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A multiple regression analysis conducted with marijuana use as the dependent variable revealed that GPA, popularity at school, and CES-D scores accounted for a significant portion of the variance in adolescent marijuana smoking (Table 1).
The vast bulk of mankind, acting upon what was taught to their parents before the latter were eight years of age (a reasonable enough procedure when societies, like the physical world, were characterized by the equilibrium of the climax) is acting upon assumptions that have little to do with the present 'reality' of rapidly changing systems of dependent variables.
Dependent variable recording is assessed for consistency throughout the experiment by frequent monitoring of interobserver agreement (e.
To enhance the understanding and use of the dummy variable coefficients in Equation (6b), the interpretive framework for comparison of the coefficients can be shifted to an "average" for the dependent variable.
The use of a series of main-effect multiple regression models (using ordinary least squares estimates) that measure the impact of all independent variables on the dependent variable (practice preference score) at Time 1 and Time 2 separately.
The process is more advanced than multiple regression because you can have more than one dependent variable, it allows for the measurement of indirect effects, and it takes into consideration the interrelationships of all of the variables.
The odd-numbered columns include only the Beige Book variable, one lag of the dependent variable, and a constant; the even-numbered columns add three more lags of the dependent variable to the specification.
Means and standard deviations associated with the dependent variable and three independent variables are displayed in Table 1.
Multiple regression is a versatile and powerful statistical method that can be used to model simultaneously the effects of multiple independent variables on a dependent variable (for example, Cohen & Cohen, 1983; Fox, 1997; Pedhazur, 1997).
Regression between the independent variable (service quality and image) and the dependent variable (behavior change) showed that there was a significant inverse correlation between these two variables.
where the dependent variable is either the GRP growth rate or GRP itself (or per capita GRP), Resource is a measure of natural resource abundance, X is a vector of controls, r and [gamma] are, respectively, regional and time fixed effects, i indexes regions, t is a year index, and [epsilon] is the error term.
In columns (1) to (3) we report results where the dependent variable is the level and first difference in number of academic publications at a university in a year.