Multiple Linear Regression Model The Effect Of Missing Data Imputation Method

Missing data is a popular problem that permeates much of the modern research work and areas of investigation being done today. It will make the analysis much more difficult, cause unrealizable results, and decrease the efficiency of the whole statistical program. Especially in the full observation and not fully observed differences between the systems of the circumstances, the use of conventional statistical methods to incomplete data sets made by the results, is not a substitute for the overall. Traditional techniques for replacing missing values may have serious limitations. Recent developments in computing allow more sophisticated techniques to be used.This paper just introduce the theory of the imputation of missing data. This paper compares the efficacy of four current, and promising methods that can be used to deal with missing data. This efficacy will be judged by examining the percent of bias in estimating parameters. The focus of this paper is on linear multi-regression model. The study involves seven levels of incomplete data (5%, 15%, 25%, 35%, 45%, 55%, and 65% missing completely at random). The four techniques used for comparison are mean substitution (Mean), expectation maximization (EM), regression imputation (Regression), and multiple imputation (MI).