PART A

1. Sam claims that the true mean price for a Sydney to Osaka economy-class airline ticket is in excess of $800. You wish to test this claim, so you randomly sample 20 Sydney to Osaka economy-class ticket sales and record the following:

Average price for the sample = $830

Sample standard deviation = $80

1. State the null and alternate hypotheses.
2. Test whether Sam is correct at the 5% significance level.
3. What is the probability of a Type I error in the test conducted in (b)?

2. Sally claims that the true mean price for a Sydney to Singapore economy class airline ticket $1000. The population standard deviation is $200. You wish to test this claim, so you randomly sample 20 Sydney to Singapore economy-class ticket sales and record the following:

Average price for the sample = $880

1. State the null and alternate hypotheses.
2. Assuming the true average Sydney to Singapore economy-class airline ticket price is $1100 and the population standard deviation is $260, calculate the probability of a Type II error when testing (a) at the 5% significance level.
3. Assuming the true average Sydney to Singapore economy-class airline ticket price is $1060 and the population standard deviation is $260, calculate the probability of a Type II error when testing (a) at the 5% significance level.
4. What is the relation between the power of the test and the distance of the true population mean from the hypothesised mean?

3. You have the following dataset for X (independent variable) and Y (dependent variable). Do this by hand and show ALL working.

X Y

2 10

4 12

3 11

5 13

4 7

7 15

1. Calculate the covariance of X and Y.
2. What are the beta coefficients for the regression line, Y on X?
3. Given that the standard error of the regression,  S , is equal to 2.2796, test whether b1 is positive, at the 5 significance level.
4. Calculate the coefficient of variation for Y.

PART B

Refer to Coshall, J., 2000, "Spectral Analysis of International Tourism Flows",

Annals of Tourism Research , 27, 3, pp. 577-589.

1. Why might tourism flows contain cyclic patterns? Provide some examples.
2. What did the author do to the outbound tourist flow to transform it into a stationary series? How can he test whether the transformed series is stationary?
3. Interpret Figure 2 (page 581). What is the graph telling us? What is measured on the x-axis?
4. What is the reason for examining the relationship between exchange rates and tourism flows? Which time series do you expect to be lagging/leading, if any (page 582)?
5. On page 586, what does the author find in regards to the relationship between tourism flows and exchange rates? What is his explanation for his findings?
6. "…the relevance of explanatory factors that influence demand for international tourism varies according to the mode of transport" (page 586). Explain how the author supports this statement.

PART C

1. Download the data file, FinalExam.xls. We are interested in analysing the

probability that a traveller from Sydney to Canberra decides to travel by air rather than alternative modes of transport, and include as exogenous variables the length of stay in Canberra ( LENGTH ), the age ( AGE ), gender ( FEMALE ) and income ( INCOME ) of the traveller in $’000, and the price of the airfare ( PRICE ). FLY is a binary variable, and is equal to 1 if the individual travelled by air and 0 otherwise.

1. Estimate a linear probability model (LPM) of FLY on LENGTH , AGE , FEMALE , INCOME and PRICE . Write down the regression equation.
2. Estimate a probit model of FLY on LENGTH , AGE , FEMALE , INCOME and PRICE . Write down the regression equation.
3. Comment on the difference (if any) between the regression results in (a) and (b). What are the drivers of these differences?
4. Suppose there is a male traveller who is planning on staying in Canberra for 4 days, he is 45 years of age, earns $45,000 per year, and flight tickets from Sydney to Canberra are currently priced at $150. Use the probit model in (b) to determine the probability that he will travel by air.
5. Refer to (d). What is the marginal effect of a $5 increase in ticket prices on the above traveller’s probability of using air travel? Use the probit model estimates.

2. Explain the difference between correlation and causation. Provide a context in which a researcher/analyst who has mistaken correlation for causation results in adverse consequences. Your example need not be aviation related.

Price: $31.55

Solution: The downloadable solution consists of 13 pages, 1855 words and 2 charts.
Deliverable: Word Document