Using SIMCA statistical software package to apply orthogonal projections to latent structures modeling
dc.contributor.author | Sadeghi-Bazargani, H | |
dc.contributor.author | Banani, A | |
dc.contributor.author | Mohammadi, S | |
dc.date.accessioned | 2018-08-26T09:44:28Z | |
dc.date.available | 2018-08-26T09:44:28Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/58671 | |
dc.description.abstract | Soft independent modeling of class analogy(SIMCA) has been the standard statistical modeling Umetrics has released SIMCA-P+ Version 12, which is a multivariate data analysis (MVDA)software that performs cluster analysis and partial least squares (PLS) regression with classification trees as well as recently developed modeling technique as well as orthogonal projections to latent structures. In this paper we will discuss briefly the general capabilities of the SIMCA software package and will get focused on PLS and OPLS statistical modeling techniques. | |
dc.language.iso | English | |
dc.relation.ispartof | 2010 World Automation Congress, WAC 2010 | |
dc.relation.ispartof | 2010 World Automation Congress, WAC 2010 | |
dc.subject | Megavariate analysis | |
dc.subject | Multi variate analysis | |
dc.subject | Orthogonal projection | |
dc.subject | Partial least square regression | |
dc.subject | SIMCA-P+12 | |
dc.subject | Statistical software packages | |
dc.subject | Cluster analysis | |
dc.subject | Packaging | |
dc.subject | Principal component analysis | |
dc.subject | Regression analysis | |
dc.subject | Software packages | |
dc.subject | Statistics | |
dc.subject | Trees (mathematics) | |
dc.subject | Multivariant analysis | |
dc.subject | Computer Programs | |
dc.subject | Data Processing | |
dc.subject | Mathematical Models | |
dc.subject | Statistical Analysis | |
dc.title | Using SIMCA statistical software package to apply orthogonal projections to latent structures modeling | |
dc.type | Review | |
dc.citation.epage | ||
dc.citation.index | Scopus | |
dc.citation.URL | https://ieeexplore.ieee.org/document/5665603/metrics#metrics |
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