An alternate statistical method for dealing outliers in perennial crop experiment
DOI:
https://doi.org/10.24154/jhs.v18i2.2172Abstract
A statistical method based on Robust ANOVA to handle outliers induced high coefficient of variation (CV) in pooled (2011-2018) analysis of long-term Mango cv. Totapuri rootstock trail was suggested. Based on the results, it was concluded that the rootstock treatment T3: Olour (average yield over the period 2011 to 2018 as 57.21 kg/tree) as the best. Precision gained as estimated by reduction in CV (%) was in the range of 11.01 % to 78.9 %. SAS IML codes were built-in for analysis. Hence, this study calls for employing robust ANOVA approach in testing the significance of evaluated treatments in a designed perennial crop experiment with high CV that would have reduced the sensitivity of testing the significance of treatment differences otherwise.
References
Gomez, K.A., Gomez, K.A., & Gomez, A.A. (1984). Statistical procedures for agricultural research. John Wiley & Sons.
Huber, P.J. (1973). Robust regression: Asymptotic, conjectures, and Monte carlo. Annals of Statstics, 1, 799-821.
Federer, W.T. (1955). Experimental design: theory and application. Macmillan, New York.
Paul, R.K., & Bhar, L.M. (2011). M-estimation in block design. Journal of Indian Society of Agricultural Statistics, 65(3), 323-330.
SAS V 9.3 2012. Statistical analysis system version 9.3 SAS Institute, Cary NC.
Venugopalan, R., & Manjunath, B.L. (2019). Appli-cation of Robust ANOVA methods in Papaya having outlier data. Journal of the Indian Society of Agricultural Statistics, 73(2), 129-132.
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Copyright (c) 2023 Venugopalan R, Reju M Kurian, Chaithra M, Sisira P
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