Statistical notes for clinical researchers: simple linear regression 3 – residual analysisStatistical notes for clinical researchers: simple linear regression 3 – residual analysis
- Other Titles
- Statistical notes for clinical researchers: simple linear regression 3 – residual analysis
- Authors
- Hae-Young Kim
- Issue Date
- 2019
- Publisher
- 대한치과보존학회
- Citation
- Restorative Dentistry and Endodontics, v.44, no.1, pp.1 - 8
- Indexed
- KCI
- Journal Title
- Restorative Dentistry and Endodontics
- Volume
- 44
- Number
- 1
- Start Page
- 1
- End Page
- 8
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/70594
- DOI
- 10.5395/rde.2019.44.e11
- ISSN
- 2234-7658
- 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.
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Collections - College of Health Sciences > Division of Health Policy and Management > 1. Journal Articles
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