Open AccessEstimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh
Author(s) -
Rashidul Hasan
Publication year - 2021
Publication title -
journal of mathematics and statistics studies
Language(s) - English
Resource type - Journals
ISSN - 2709-4200
DOI - 10.32996/jmss.2021.2.2.7
Subject(s) - weibull distribution , statistics , goodness of fit , mathematics , anderson–darling test , probability distribution , kolmogorov–smirnov test , gamma distribution , rayleigh distribution , exponential distribution , probability density function , exponential function , statistical hypothesis testing , mathematical analysis
The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.