efer to Exercise 12.12. In the plot of the data, airport 20 had a much larger revenue than any of the other 21 airports. Replot the three scatterplots with the data from airport 20 deleted. Does there appear to be any relationship among revenue and the two explanatory variables in this data set? Fit a first-order regression model relating revenue to distance and population size. Comment on the quality of the fit of the model to the data. Is revenue related to distance from hub and population size once airport 20 is deleted from the data? What conclusions can be inferred from parts (a) and (b) about the importance of plotting the data and not just running models through a software program? 12.12 A regional airline transfers passengers from small airports to a larger regional hub airport. The airline’s data analyst was assigned to estimate the revenue (in thousands of dollars) generated by each of the 22 small airports based on two variables: the distance from each airport (in miles) to the hub and the population (in hundreds) of the cities in which each of the 22 airports is located. The data is given in the following table.
efer to Exercise 12.12. In the plot of the data, airport 20 had a much larger revenue than any of the other 21 airports. Replot the three scatterplots with the data from airport 20 deleted. Does there appear to be any relationship among revenue and the two explanatory variables in this data set? Fit a first-order regression model relating revenue to distance and population size. Comment on the quality of the fit of the model to the data. Is revenue related to distance from hub and population size once airport 20 is deleted from the data? What conclusions can be inferred from parts (a) and (b) about the importance of plotting the data and not just running models through a software program? 12.12 A regional airline transfers passengers from small airports to a larger regional hub airport. The airline’s data analyst was assigned to estimate the revenue (in thousands of dollars) generated by each of the 22 small airports based on two variables: the distance from each airport (in miles) to the hub and the population (in hundreds) of the cities in which each of the 22 airports is located. The data is given in the following table.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Refer to Exercise 12.12. In the plot of the data, airport 20 had a much larger revenue than any of the other 21 airports.
- Replot the three scatterplots with the data from airport 20 deleted. Does there appear to be any relationship among revenue and the two explanatory variables in this data set?
- Fit a first-order regression model relating revenue to distance and population size. Comment on the quality of the fit of the model to the data. Is revenue related to distance from hub and population size once airport 20 is deleted from the data?
- What conclusions can be inferred from parts (a) and (b) about the importance of plotting the data and not just running models through a software program?
12.12 A regional airline transfers passengers from small airports to a larger regional hub airport. The airline’s data analyst was assigned to estimate the revenue (in thousands of dollars) generated by each of the 22 small airports based on two variables: the distance from each airport (in miles) to the hub and the population (in hundreds) of the cities in which each of the 22 airports is located. The data is given in the following table.
Transcribed Image Text:Airport Revenue
1
2
4
5
6
7
8
9
10
11
233
272
253
296
268
296
276
235
253
233
240
Distance Population Airport Revenue
233
56
12
209
174
13
206
67
14
232
125
245
213
134
140
165
234
78
73
54
100
98
95
81
52
15
16
17
18
19
20
21
22
267
338
243
252
269
242
233
234
450
340
200
Distance Population
96
96
73
205
214
183
230
238
144
220
170
170
290
340
55
91
64
60
60
240
70
75
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