14. How many observations are used in this regression? Is this different from th
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Question
14. How many observations are used in this regression? Is this different from the number of observations used in Exhibit D? If yes, why do you think this is the case?
15. Ceteris paribus, on average, if a route has one more direct rivals, what is the expected difference in business class fares? Compare this number to the expected difference in economy class fares under the same situation. [Hint: using just Exhibit E may not be enough to answer this question. Look elsewhere, utilize what Carly inspires you.]
16. Obviously, the authors of this paper has done a lot more regressions and analyses. What are some factors, in your opinion, that may also affect prices of tickets? Please list three of those. For each one, illustrate how that variable may affect prices, and give some ideas on the possible sign for that particular coefficient.
hibit D 11,064 172.24 0.000 0.0153 0.0152 101.32 Source df MS Number of obs - F(1, 11062) Model Residual 1768300.97 113569163 1 1768300.97Prob> F 11,062 10266.603 R-squared Adj R-squared- Total 115337464 11,063 10425.5142 Root MSE coach fare Coef. Std. Err. [95% Conf. Interval] 22.73175 266.2998 16.82378 272.5008 num car dir 19.777761.506997 13.120. 000 cons 269.4003 1.581759 170.32 0.000Explanation / Answer
12)
A) dependent variable = coach_fare
B) independent variable = num_car_dir = the number of direct rivals, or competitors
13)
slope = -19.7776
yes, it makes sense
as the number of direct rivals, or competitors increases the coach fare should decrease
hence slope is negative
14)
degree of freedom total = n-1
where n is sample size
here
n -1 = 3143
n = 3144
hence number of observations are used in this regression = 3144
in Exhibit D
n-1 = 11063
n = 11064
this is different from the number of observations used in Exhibit D
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