Fitting of the distribution for CV value of the cotton and tobacco experiment

Abstract

Coefficient of variation (CV) was a measure commonly applied to present variation in agricultural experiments. Its merits are well known, most important being one that CV deals with what we could call the scale-invariant variability in the experiments. It was easier to understand than variance it was based upon. Coefficient of variation used to compare the variation of traits in two (or more) populations or, more commonly, the variation of different traits in a population of the study. If the CV was within certain limits one can say that block has homogeneity in the character under study. Conditional on which distribution was to be used for modelling of the CV data of an experiment, which was a common problem in agricultural science. The six distributions viz., Normal, Lognormal, Gamma, Weibull, Exponential and Beta used for the fitting of distribution study. The test statistic Kolmogorov-Smirnov test, Cramer-Von-Mises test, Anderson-Darling test and Chi-Square test for each data set was computed for six probability distributions and used to identify the best-fit distribution. The probability distributions viz., Normal, Lognormal, Gamma, Beta, Weibull, Exponential were identifying to evaluate the best-fit probability distribution for CV value. In addition, the different forms of these distributions also tried and thus the probability distributions applied to find out the best-fit probability distribution and to know the nature and shape of the distribution.