Early in 2021, the Southeastern Transportation Authority (STA), a public agency responsible for serving the commuter rail transportation needs of an Eastern city, was faced with rising 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 the major cutback in service. This cutback would result in no service after 7:00 p.m., no service on weekends, and a reduced schedule of service during the midday period Monday through Friday. The STA board 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 fare increase, a fare increase from the current level of $1.00 to a new level of $1.25 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 28 years and include the following variables. Price per ride (in cents): This variable is designated X1 in the following data table. Population in the metropolitan area serviced by STA (in thousands): This variable is designated X2 in the table below. Disposable per capita income (in dollars): This variable is designated X3 in the table below. Parking rate per hour in the downtown area (in cents): This variable is designated X4 in the table below.

		Price		Disposable
	Weekly	per		per Capita	Parking
	Riders	Ride	Population	Income	Rate
	(000's)	(cents)	(000's)	($)	(cents)
Year	Y	X1	X2	X3	X4
1993	1200	15	1825	2795	55
1994	1195	15	1815	2995	55
1995	1186	15	1805	3095	65
1996	1101	25	1803	3145	65
1997	1100	25	1775	3170	65
1998	1120	25	1765	3185	75
1999	1120	25	1750	3995	80
2000	1091	30	1750	4195	80
2001	1088	30	1745	4295	80
2002	1078	30	1730	4495	85
2003	1070	30	1735	4710	85
2004	1010	40	1725	5180	85
2005	1003	40	1720	5560	90
2006	1012	40	1720	5695	105
2007	1000	40	1715	5795	110
2008	991	40	1655	5810	110
2009	915	75	1665	6220	110
2010	915	75	1660	6395	115
2011	920	75	1655	6507	130
2012	928	75	1645	6778	135
2013	945	75	1640	6900	155
2014	910	100	1630	7129	160
2015	928	100	1615	7395	170
2016	926	100	1620	7495	180
2017	942	100	1615	7695	180
2018	943	100	1625	7895	195
2019	948	100	1635	7905	195
2020	850	100	1640	8000	200

1. What is the dependent variable in this demand study? What are the independent variables? What are the expected signs of the independent variables? 2. Using Excel (or similar spreadsheet software), estimate the coefficients of the demand model for the data in the table above. Write the estimated equation in the form: Y = a + b1X1 + b2X2 + b3X3 + b4X4 3. Provide an economic interpretation for each of the coefficients in the regression equation you have computed. For example, what happens to the number of riders:

(a) If price per ride increases by 10 cents? (b) If population increases by 1,000? (c) If income increases by $100? (d) If the parking rate increases by 10 cents? 4. Which of the independent variables is/are statistically significant (at the 0.05 level) in explaining the number of STA riders? Explain how you decided which variables were statistically significant at the 0.05 level. 5. Is the overall model statistically significant (at the 0.05 level)? What test did you use to answer this question? Explain how you decided that the model was statistically significant at the 0.05 level. 6. What is the value of the coefficient of determination? What does the coefficient of determination tell you? 7. Using the regression model and and price from 2020, calculate the price elasticity of demand. What does this elasticity coefficient tell you? Based on your understanding of the relationship among price, price elasticity of demand, and total revenue, what does this elasticity coefficient tell you will happen to total revenue if price is increased? 8. Using the regression model and price from 2020, calculate the income elasticity of demand in 2020. What does this elasticity coefficient tell you? 9. If the fare is increased to $1.25 in 2021, what is the expected impact on weekly riders to the transit system if all other variables remain at their 2020 levels. In other words, determine a point estimate of the number of riders after the price change. Compute a 95% prediction interval for this point estimate for your point estimate of the number of weekly STA riders. Using your point estimate of weekly riders, determine predicted annual revenue for 2021 after price is increased to $1.25. How much larger (or smaller) is this annual revenue than the annual revenue generated in 2020 when price was $1.00? Using the lower and upper boundaries of your 2021 point estimate of weekly STA riders, determine the lower and upper boundaries for your 2021 annual revenue prediction. What do these boundaries (particularly the lower boundary) suggest about the likelihood of 2021 annual revenue exceeding 2020 annual revenue. 10. Write a brief concluding statement to the STA manager that verbally describes and summarizes your analysis of the board's recommendation to increase fares to $1.25 and the expected impact on annual revenues if STA increases fares from $1.00 to $1.25. This statement should be written in such a manner that someone who is not familiar with analytical tools of analysis can understand what is expected to happen to STA's annual revenues as a result of the price increase.