2024, Vol. 9, Issue 6, Part B

In this research, the multi-level linear regression model was dealt with, which is one of the most important models widely used and applied in the analysis of data that are characterized by the fact that the observations take a hierarchical shape, as well as two different methods were applied to estimate the parameters of the model, namely the general least squares method and the Baizian analysis, and a comparison was made between them and which is better in the estimation process, through the Akayke information standard.

Akaike information criterion and Bayesian information criterion It was found that the Bayesian analysis method is the most efficient in the estimation process The Bayesian analysis method is the best way to estimate the parameters of a multi-level model (with two levels) for PMRM-2 data in general for any type of regression model for panel data and for different sample sizes, as the method maintained the virtue of estimation by adopting AIC scales. This means that the Baizian analysis method for estimation can be adopted in the applied aspect when estimating the parameters of a multi-level model (with two levels) for PMRM-2 panel data similar to wheat data in some governorates of Iraq and according to the available time series extending from 2000 to 2021.