# As shown in the following grap

1. As shown in the following graph, the airfare is determinined bya couple of factors. The excel file “Airfare.xlsx” include a coupleof such factors, such as:
• Origin: Airport code for theorigin
• Destination: Airport code for thedestination
• Average Fare: Average non-stop fare for theroute
• # of Airlines: Number of Airlines providingdirect service between O & D
• Distance: Distance between O &D
• South_West: Whether Southwest provides aDirect Service
• Holiday_O: Whether Origin is a holidaymarket
• Holiday_D: Whether Destination is holidaymarket
• Traffic_O: Annual airport traffic at Origin ècity size
• Traffic_D: Annual airport traffic atDestination è city size

• Please estimate the airfare model with the following steps
• Step 1: Generate “DumHO” using Holiday_O
• Step 2: Generate “DumHD” using Holiday_D
• Step 3: Generate “DumSW” using SouthWest
• Step 4: Perform a regression analysis

Airfare= a + b1* (# of Airlines) + b2* Distance

+b3* Traffic_O + b4* Traffic_D

+b5* DumHO + b6* DumHD

+b6* DumSW + ε

• Answer the following questions in Excel (you can find them inthe worksheet “questions”):
• Q1: One more airline in the market will (decrease/increase)average fare by \$().
• Q2: Presence of South West in the market will(decrease/increase) average fare by\$(                    ).
• Q3: One mile longer in distance will (decrease/increase)average fare by\$(                        ).
• Q4: If destination is holiday market, average fare will(decrease/increase) by \$().
 Origin Destination Average Fare # of Airlines Distance Traffic_O Traffic_D Holiday_O Holiday_D South_West DFW MFE 268 1 468 16.98 12.50 Yes No No HOU HRL 117 1 276 15.10 12.89 No No Yes BNA PHL 247 2 675 15.18 16.19 No No Yes DFW PHL 281 3 1302 16.98 16.19 Yes No No MCO PHL 158 3 861 16.38 16.19 Yes No Yes MHT PHL 151 3 290 14.41 16.19 No No Yes PBI PHL 148 3 951 14.89 16.19 Yes No No PHX PHL 224 4 2075 16.65 16.19 Yes No Yes PIT PHL 133 3 267 15.57 16.19 No No Yes

o/p\ from excel for regression analysis

 SUMMARY OUTPUT Regression Statistics Multiple R 0.971852 RSquare 0.944496 Adjusted R Square 0.555965 Standard Error 41.89654 Observations 9
 ANOVA df SS MS F Significance F Regression 7 29869.57 4267.081 2.430941 0.458299 Residual 1 1755.32 1755.32 Total 8 31624.89
 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -867.018 495.238 -1.751 0.330 -7159.609 5425.573 -7159.609 5425.573 # ofAirlines -57.701 41.355 -1.395 0.396 -583.161 467.760 -583.161 467.760 Distance 0.074 0.046 1.606 0.355 -0.514 0.663 -0.514 0.663 Traffic_O 48.375 24.084 2.009 0.294 -257.645 354.395 -257.645 354.395 Traffic_D 30.350 22.945 1.323 0.412 -261.197 321.896 -261.197 321.896 Holiday_O -75.249 66.317 -1.135 0.460 -917.892 767.394 -917.892 767.394 Holiday_D 0 0 65535 0 0 0 0 0 South_West -64.3027 47.08193 -1.36576 0 -662.535 533.9299 -662.535 533.9299

airfare = -867.012 + (-57.7008)airlines + 0.074distance +48.375traffic_o + 30.350traffic_D + (-75.249)holiday_o +(0)holiday_D + (-64.3027)south_west

a)

one more airline will decrease the averageairfare by \$(57.7008)

b)

Presence of South West in the market will(decrease/ average fare by \$(64.3027)

c)

One mile longer in distance will(increase) average fare by \$(0.074 )

d)

the intercept for holiday market is 0 , so no effect onairfare by market holiday

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