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

Answer:

1)True

Picking and picking dependent on essentialness level will both inclination your outcomes

2)True

• In relapse, "multicollinearity" alludes to indicators that are related with different indicators.
• Multicollinearity happens when your model incorporates various components that are associated to your reaction variable, yet in addition to one another.
• As it were, it results when you have factors that are somewhat repetitive.

3)False

Relapse coefficient can be discarded just when relapse coefficients are factually irrelevant.