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Vol. 11, Special Issue 4 (2022)

Parametric and non-parametric statistical techniques for modelling and monitoring area, production, productivity trends of dry fruits under temperate condition

Author(s):
Dr. Nageena Nazir, Dr. SA Mir and Dr. BS Dhekale
Abstract:
The present investigation is carried out to study the trends in dry fruit area, production and productivity in Jammu and Kashmir for the period 2000-2001 to 2017-2018 based on the parametric and nonparametric regression models. In parametric models different linear models are employed. The statistically most suited parametric models are selected on the basis of highest adjusted R2, significant regression co-efficient and co-efficient of determination (R2). Appropriate model is selected based on the model performance measures such as, Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, assumptions of normality and independence of residuals. Nonparametric estimates of underlying growth functions are computed at each and every time points. Relative growth rates of crop productions are estimated based on the best fitted trend functions. In this study it is found that non parametric/semi parametric regression comes out to be a good fit for trend in dry fruit in comparison to parametric regression. Even semi parametric spline is selected as the best fit model for trend analysis.
Pages: 1737-1742  |  249 Views  66 Downloads
How to cite this article:
Dr. Nageena Nazir, Dr. SA Mir and Dr. BS Dhekale. Parametric and non-parametric statistical techniques for modelling and monitoring area, production, productivity trends of dry fruits under temperate condition. The Pharma Innovation Journal. 2022; 11(4S): 1737-1742.

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