Please assist me with the attached.
There are two documents posted. The .xls has the data and the regression - see tabs at bottom left. The .doc has the actual problem. The question and background is in black, my answers so far are in blue and the questions I need answered are highlighted in yellow ? they are 6,7,8,9.
Early in 1993, the Southeastern Transportation Authority (STA), a public agency responsible for serving the commuter rail transportation needs of a large Eastern city, was faced with rising operating deficits on its system. Also, because of a fiscal austerity program at both the federal and state levels, the hope of receiving additional subsidy support was slim.
The board of directors of STA asked the system manager to explore alternatives to alleviate the financial plight of the system. The first suggestion made by the manager was to institute a major cutback in service. This cutback would result in no service after 7:00 pm, no service on weekends, and a reduced schedule of service during the midday period Monday through Friday. The board of STA indicated that this alternative was not likely to be politically acceptable and could only be considered as a last resort
The board suggested that because it had been over five years since the last basic fare increase, a fare increase from the current level of $1 to a new level of $1.50 should be considered. Accordingly, the board ordered the manager to conduct a study of the likely impact of this proposed fare hike.
The system manager has collected data on important variables thought to have a significant impact on the demand for rides on STA. These data have been collected over the past 24 years and include the following variables:
1. Price per ride (in cents) - This variable is designated P in Table 1. Price is expected to have a negative impact on the demand for rides on the system.
2. Population in the metropolitan area serviced by STA - It is expected that this variable has a positive impact on the demand for rides on the System. This variable is designated T in Table 1
3. Disposable per capita income - This variable was initially thought to have a positive impact on the demand for rides on STA This variable is designated I in Table 1
4. Parking rate per hour in the downtown area (in cents) this variable is expected to have a positive impact on demand for rides on the STA. It is designated H in Table 1.
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