Abstract
Yield trials play an important role for screening the competing cultivars. Often researchers normally measure several
characteristics, commonly called as “parameters”, related to yield for each cultivar. Usually, researchers apply univariate
statistical approaches even for multi-variable data; which limits the scope of the research implication. However, there is least
tendency for considering multivariate techniques to analyze the multi-variable data. If results turn out weird than what are
normally expected; then this leads the researcher to a dilemma. One of the possible reasons for such unexpected results is the
presence of outlier(s) in the data; which is often not obvious particularly in case of multivariate data. The focus of the present
study is to provide the researchers with a comparative study of classical and robust techniques for the detection of outlier(s)
where the data is recorded on multi variables. The statistical software R is used for the analysis. Results show that robust
technique is more appropriate for detecting outlier(s) than the classical ones.
Muhammad Zafar Iqbal, Samra Habib, Muhammad Imran Khan, Muhammad Kashif. (2020) COMPARISON OF DIFFERENT TECHNIQUES FOR DETECTION OF OUTLIERS IN CASE OF MULTIVARIATE DATA, Pakistan Journal of Agricultural Sciences, Volume 57, Issue 3.
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