Plot the data on air travel delays. Can you see seasonal patterns? Explain. Use Megastat to...

Plot the data on air travel delays. Can you see seasonal patterns? Explain. Use Megastat to calculate estimated seasonal indices and trend. Which months have the most delays? The fewest? Is this logical? Is there a trend in the deseasonalized data?

National Airspace Total System Delays, 2002-2006

Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
2002	14,158	13,821	20,020	24,027	28,533	33,770	32,304	29,056	24,493	25,266	17,712	22,489
2003	16,159	18,260	25,387	17,474	26,544	27,413	32,833	37,066	28,882	21,422	34,116	31,332
2004	28,104	32,274	34,001	32,459	50,800	52,121	46,894	43,770	30,412	37,271	35,234	32,446
2005	32,121	30,176	34,633	25,887	30,920	48,922	58,471	45,328	32,949	34,221	34,273	29,766
2006	29,463	24,705	37,218	35,132	40,669	48,096	47,606	46,547	48,092	51,053	43,482	39,797

In Column Format
Year	Month	Delays
2002	Jan	14158
	Feb	13821
	Mar	20020
	Apr	24027
	May	28533
	Jun	33770
	Jul	32304
	Aug	29056
	Sep	24493
	Oct	25266
	Nov	17712
	Dec	22489
2003	Jan	16159
	Feb	18260
	Mar	25387
	Apr	17474
	May	26544
	Jun	27413
	Jul	32833
	Aug	37066
	Sep	28882
	Oct	21422
	Nov	34116
	Dec	31332
2004	Jan	28104
	Feb	32274
	Mar	34001
	Apr	32459
	May	50800
	Jun	52121
	Jul	46894
	Aug	43770
	Sep	30412
	Oct	37271
	Nov	35234
	Dec	32446
2005	Jan	32121
	Feb	30176
	Mar	34633
	Apr	25887
	May	30920
	Jun	48922
	Jul	58471
	Aug	45328
	Sep	32949
	Oct	34221
	Nov	34273
	Dec	29766
2006	Jan	29463
	Feb	24705
	Mar	37218
	Apr	35132
	May	40669
	Jun	48096
	Jul	47606
	Aug	46547
	Sep	48092
	Oct	51053
	Nov	43482
	Dec	39797

Expert Solution

The plot using Megastat is:

Yes, the seasonal patterns are observed.

The seasonal indexes are:

				Centered
				Moving	Ratio to	Seasonal	Delays
t	Year	Quarter	Delays	Average	CMA	Indexes	Deseasonalized
1	1	1	14,158			0.946	14,961.9
2	1	2	13,821			1.003	13,779.3
3	1	3	20,020	19803.38	1.011	1.073	18,663.8
4	1	4	24,027	24093.88	0.997	0.978	24,566.4
5	2	1	28,533	28123.00	1.015	0.946	30,153.2
6	2	2	33,770	30287.13	1.115	1.003	33,668.2
7	2	3	32,304	30410.75	1.062	1.073	30,115.7
8	2	4	29,056	28842.75	1.007	0.978	29,708.2
9	3	1	24,493	25955.75	0.944	0.946	25,883.8
10	3	2	25,266	23310.88	1.084	1.003	25,189.8
11	3	3	17,712	21448.25	0.826	1.073	16,512.2
12	3	4	22,489	19530.75	1.151	0.978	22,993.8
13	4	1	16,159	19614.38	0.824	0.946	17,076.5
14	4	2	18,260	19946.88	0.915	1.003	18,205.0
15	4	3	25,387	20618.13	1.231	1.073	23,667.3
16	4	4	17,474	23060.38	0.758	0.978	17,866.3
17	5	1	26,544	25135.25	1.056	0.946	28,051.2
18	5	2	27,413	28515.00	0.961	1.003	27,330.4
19	5	3	32,833	31256.25	1.050	1.073	30,608.9
20	5	4	37,066	30799.63	1.203	0.978	37,898.1
21	6	1	28,882	30211.13	0.956	0.946	30,522.0
22	6	2	21,422	29654.75	0.722	1.003	21,357.4
23	6	3	34,116	28840.75	1.183	1.073	31,805.0
24	6	4	31,332	30100.00	1.041	0.978	32,035.3
25	7	1	28,104	31442.13	0.894	0.946	29,699.8
26	7	2	32,274	31568.63	1.022	1.003	32,176.7
27	7	3	34,001	34546.50	0.984	1.073	31,697.7
28	7	4	32,459	39864.38	0.814	0.978	33,187.6
29	8	1	50,800	43956.88	1.156	0.946	53,684.5
30	8	2	52,121	46982.38	1.109	1.003	51,963.9
31	8	3	46,894	45847.75	1.023	1.073	43,717.4
32	8	4	43,770	41443.00	1.056	0.978	44,752.5
33	9	1	30,412	38129.25	0.798	0.946	32,138.9
34	9	2	37,271	35256.25	1.057	1.003	37,158.6
35	9	3	35,234	34054.38	1.035	1.073	32,847.2
36	9	4	32,446	33381.13	0.972	0.978	33,174.3
37	10	1	32,121	32419.13	0.991	0.946	33,944.9
38	10	2	30,176	31524.13	0.957	1.003	30,085.0
39	10	3	34,633	30554.13	1.133	1.073	32,286.9
40	10	4	25,887	32747.25	0.791	0.978	26,468.1
41	11	1	30,920	38070.25	0.812	0.946	32,675.7
42	11	2	48,922	43480.13	1.125	1.003	48,774.5
43	11	3	58,471	46163.88	1.267	1.073	54,510.1
44	11	4	45,328	44579.88	1.017	0.978	46,345.5
45	12	1	32,949	39717.50	0.830	0.946	34,819.9
46	12	2	34,221	34747.50	0.985	1.003	34,117.8
47	12	3	34,273	32366.50	1.059	1.073	31,951.3
48	12	4	29,766	30741.25	0.968	0.978	30,434.2
49	13	1	29,463	29919.88	0.985	0.946	31,136.0
50	13	2	24,705	30958.75	0.798	1.003	24,630.5
51	13	3	37,218	33030.25	1.127	1.073	34,696.8
52	13	4	35,132	37354.88	0.940	0.978	35,920.6
53	14	1	40,669	41577.25	0.978	0.946	42,978.3
54	14	2	48,096	44302.63	1.086	1.003	47,951.0
55	14	3	47,606	46657.38	1.020	1.073	44,381.1
56	14	4	46,547	47954.88	0.971	0.978	47,591.9
57	15	1	48,092	47809.00	1.006	0.946	50,822.8
58	15	2	51,053	46449.75	1.099	1.003	50,899.1
59	15	3	43,482			1.073	40,536.5
60	15	4	39,797			0.978	40,690.4

	Calculation of Seasonal Indexes
	1	2	3	4
1			1.011	0.997
2	1.015	1.115	1.062	1.007
3	0.944	1.084	0.826	1.151
4	0.824	0.915	1.231	0.758
5	1.056	0.961	1.050	1.203
6	0.956	0.722	1.183	1.041
7	0.894	1.022	0.984	0.814
8	1.156	1.109	1.023	1.056
9	0.798	1.057	1.035	0.972
10	0.991	0.957	1.133	0.791
11	0.812	1.125	1.267	1.017
12	0.830	0.985	1.059	0.968
13	0.985	0.798	1.127	0.940
14	0.978	1.086	1.020	0.971
15	1.006	1.099
mean:	0.946	1.003	1.072	0.978	3.998
adjusted:	0.946	1.003	1.073	0.978	4.000

Which months have the most delays?

July 2005, May 2004

The fewest?

February 2002, January 2002

Is this logical?

Yes

Is there a trend in the deseasonalized data?

Yes

orchestra answered 1 year ago

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