Seasonal time series model comparison for nonstationary passenger flight data

Moore, Theresa

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dc.contributor.author	Moore, Theresa
dc.contributor.other	Youngstown State University. Department of Mathematics.
dc.date.accessioned	2021-10-15T15:47:24Z
dc.date.available	2021-10-15T15:47:24Z
dc.date.issued	2007
dc.identifier.other	B20249275
dc.identifier.other	223966353
dc.identifier.uri	https://jupiter.ysu.edu:443/record=b2024927
dc.identifier.uri	http://hdl.handle.net/1989/16635
dc.description	xi, 188 leaves : ill. ; 29 cm. Thesis (M.S.)--Youngstown State University, 2007. Includes bibliographical references (leaves 171).	en_US
dc.description.abstract	The objective of this paper is to analyze the number of passengers flying a sample of three airlines before and after 9/11 to discover whether there has been a recovery. The three airlines were modeled using simple linear regression and time series analysis. Dummy variables and trigonometric functions were used to mimic the seasonal variation and additive decomposition was used to remove the seasonal component and model the trend. The additive decomposition quadratic models were deemed the best fits. From the quadratic models is concluded that the three airlines chosen for this paper have recovered from the effects of 9/11.	en_US
dc.description.sponsorship	Youngstown State University. Department of Mathematics.	en_US
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	Master's Theses;no. 0976
dc.subject	Air travel -- United States.	en_US
dc.subject	September 11 Terrorist Attacks, 2001.	en_US
dc.title	Seasonal time series model comparison for nonstationary passenger flight data	en_US
dc.type	Thesis	en_US

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