Decidable Regression Techniques for Statistical Modelling with Sustainable Agriculture Operations

One of the powerful statistical techniques is the regression analysis that is used for modelling the relationship of a response variable with one or more explanatory variables. In addition, a variety of ways to estimate agricultural production have been developed in order to help farmers, researchers, and policy makers in several practical problems. It is well-known that the regression models have a wide applications in the sector of agriculture, and help decision makers by revealing information regarding crop productivity, resource optimization, and risk management. These models support sustainable and effective agricultural practices by exploring the relationships between different factors and outcomes. In this paper, the modelling and analysis are developed to evaluate the growth pattern based on total cultivated area, agriculture production, temperature and humidity. In this study, several regression procedures are comprehensively analyzed, and the goodness of fit is examined. Both univariate and multivariate regression models are designed for the study purpose. In addition, the univariate regression models and the multivariate regression models are found to fit the data significantly in this investigation. The bootstrap technique is also performed to construct the confidence intervals for the estimates of the regression models to test the validity of the estimates. Both univariate and multivariate regression estimates are found to be significant. Also, the estimators for all univariate and multivariate regression models are bounded within 95% bootstrapping confidence limits.