Application of Burrxii-Dal and Weibull-Dal Distribution: A Case Study of Wheat Yield in Multan Pakistan
Background Statistical analysis is highly dependent on the Probability distribution of the data and new models are most important part for the expansion of this field. Burr-XII DAL (BDAL) and Weibull-DAL (WDAL) distribution with five parameters are used to analysis the distribution and to predict th...
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Main Authors: | , , |
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Format: | Article |
Language: | English |
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Res Militaris
2023
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Online Access: | https://repo.uum.edu.my/id/eprint/30934/1/EJMS%2013%2002%202023%203906-3914.pdf https://repo.uum.edu.my/id/eprint/30934/ https://resmilitaris.net/issue-content/application-of-burrxii-dal-and-weibull-dal-distribution-a-case-study-of-wheat-yield-in-multan-pakistan-1809 |
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Summary: | Background Statistical analysis is highly dependent on the Probability distribution of the data and new models are most important part for the expansion of this field. Burr-XII DAL (BDAL) and Weibull-DAL (WDAL) distribution with five parameters are used to analysis the distribution and to predict the yield and growth rate of different products. Objective The purpose of the study is to determine the effectiveness of the application of modified BURRXII-DAL and WEIBULL-DAL distribution for the analysis of the wheat yield in Multan, Pakistan by estimating their maximum likelihood.
Methodology The study involves the comparative analysis of Burr-XII DAL (BDAL) and Weibull-DAL (WDAL) distribution with other distribution models including Beta exponentiated Nadarajah Haghighi (BENH), Kumaraswamy Nadarajah Haghighi, exponentiated generalized Nadarajah Haghighi and modified Nadarajah Haghighi (MNH). The properties of Burr-XII DAL (BDAL) and Weibull-DAL (WDAL) analyzed through the R package and software to determine MLE, standard errors and W∗, A∗, K-S, p-value, AIC, BIC and l ̂along with Akaike information criteria (AIC) and Bayesian information criteria (BIC). Conclusion The study concluded that modified propose Burr-XII DAL (BDAL) and Weibull-DAL (WDAL) are effective alternative models for the analysis of the distribution due to their high reliability and maximum likelihood value for the data make it reliable for the analysis of the real-life data |
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