POM w/OCO position), and b is the intercept of the line with the Y-axis.
Each regression controls for state and national employment, county population growth rate, county unemployment rate, and lagged dependent variables.
To asses' classification of observations by logistic regression, six predictor variables were entered into model and one dependent variable.
Regression between the independent variable (image) and the dependent variable (purchase) indicate that there is significant positive correlation between these two variables.
The dependent variable is the ratio of investment to capital stock, but very similar results obtain when the dependent variable is the logarithm of overall gross investment.
In regression analysis we consider that the dependent variable y is a function of one or more independent variables of the type: y= f([x.
76 showing that independent variables have a 76% effect on the dependent variable which shows that up to 76% dependent variable is caused by the independent variables.
And with the unemployment rate as the dependent variable, the coefficient on PRESIDENT changes only slightly, from -1.
The logarithmic gains simply represent the percentage change in the logarithmic value of the steady state of a dependent variable and net flux or local flux as a result of an infinitesimal change in the logarithmic value of an independent variable [15], which are computed as follows:
000) and the dependent variable (ROA) is a strong connection (Table 1).
Stated another way, the value of the dependent variable is thought to depend on, or be influenced by, the value of the independent variable.