the main assumptions of the linear regression model
were met. It is known that if any of these assumptions is violated,
then the economic insights yielded by a regression model may be inefficient.
As Osborne andWaters (2002) observe, “fewarticles report having
tested assumptions of the statistical tests they rely on for drawing
their conclusions. This creates a situation where we have a rich literature
in education and social science, but we are forced to call into question
the validity ofmany of these results, conclusions, and assertions, as
we have no idea whether the assumptions of the statistical tests were
met”. For the two prediction models, OGP and FOM, the following assumptions
were checked, by means of specific statistical tests: the
multicollinearity was evaluated by examining the Pearson product–moment
correlation factor; the linearity of the relationship between dependent
and independent variables was measured by correlation
coefficients and examination of the partial regression plots; the independence
of the errors was checked using the Durbin-Watson statistic;
the homoscedasticity of the errors was tested through a Lagrange Multiplier
test; and the normality of the error distribution was appraised