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Statistical notes for clinical researchers: simple linear regression 3 - residual analysis

  • Kim, Hae-Young (Department of Health Policy and Management, College of Health Science, and Department of Public Health Science, Graduate School, Korea University)
  • Published : 2019.02.28

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.

Keywords

References

  1. Kim HY. Statistical notes for clinical researchers: simple linear regression 1 - basic concepts. Restor Dent Endod 2018;43:e21. https://doi.org/10.5395/rde.2018.43.e21
  2. Daniel WW. Biostatistics: basic concepts and methodology for the health science. 9th ed. Danvers, MA: John Wiley & Sons; 2010. p410-412.