Georgia Institute Of Technology MGT 6203 MIDTERM - SOLUTION KEY - PART 1 all answers verified 2021

Document Content and Description Below

MIDTERM – SOLUTION KEY THEORY QNS *Q1) Consider a linear regression model with 2 independent variables (assume both are correlated with the response variable). If we add an interaction term betwe... en the independent variables to the model, how will the model be affected: A) The R2 will increase (or remain the same) with certainty while the adjusted R2 can increase or decrease B) Both the R2 and adjusted R2 will increase with certainty. C) The R2 will decrease (or remain the same) with certainty while the adjusted R2 can increase or decrease D) Both the R2 and adjusted R2 will decrease with certainty. Solution A: The R2 is bound to increase with the addition of new variables or stay the same if the interaction variable doesn’t improve model. The adjusted R2 adds a penalty term on the number of variables in the model, hence it may go down or up (if the new interaction variable offers significant predictive performance). (Week 1 Lesson 4) *Q2) Consider the correlation matrix of independent variables below. What pair of variables would be least valuable to use in a linear regression model? A) Education and Income B) CompPrice and Price C) Population and CompPrice D) Income and Price Solution B: An assumption of linear regression is the lack of multicollinearity in the independent variables. As a result, it is natural to reject the most highly correlated pair of variables, which from the correlation matrix above is clearly Price and CompPrice. (Week 1 Lesson 8) *Q3) Which of the following is NOT a binary dependent variable? (a). Whether a customer will default on his debt. (b). Would a student pass a course. (c). Change in value of an investment. (d). If a firm would go bankrupt in the next year. Answer – Option C (Week 4 Lesson 2) Change in value of an investment can have more than two values. Rest all only take two values. Hence Option C is not a binary dependent variable Q4)In the model log(Y) = b0 + b1*log(X), the elasticity of Y is the percentage change in Y (the dependent variable), when X (the independent variable) increases by one unit. A. False B. True Answer: False (A) (Week 3 Lesson 4) Explanation: Elasticity is the percent change in Y when X increases by 1% *Q5) The odds for your team winning is 0.6 in the next game. What is the probability of your team losing in the next game? (A) 0.4 (B) 0.375 (C) 0.6 (D) 0.625 Ans) (D) (Week 4 Lesson 1) odds = 0.6 = p/(1-p) This means that p = 0.6/1.6 = 0.375 Hence probability of team losing = 1-0.375 = 0.625 *Q6)While calculating a difference in difference, we run a regression which is as follows: lm( y ~ d1 +d2 + d3)where d1 and d2 are dummy variables and d3 is their interaction term. We thus get its coefficients according to the below equation: Y = a + b*d1 + c*d2 + d*d3 What is the difference in difference estimator? (A) a (B) (d-c)-(b-a) (C) a+b+c+d (D) d Answer: (D) d - Difference in difference estimator is given by coefficient of interaction term (Week 5 Lesson 5) [Show More]

Last updated: 1 year ago

Preview 1 out of 6 pages