summer mgt 6203 final exam part1 – theory week 1 q1 what would be the null hypothesis for the regression f test for the following equation y b0 b1 x1 b2 x2 u a b1 0 and b2 0 b b1 0 or
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Summer MGT 6203
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Summer MGT 6203 FINAL EXAM
PART1 – THEORY
Week 1
Q1) What would be the null hypothesis for the regression F-test for the following equation
Y = b 0 + b 1 x1 + b 2 x2 + u
A. b1 = 0 and b2 = 0
B. b1 = 0 or b2 = 0
C. b0 = 0 and b1 = 0 and b2 = 0
D. b0 = 0 or b1 = 0 or b2 = 0
Ans: A, the regression F-statistics test the joint null hypothesis that the values of all the
coefficients of explanatory variables except the constant are 0. The intercept is not included
in the hypothesis. (Week 1, slide 24 and 32)
Week 2
Q2) Which type of model does NOT allow for an isolated interpretation of coefficients. Linear-Log
Model
a. Log-Linear Model
b. Log-Log Model
c. Polynomial
Answer: D
Explanation: Lesson 3/ video 5 / slide 4
Week 3
Q3) A group of 20 students spend between 0 and 6 hours studying for an exam (number of
hours being the predictor). The response variable has 2 outcomes: Pass-1, Fail-0. The logistic
regression analysis gives the following output:
Coefficient Std.Error z-value P-value
Intercept −4.0777 1.7610 −2.316 0.0206
Hours 1.5046 0.6287 2.393 0.0167
What is the probability of passing the exam for a student who studies 2 hours?
a. 0.87
b. 0.26
c. 0.34
d. 0.61
Q4 ) Which statement is FALSE about the difference estimator in a linear regression? Selection bias
can negatively impact a difference estimator
a. A difference estimator measures the difference of means between two groups
b. Randomized controlled experiments can have a negative impact on a difference
estimator
c. Self-Selection has a negative impact on a difference estimator
Explanation: Week 4, Lesson 2, Slide 5
Q5) Given a simple linear equation (Y = a + bX + e) which uses OLS to estimate model parameters,
which of the following statements is TRUE?
A) When X and e are correlated it is difficult to estimate model parameters
B) “Orthogonality Assumption” assumes Cov(Y,X) = 0
C) Difference-In-Difference estimator can be estimated with the model parameter ‘b ‘
D) Difference estimator can be estimated with the model parameter ‘a’
Explanation: Week 4, Lesson 2, Slide 3 & 5
Week 5
Q6) Using the stock monthly return in the chart below – calculate the Compounded return.
You may use the standalone built-in calculator app on your computer, but advanced
statistical tools like excel, python, r are prohibited and will trigger a violation.
Date Price Dividend Stock Split Return
Jan 45
Feb 45.35 0.6 2.1%
March 47.2 4.1%
April 48.3 3 for 2 53.5%
May 40.6 -15.9%
A. 37%
B. 121%
C. 46%
D. 61%
Answer: A. 37%. Lesson 6, slides 6-7.
Calculate the returns each month:
Date Price Dividen Stock Split Return Calculation
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