Statistical notes for clinical researchers: simple linear regression 3 – residual analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hae-Young Kim | - |
dc.date.accessioned | 2021-09-02T01:05:01Z | - |
dc.date.available | 2021-09-02T01:05:01Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2234-7658 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/70594 | - |
dc.description.abstract | In the previous sections, simple linear regression (SLR) 1 and 2, we developed a SLR model and evaluated its predictability. To obtain the best fitted line the intercept and slope were calculated by using the least square method. Predictability of the model was assessed by the proportion of the explained variability among the total variation of the response variable. In this session, we will discuss four basic assumptions of regression models for justification of the estimated regression model and residual analysis to check them. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 대한치과보존학회 | - |
dc.title | Statistical notes for clinical researchers: simple linear regression 3 – residual analysis | - |
dc.title.alternative | Statistical notes for clinical researchers: simple linear regression 3 – residual analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hae-Young Kim | - |
dc.identifier.doi | 10.5395/rde.2019.44.e11 | - |
dc.identifier.bibliographicCitation | Restorative Dentistry and Endodontics, v.44, no.1, pp.1 - 8 | - |
dc.relation.isPartOf | Restorative Dentistry and Endodontics | - |
dc.citation.title | Restorative Dentistry and Endodontics | - |
dc.citation.volume | 44 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 8 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002437904 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
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