Multiple Imputations, tool for the estimation of missing data in regression modeling

Abstract

In recent years there has been an increase in research on missing data problems, with multiple imputation being a fundamental alternative; where data sets often present complexities that are currently difficult to manage appropriately in the probability framework, but relatively simple to deal with imputation; For this reason, this article describes a series of practical aspects to apply this methodology in the case of carbon capture modeling for Colombia, based on the World Bank databases including missing data reaching R2 of 79.2988%, highlighting that when estimating said data and recalculating the respective model, a greater R2 is evidenced, being of 94.76901%, which evidences a substantial improvement of the respective multiple linear regression model as such.

Keywords

• nalysis of variance
• coefficient of determination
• equation
• sum of squares
• validation