Part A: Multiple- Choice Questions [5 marks] (highlight correct answer in RED FONT)
Question 1
Which of the following is true of R2?
R2 is also called the standard error of regression.
A low R2 indicates that the Ordinary Least Squares line fits the data well.
R2 usually decreases with an increase in the number of independent variables in a regression.
R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Question 2
Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables.
17
2
3
4

Question 3
Which of the following is true of dummy variables?
A dummy variable always takes a value less than 1.
A dummy variable always takes a value higher than 1.
A dummy variable takes a value of 0 or 1.
A dummy variable takes a value of 1 or 10.

Question 4
If the total sum of squares (TSS) in a regression equation is 81, and the residual sum of squares (RSS) is 25, what is the explained sum of squares (ESS)?
64
56
32
18

Question 5
Standardized coefficients are also referred to as:
beta coefficients.
y coefficients.
alpha coefficients.
j coefficients.

Part B: Discussion Questions [10 marks]
Students are required to answer the following 2 questions in the space provided. Only typed answers will be accepted.
Question 1 [4 marks]

To explain what determines the price of air conditioners, B. T. Ratchford obtained the following regression results based on a sample of 19 air conditioners:
(Y_i ) ̂=-68.236+0.023X_2i+19.729X_3i+7.653X_4i R^2=0.84
se= (0.005) (8.992) (3.082)
where Y = the price, in dollars
X2 = the BTU rating of air conditioner
X3 = the energy efficiency ratio
X4 = the number of settings

At α = 5%, test the hypothesis that the BTU rating has no effect on the price of an air conditioner versus that it has a positive effect. Show null and alternative hypothesis and entire working process. With the result, what conclusion can you draw? (2 marks)

Would you accept the null hypothesis that the three explanatory variables explain a substantial variation in the prices of air conditioners? Show clearly all your calculations. (2 marks)

Question 2 [6 marks]
Model Intercept Slope R2
Log-linear (lnY=B1+B2lnX) 0.7826 0.8539 0.997
t=11.4 t=108.93
Log-lin (lnY=B1+B2X) 7.24 0.0001 0.832
t=80.85 t=14.07
Lin-log (Y=B1+B2lnX) -24299 3382 0.899
t=-15.45 t=18.84

For each model interpret the slope coefficient (3 marks)

For each model estimate the elasticity of Y respect to X, given x=9347.6 and
y = 5113.6. (3 marks)