Using A Semi-Parametric Regression Model to Estimate A Function of The Quantity of Date Production In Iraq

Abstract

The semi-parametric referral model is one of the important developments that used the analysis of their independent effect on other variables, which produces prediction issues. It is known that the semi-parametric referral model combines direct referral models, whose variables are linear, with analmic conversion models, whose variables are non-linear. In this, it was done. The research presents the production function of the quantity of dates, which is affected by the multiplicities. Some of them control parameters such as heart rate and settings of fruiting palm trees, and some of them behave nonlinearly, such as humidity, temperature, wind, and others, and we take the variable to determine the air temperature. It has been observed that the mean square error of the semi-parametric regression model is less than the mean square error of the parametric regression model, which assumes that all variables behave linearly. This proves the validity of the results in the case of using sample sizes for a set of data generated through simulation experiments, which showed that the regression models are semi-parametric.