Statistical notes for clinical researchers: simple linear regression 3 – residual analysis
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초록
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.
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.
참고문헌 (2)
- Hae-Young Kim, "Statistical notes for clinical researchers: simple linear regression 1 – basic concepts", Restorative Dentistry and Endodontics, 43(2), pp.1~5 (2018), 대한치과보존학회
- Daniel WW, "Biostatistics: basic concepts and methodology for the health science", pp.410~412 (2010), John Wiley & Sons