When variances are diverse and some functions of means, data transformation is the most suitable corrective action. This method involves converting the original data to a new scale, producing a new data set that is anticipated to fulfil the homogeneity of variances. The comparative values between treatments are not changed, and comparisons between them are still valid, because a same transformation scale is applied to all data. The remedy for heterogeneity in trials, when certain treatments have mistakes that are noticeably larger (lower) than others because of the nature of the treatments investigated, is called error partitioning. We covered the most popular data transformation methods in this chapter along with instances from the real world.
Author(s) Details:
Bhim Singh,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.
Amar Singh,
Department of Agricultural Statistics, CSSS PG College (Affiliated to CCS University, Meerut, U.P.), Machhra, Meerut, (U.P.), India.
Prerna Sharma,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.
Please see the link here: https://stm.bookpi.org/CTAS-V8/article/view/7256
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