Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India
International Journal of Statistical Distributions and Applications
Volume 4, Issue 1, March 2018, Pages: 1-5
Received: Sep. 11, 2017;
Accepted: Nov. 11, 2017;
Published: Jan. 19, 2018
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M. Sundar Rajan, Department of Statistics, Govt. Arts College (Affiliated Bharathiar University), Coimbatore, India
M. Palanivel, Department of Statistics, Govt. Arts College (Affiliated Bharathiar University), Coimbatore, India
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Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.
Regression Growth Models, Area, Production, Productivity, Cotton, Adjusted R2, Growth Models
To cite this article
M. Sundar Rajan,
Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India, International Journal of Statistical Distributions and Applications.
Vol. 4, No. 1,
2018, pp. 1-5.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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