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Offline data analysis of non-standardized raw data sample using regressive techniques

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dc.contributor.authorSiti Sarah Anuar
dc.date.accessioned2024-04-01T07:52:54Z
dc.date.available2024-04-01T07:52:54Z
dc.date.issued2011
dc.descriptionTA340.S57 2011
dc.description.abstractThis thesis presents the development of software using MATLAB that can be used to analyze the raw data sample using regressive techniques. The data sample varies from a specific data collection to randomly generated data. Regression analysis is a statistical tool for the investigation of relationships between variables. The two basic types of regression are linear regression and multiple regressions. Linear regression uses one independent variable to explain and/or predict the outcome of Y, while multiple regressions use two or more independent variables to predict the outcome. Regression technique is used to improve captured data analysis and to predict the required information. The main techniques for regression include autoregressive, moving average and autoregressive moving average. In this project, autoregressive modelling is used to forecast or predict the past and future value using forward or backward linear prediction.
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/31778
dc.language.isoen_US
dc.subjectEngineering
dc.titleOffline data analysis of non-standardized raw data sample using regressive techniques
dc.typeResource Types::text::Final Year Project
dspace.entity.typePublication
oaire.citation.endPage50
oaire.citation.startPage1
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
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