Question

Please declare each statement true or false and explain your response carefully:

1. Bias should never be traded off for significance in choosing variables for an equation.

2. A high R2 for an equation with low significance for all of the independent variables suggests multicollinearity.

3. The constant should be omitted from a regression specification only when its value is believed to be negligibly small.

Answer 1

Ans:

1)True

Picking and choosing based on significance level will both bias your results

2)True

In regression, "multicollinearity" refers to predictors that are correlated with other predictors. Multicollinearity occurs when your model includes multiple factors that are correlated not just to your response variable, but also to each other. In other words, it results when you have factors that are a bit redundant.

3)False

Regression coefficient can be omitted only when regression coefficients are statistically insignificant.