Featuring Engineering in Python: Variable distribution

Linear regression is a common technique used in the association study between the targeted outcome and some potential risk factors (e.g., age, sex). The violation of the normality assumption sometimes may be attributed by the skewed nature of the dependent variable and may be a concern for naturally skewed outcome variables, such as best corrected visual acuity, 1 refractive error, 2 and Rasch score. Normality violation will affect the estimates of the standard error (SE) and the confidence interval, and hence the significance of the risk factors. Nonparametric regression model or bootstrap techniques are suggested to be performed as they provide more robust estimates of SE. However, nonparametric techniques require large sample sizes to supply; the model structure, and are very sensitive to the outliers. Thus, a key question is whether simple linear regression modeling still is valid if the "normality assumption" is violated.