Norrulashikin, Siti Mariam and Yusof, Fadhilah and Mohd. Nor, Siti Rohani and Kamisan, Nur Arina Bazilah (2021) Best fitted distribution for meteorological data in Kuala Krai. Journal of Statistical Modeling and Analytics (JOSMA), 3 (1). pp. 1625. ISSN 21803102

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Official URL: http://dx.doi.org/10.22452/josma.vol3no1.2
Abstract
Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodnessoffit tests such as the KolmogorovSmirnov, and AndersonDarling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as QQ plot are presented.
Item Type:  Article 

Uncontrolled Keywords:  burr, meteorology, goodnessoffit, maximum likelihood estimation, probability distribution 
Subjects:  Q Science > QA Mathematics 
Divisions:  Science 
ID Code:  87082 
Deposited By:  Yanti Mohd Shah 
Deposited On:  28 Sep 2022 07:38 
Last Modified:  28 Sep 2022 07:38 
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