# Economics Essays

1043 Words Oct 29th, 2013 5 Pages
Demand Estimation: Southern Transportation Authority

The following exercise was taken from: McGuigan, J.R., R. Ch. Moyer, and F. H. deB. Harris, Managerial Economics: Applications, Strategy, and Tactics, 9th Edition, South Western. Early in 2011, the Southern Transportation Authority (STA), a public agency responsible for serving the commuter rail transportation needs of a large US Eastern city, was faced with raising operating deficits on its system. Also, because of a fiscal austerity program at both the federal and state levels, the hope of receiving additional 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
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This variable is designated I in Table 1.
5. 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.

The transit manager has decided to perform a multiple regression on the data to determine the impact of the rate increase.

Questions:

1. What is the dependent variable in this demand study? Y= Weekly Riders

2. What are the independent variables?

P= Price per Ride T= Population I= Income H= Parking rate

3. What are the expected signs of the variables thought to affect transit ridership on STA?

P= Negative T= Positive I= Negative H= Positive

4. Using a multiple regression program available on a computer, estimate the coefficients of the demand model for the data given in Table 1. Present the model with all relevant statistics. The regression equation is

Weekly Riders (Y) = 13 - 1.55 Price (P) per Ride (Cents)+ 0.682 Population (T) (x1000) - 0.0462 Income (I)+ 1.91 Parking Rate(H) (cents)

Predictor Coef SE Coef T P Constant 12.6 487.3 0.03 0.980 Price (P) per Ride (Cents) -1.5481 0.5016 -3.09 0.005 Population (T) (x1000) 0.6824 0.2595 2.63 0.015 Income (I) -0.04616 0.01227 -3.76 0.001 Parking Rate (H) (cents) 1.9148 0.3502 5.47 0.000

S = 20.6925 R-Sq = 95.9%

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