In early 2003, the Glasure Transportation Authority, a public agency responsible forserving the commuter rail transportation needs of a large city, was faced with rising operating deficits on itssystem. Also, because of a fiscal austerity program at both the federal and state levels, the hope of receivingadditional subsidy was slim.
The board of directors of GTA suggested that because it has been over five yearssince the last basic fare increase, a fare increase from the current level of $1 to a new level of $1.50 should beconsidered. Accordingly, the board ordered the manager to conduct a study of the likely impact of this proposed farhike.
You, the system manager, have collected data on important variables thought tohave a significant impact on the demand for riders on the Glasure Transportation Authority (UTA). Thesedata have been collected over the past 24 years and include the following variables:
1. Price per ride (in cents) -- This variable is designated P. Price is expected tohave a negative impact on the demand for riders on the system.
2. Population in 1,000s in the metropolitan area serviced by GTA --It is expectedthat this variable has a positive impact on the demand for rides on the system. This variable is designated T.
3. Disposable per capita income -- This variable was initially thought to have apositive impact on the demand for rides on GTA. This variable is designated I.
4. Parking rate per hour in the downtown area (in cents) -- This variable is expectedto have a positive impact on the demand for riders on GTA. It is designated H.
5. Weekly riders in 1,000s. It is designated Y.
You have decided to perform a multiple regression on the data to determine theimpact of the rate increase. Based on the demand analysis you learned in chapter 5, determine first thedependent variable for the estimating demand equation.
Questions:
1. Write the estimated demand model for the data given above (use a 3- decimal points for the estimated coefficients.
2. Provide an economic interpretation for each of the coefficients in the estimateddemand equation you have computed.
3. What is the value of the coefficient of determination? How would you interpretthis result?
4. Calculate the price elasticity using 2012 data. Explain the estimatedcoefficient of the price elasticity you just computed.
5. Calculate the income elasticity using 2012 data. Explain the estimatedcoefficient of the income elasticity you