Examples of using Dependent variable in English and their translations into Russian
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The dependent variable is global competitiveness, and independent variables are trust indices,
logit and limited dependent variable models.
Regression coefficient usually shows how much a dependent variable will increase if an independent variable changes.
taken into account to explain a quantitative value, namely, the dependent variable.
The median or mean of nearest individuals determine the value of the estimated, dependent variable for the individuals.
of unit change in the dependent variable all other things being equal,
Therefore, yielding a negative b value would entail the experimental group have scored less than the control group on the dependent variable.
Therefore, yielding a negative b value would entail the coded group as having scored less than the mean of all groups on the dependent variable.
Another major purpose of this section is to indicate why linear hedonic regression models(where the dependent variable is the model price
The main dependent variable is Subjective Well-being index on individual level
The main dependent variable was an index of subjective well-being on the individual level
i.e., a quantitative dependent variable(the amount claimed)
The various plots examined confirmed that the unexplained part of the(log transformed) dependent variable is almost normally distributed
The explanatory variable thus had a dual effect on the dependent variable:(1) an effect proportional to the value of that variable,
their variances by constructing a respecified dependent variable that is"lagged" by weightings on the dependent variable on other locations, where the weights are degree of relationship.
subsequent studies have conceptualized the dependent variable as awareness, attention,
The dependent variable in the statistical analysis is CO2 emission per unit of GDP in 1995 US dollars(CO2/GDP)
The basic assumption underlying any linear regression analysis is that the dependent variable can be expressed as a linear combination(i.e., a weighted sum) of a given set of explanatory factors.
establishing with a high level of confidence that the models explained a significant part of the dependent variable.
the data source used in calculating the dependent variable a vs. b.