Best Fit Probability Distribution Analysis of Major Crop Paddy of Rice Bowl State of India-Telangana
##plugins.themes.bootstrap3.article.main##
Telangana state's population is mostly dependent on agriculture. The Telangana state's economy depends heavily on agriculture, as does the nation's and the state's ability to achieve food security. Combining art and science to fit a statistical distribution to data involves making trade-offs along the way. The secret to effective data analysis is striking a balance between improving distributional fit and preserving ease of estimation while keeping in mind that the analysis's ultimate goal is to help you make better decisions. A recurring issue in agricultural research was which distribution should be utilized to simulate the production data from an experiment.
An analysis is then carried out utilizing the obtained distributions using the statistical method to fit probability distributions to data of variables. These distributions would be a representation of the properties of the variable data. The twenty distributions are: Cauchy, Error, Hypersequent, Gamma(3p), Laplace, Logistic, Log Pearson 3, Rayleigh (2p), Weibull (3p), Log Logistic (3p), Triangular, Gen Gamma, Gen.Gamma(4p), Gen.Extreme Value, Log Normal (3p), Pearson 5 (3p), Fatigue life (3p), Inv. Gaussian (3p), Nakagami, In order to suit a distribution research, rice distributions are used. Twenty probability distributions were computed, and the test statistic Kolmogorov-Smirnov test, Anderson-Darling test, Chi-Square test, and each data set were used to choose the distribution that fit the data the best. The probability distributions include Cauchy, Error, Hypersequent, Gamma, Laplace, Logistic, Log Pearson 3, Rayleigh (2p), Weibull (3p), Log Logistic (3p), Triangular, Gen. Gamma, Gen. Gamma (4p), Gen. Extreme Value, Log Normal (3p), Log Pearson 5, Fatigue life (3p), Inv. Gaussian (3p), and Nakagami.
References
-
Dikko HG, David IJ, Bakari HR. Modeling the distribution of rainfall intensity using quarterly data. IOSR Journal of Mathematics. 2013; 9(1): 11-16.
Google Scholar
1
-
Jamaludin S, Jemain AA. Fitting the statistical distributions to the daily rainfall amount in peninsular Malaysia. Journal Technology. 2007: 33â-48.
Google Scholar
2
-
Ghosh S, Roy MK, Biswas SC. Determination of the best fit probability distribution for monthly rainfall data in Bangladesh. American Journal of Mathematics and Statistics. 2016; 6(4): 170-174.
Google Scholar
3
-
Gupta SC, Kapoor VK. Fundamentals of mathematical statistics. 11th ed. Sultan Chand & Sons; 2002.
Google Scholar
4
-
Kumar V, Bala S. Best fit probability distribution analysis of precipitation and potential evapotranspiration of India’s highly dense population state-Bihar. MAUSAM. 2022; 73(1): 139-150.
Google Scholar
5
-
Chaudhari RH, Khokhar AN, Paramr DJ, Patel HV, Kumar P, Kumar R. Fitting of the distribution for CV value of the cotton and tobacco experiment. Journal of Pharmacognosy and Phytochemistry. 2020; (5S): 884-890.
Google Scholar
6
-
Amin MT, Rizwan M, Alazba AA. A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan. Open Life Sciences.2016; 11(1): 432-440.
Google Scholar
7
-
Beckman RJ, Tiet jen GL. Maximum likelihood estimation for the beta distribution. Journal of Statistical Computation and Simulation. 1978; 7(3-4): 253-258.
Google Scholar
8
-
Smirnov N. Table for estimating the goodness of fit of empirical distributions. The Annals of Mathematical Statistics. 1948; 19(2): 279-281.
Google Scholar
9
-
Alam MA, Emura K, Farnham C, Yuan J. Best-fit probability distributions and return periods for maximum monthly rainfall in Bangladesh. Climate. 2018; 6(1): 9.
Google Scholar
10
-
Darling DA. The kolmogorov-smirnov, cramer-von mises tests. The Annals of Mathematical Statistics. 1957; 28(4): 823-838.
Google Scholar
11
-
Anderson TW, Darling DA. A test of goodness of fit. Journal of the American statistical Association. 1954; 49(268): 765-769.
Google Scholar
12
Most read articles by the same author(s)
-
Deepu Dileep,
Soumya Rudraraju,
V. V. HaraGopal,
Topic Modelling on Pharmaceutical Incident Data , European Journal of Mathematics and Statistics: Vol. 2 No. 3 (2021) -
R. Nellutla,
R. Ashok,
M. Ramesh,
V. V. Haragopal,
Technical Efficiency of Universities in Telangana State through Data Envelopment Analysis (DEA) Approach , European Journal of Mathematics and Statistics: Vol. 2 No. 6 (2021) -
Sampath Kumar,
V. V. HaraGopal,
Higher Order Moments of the Order Statistics for the Rectangular, Exponential, Gamma and Weibull Distributions , European Journal of Mathematics and Statistics: Vol. 2 No. 3 (2021)