Question

QUESTION 2

1. In multiple linear regression analysis, the number of independent variables should be

   		as large as possible.
   		more than 5.
   		guided by economic theory.
   		enough to guarantee that statistical significance is achieved.

QUESTION 3

1. Omitted variable bias occurs when

   		always occurs when performing simple linear regression analysis.
   		independent variables that should be included in the analysis are not included and those independent variables are related to the variables in the regression model.
   		independent variables that should not be included in the analysis are included in the analysis.
   		always occurs when performing multiple linear regression analysis.

QUESTION 4

1. Multiple linear regression analysis determines the

   		linear relationship between the dependent variable and many independent variables.
   		true value of the population slope coefficient.
   		linear relationship between the dependent variable and exactly one independent variable.
   		true value of the population intercept.

QUESTION 5

1. The “holding all other independent variables constant” condition is important

   		because it comes at the end of every definition in economics.
   		because economists want to know how a change in the dependent variable affects the independent variable.
   		to ensure that the error term is correlated with the independent variables.
   		to ensure that we are correctly estimating marginal effects.

Answer 1

2. The number of variables you include in your multiple regression analysis are needed to be guided and based on your economic theory. There is no such limit to the number of variable that can be introduced into the model. This choice of number of variables should be carefully made keeping in mind the need and demand of the theory that you are pursuing. Thus, option C is correct.