Statistical Modelling of the Agriculture Variables using Various Regression Techniques

Abstract

A response variable's association with one or more explanatory variables examined and modelled using the strong statistical technique, regression analysis. Regression models used in a variety of ways to estimate agricultural production and area, to assist farmers, academics, and policymakers in making well-informed choices. By revealing information about crop productivity, resource optimization, and risk management, regression models in agriculture help decision-makers. These models support sustainable and effective agricultural operations by exploring connections between different elements and results. The analysis conducted to assess the growth pattern in terms of total cultivated area, production, temperature and humidity. In this study, numerous regression procedures were thoroughly analysed, and the goodness of fit examined. Simple and multiple regression models designed for the study purpose. All simple regression models in this investigation found to match the data satisfactorily, likewise all multiple regression models were in line with the data well. Bootstrap technique also performed to build confidence intervals for regression models’ estimations in order to check the validity of the estimates. All simple and multiple regression estimates found to be valid. All simple and multiple regression models’ estimates found bounded within the 95% bootstrapping confidence limits.