explanatory variables

Also, the range of the explanatory variables defines the information available from them.

A detailed case study approach is also called for by the nature of the explanatory variables that will be proposed.

Among different countries, educational levels and income are the most powerful explanatory variables, with age being a third one.

Consequently, the number of subjects varied when different explanatory variables were used.

The approach generalizes to a model with multiple explanatory variables.

Finally the parameters of the model are computed for the selected explanatory variables.

The explanatory variables were race/skin color, gender and education.

The relevant explanatory variables are listed in Table 5.1.

The work characteristics and social support were more likely to have been misclassified than some of the other explanatory variables.

The definitions of the other explanatory variables are obvious.

In this case, for each data point, a set of explanatory variables is created as follows:

This scheme has the advantage that it retains the information in the explanatory variables.

Explanatory variables in equations (4) and (5) were the measurements taken at Exam 1.

A common reason is the omission of relevant explanatory variables, or dependent observations.

In the process, the model attempts to explain the relative effect of differing explanatory variables on the different outcomes.

This is sufficient to determine which explanatory variables have an impact on the response variable(s) of interest.

In regression problems, the explanatory variables are often fixed, or at least observed with more control than the response variable.

Secondly, several explanatory variables were difficult to measure and were only measured at one point in time.

The model can be augmented by including additional explanatory variables, which would capture differences in scores among different groups.

In statistics, collinearity refers to a linear relationship between two explanatory variables.

Residuals against the explanatory variables in the model.

The error is a random variable with a mean of zero conditional on the explanatory variables.

The degree to which this is possible depends on the observed correlation between explanatory variables in the observed data.

One level of criticism concerned the nature of the explanatory variables themselves (for instance, Sayer, 1982).

Both situations produce the same value for Y regardless of settings of explanatory variables.