This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System TAPFSS).
MI is an interesting approach because it uses randomized techniques to do its imputation an example of it is regression imputation, which regresses the response variable based on the explanatory variable.
Missing data will be imputed using regression imputation.
Missing data on follow-up measurements will be imputed using regression imputation.
Missing data at follow-up will be imputed using regression imputation.
Therefore, missing data at follow up will be imputed using regression imputation.
These missing values are then imputed using regression imputation on all other variables (inclusive their "filled-in" values).
We performed multiple imputations using an interval regression imputation model (100 imputed datasets) with interval censoring of the indicators of pubertal events.
Missing answers that did not exceed these criteria, were imputed using regression imputation within the relevant domain, i.e., the domain's mean was calculated and entered for missing values.
Regression imputation was used to impute values.
Missing data were imputed using stochastic regression imputation (Enders 2010, pp. 46 49).
