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

An explanatory variable may be dropped to produce a model with significant coefficients.

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

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

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

For more than one explanatory variable, it is called multiple linear regression.

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

The approach generalizes to a model with multiple explanatory variables.