MULTIVARIATE ANALYSIS: DETECTION OF MULTIPLE OUTLIERS MISSING DATA

Abstract

For many years, statisticians have been interested in locating "outlying," "unusual," or "unrepresentative" observations as a prelude to data analysis. Data that has been entered improperly or that does not belong to the population from which the rest of the data was collected may cause estimates to be skewed and findings to be misleading. In a number of circumstances, methods have been developed to detect and/or accommodate outlier findings. Scientists are gathering huge data sets thanks to recent technological advancements, and analysts are delving deeper to uncover the secrets of data. As a result, having a solid technique in place for dealing with rogue findings that may go unnoticed in a normal data analysis is critical