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# hw3 - Cheng GONG Assignment 3 1 Explore the data When we...

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Cheng GONG Assignment 3 1. Explore the data When we use crosstab to analysis the decision, we first draw a graph (Fig 1.1), which is the percentage of alternatives that people can choose when they made their decision by different range of income. We notice that the number people can have train is nearly equal to car, which means that when people making their travel choice, the train and car shares the same chance to be chosen. The air shares the least percentage. Fig. 1.1 the percentage of choice For those the really number of people’s choice, we can get the Table 1.1. Table 1.1 INCOME 1(TRAI N) 2(AIR) 3(BUS ) 4(CAR ) SUM 5000 8.46% 1.49% 18.41 % 71.64 % 100.00 % 15000 4.76% 0.71% 9.52% 85.00 % 100.00 % 20000- 25000 5.15% 0.76% 6.30% 87.79 % 100.00 % 25001- 30000 6.10% 0.00% 1.22% 92.68 % 100.00 % 30001- 35000 3.75% 0.71% 5.36% 90.18 % 100.00 % 35001- 40000 8.70% 0.00% 0.00% 91.30 % 100.00 % 40001- 45000 1.89% 0.95% 3.55% 93.62 % 100.00 % 50000- 55000 2.62% 1.31% 2.62% 93.44 % 100.00 % 70000 3.25% 1.83% 2.03% 92.90 % 100.00 % SUM 4.09% 1.02% 5.74% 89.15 % 100.00 % Fig 1.2 distribution of actual choice of traveller The distribution of car uses the second y-coordination, which is on the right side. From Fig 1.2 we can easliy get the distribution of actual choice of traveller. For bus, when the annual income is 5000, the people are willing to take bus instead of other alternatives. For train, the distribution is similar with the bus, while it shares less percentage than bus from 5000 to 20000, and than, the distribuion is really the same. Obviously, the most part of alternatives is car. It has almost more than 90% alternatives after 25000 annual income. For air, it increases when income increase. Moreover, we can focus on the gender relationship with the alternatives. GENDER 1(TRAI 2(AIR) 3(BUS 4(CAR SUM

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Cheng GONG N) ) ) 0 2.50% 0.91% 3.41% 93.18% 100.0 0% 1 6.06% 1.17% 8.61% 84.16% 100.0 0% Table 1.2 It is very interesting that for female, they usually choose train, air and bus than male. And we find that in these 3032 cases, our sample includes 1199 female and 1833 male . So even the number of male is larger than female, we found that more male will try car than other alternatives. Maybe it is because the car is more convenience than others, and the men don’t like inconvenience choices. Due to those unrealistic data, in this part we just ignore them. In the rest part, for these 58 data, so we replace 0 and 1 into 0.6 (1833/3032). 2. Estimate a basic reference model The travel time (TT) and travel cost (TC) variables are specified as generic in this model. After running the Elm, we can first get the estimation of a constant only model, which states in Table 2.1. Notice, we set car as a base. Table 2.1Constant only Model Estimates Model Parameter Estimates Parameter InitValue FinalValue StdError t-Stat NullValue [email protected] rain 0 -3.0794 0.091844 -33.529 0 [email protected] Air 0 -4.0295 0.18114 -22.245 0 [email protected] Bus 0 -2.3485 0.079287 -29.62 0 Log Likelihood at Convergence -1266.26034 Log Likelihood at Null Parameters -3582.76946 The Adjusted Rho Squared with respect to zero is 0.653. Then we build a basic reference model that includes only the alternative specific constants, travel time and travel cost. Due to travel time, firstly, we use total travel time.
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