Discuss the steps in interpreting a multiple regression output.

Question

What will be an ideal response?

Answer 1

1. Examine the model F-test. If the test result is not significant, the model should be dismissed and there is no need to proceed to further steps.2. Examine the individual statistical tests for each parameter estimate. Independent variables with significant results can be considered a significant explanatory variable.3. Examine the model R2. No cutoff values exist that can distinguish an acceptable amount of explained variation across all regression models. However, the absolute value of R2 is more important when the researcher is more interested in prediction than explanation. In other words, the regression is run for pure forecasting purposes. When the model is more oriented toward explaining which variables are most important in explaining the dependent variable, cutoff values for the model R2 are inappropriate.4. A next step would be to diagnose multicollinearity. Simply put, this is the extent to which the independent variables are redundant. A detailed discussion of this topic is beyond the scope of this particular text. However, a simple check for problems can be obtained by taking a look at the Variance Inflation Factors (VIF). Most statistical packages allow these to be computed. VIFs of between 1 and 2 are generally not indicative of problems with multicollinearity. As they become larger, the results become more susceptible to interpretation problems because of overlap in the independent variables.