Time series analysis of RTC Great Lakes recruit graduate data

Time series analysis of RTC Great Lakes recruit graduate data

This thesis formulates predictions for Recruit Training Command (RTC) Great Lakes' recruit graduation rates based on two econometric approaches. The Navy's recruit graduation rates exhibit pronounced seasonal and long-term behaviors, which tends to cause logistical problems at RTC. The mod...

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Bibliographic Details
Main Author: Bosque, Edward F.
Other Authors: Euske, Kenneth J.
Language:en_US
Published: Monterey, California. Naval Postgraduate School 2013
Online Access:http://hdl.handle.net/10945/32612
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-326122014-11-27T16:18:26Z Time series analysis of RTC Great Lakes recruit graduate data Bosque, Edward F. Euske, Kenneth J. NA Management This thesis formulates predictions for Recruit Training Command (RTC) Great Lakes' recruit graduation rates based on two econometric approaches. The Navy's recruit graduation rates exhibit pronounced seasonal and long-term behaviors, which tends to cause logistical problems at RTC. The modeling and subsequent forecast of RTC graduation rates is therefore an important management tool which could facilitate future planning for both RTC Great Lakes and the U. S. Navy. First the multiplicative decomposition method is employed to produce a model. As an alternative the autoregressive integrated moving average (ARIMA) process is used to describe the data. In both instances, satisfactory forecasting results are attained. 2013-05-06T18:43:16Z 2013-05-06T18:43:16Z 1998-12 Thesis http://hdl.handle.net/10945/32612 en_US Approved for public release, distribution unlimited Monterey, California. Naval Postgraduate School
collection NDLTD
language en_US
sources NDLTD
description This thesis formulates predictions for Recruit Training Command (RTC) Great Lakes' recruit graduation rates based on two econometric approaches. The Navy's recruit graduation rates exhibit pronounced seasonal and long-term behaviors, which tends to cause logistical problems at RTC. The modeling and subsequent forecast of RTC graduation rates is therefore an important management tool which could facilitate future planning for both RTC Great Lakes and the U. S. Navy. First the multiplicative decomposition method is employed to produce a model. As an alternative the autoregressive integrated moving average (ARIMA) process is used to describe the data. In both instances, satisfactory forecasting results are attained.
author2 Euske, Kenneth J.
author_facet Euske, Kenneth J.
Bosque, Edward F.
author Bosque, Edward F.
spellingShingle Bosque, Edward F.
Time series analysis of RTC Great Lakes recruit graduate data
author_sort Bosque, Edward F.
title Time series analysis of RTC Great Lakes recruit graduate data
title_short Time series analysis of RTC Great Lakes recruit graduate data
title_full Time series analysis of RTC Great Lakes recruit graduate data
title_fullStr Time series analysis of RTC Great Lakes recruit graduate data
title_full_unstemmed Time series analysis of RTC Great Lakes recruit graduate data
title_sort time series analysis of rtc great lakes recruit graduate data
publisher Monterey, California. Naval Postgraduate School
publishDate 2013
url http://hdl.handle.net/10945/32612
work_keys_str_mv AT bosqueedwardf timeseriesanalysisofrtcgreatlakesrecruitgraduatedata
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