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dc.contributor.advisorKettleborough, C. F.
dc.contributor.advisorKshirsagar, A. M.
dc.creatorTommerup, Jocelyn Anthea
dc.description.abstractTraditionally, statistical control has been utilised in attempting to detect variation in quality of process-output for which it was assumed statistically independent observations were obtained over time. In practice, this convenient assumption is rarely valid (for real life phenomena and forecasting and control technology is required for dependent time series data. Although the literature abounds with references to control systems for independent measurements the only reference to control of dependent data emphasized the total inadequacy of traditional control limits and the need for more realistic values, but offered no alternative methodology. To partially meet this need for control techniques for dependent observations, several models assuming some degree of dependency have been proposed and illustrated with practical applications. The models chosen represent observations generated by moving averages, the linear autoregressive mechanism, a two -state Markov chain and the number of renewals for selected ordinary and Markov renewal processes.en
dc.format.extent125 leavesen
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectIndustrial Engineeringen
dc.subject.classification1974 Dissertation T661
dc.titleControl limits for dependent observationsen
dc.typeThesisen Engineeringen A&M Universityen of Philosophyen
dc.contributor.committeeMemberBurgess, A. R.
dc.contributor.committeeMemberKrouskop, Thomas A.
dc.contributor.committeeMemberMatis, J. H.
dc.contributor.committeeMemberSpencer, Don L.
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries

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