Sample regression question Using daily data from January 1st 1999 to January 1st

Question

Sample regression question Using daily data from January 1st 1999 to January 1st 2002, British Airways estimated the demand function for 8 of its key European routes. The resulting regression equation was estimated: log Q = - 1.55 - 1.71 log F +1.44 log GNP + 1.58 log A - 0.25 log S (0.16) (0.58) (0.83) (0.37) Where Q is the number of passengers flying with BA on their European routes, F is the average yield per passenger-kilometre, GNP combines the real gross national products for the countries involved, A is advertising on European routes, and S is the average speed of all European flights. Standard errors are in brackets, R2 is 0.93. Given the above information, interpret the estimated demand function for BA. Comment on the significance of this model (use tcritical = 1.96). What implications do these results have for BA in formulating its strategy? What other variables would be of interest to estimate?

Explanation / Answer

1. The estimated demand equation show that the number of passengers flying with the airline is negatively related to the average yield per passenger kilometre, is positively related to the real GDP of the countries involved, positively related to the advertising on Europeans involved and negatively related to the average speed of all European flights. The one per cent increase in the value of three former variables will lead to increase in the number of passengers by more than one per cent.

2. Since the value of R2 is .93 or 93 per cent shows that data is close to the fitted regression line.

3. In formulating its strategy, the airline should consider the above factors like raising advertising expenditure, increase speed of the aircrafts to make them more efficient.

Other factors like price charged, provision of various amen ties in the flight like food, videos etc can be considered.

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