The College of Education for Pure Sciences, Department of Mathematics, organized a scientific lecture on
(Statistical inference in the variance components of repeated measures models)
The lecture presented by the researcher (Ammar Abdel Amir Hamza) included the repeated measurements model, one of the most widely used models in the field of experimental design, especially in agricultural, biological, medical and environmental research that fall into this field.
This thesis deals with the study of statistical inference in the variance components of repeated measurement models. Three aspects of the work are considered. In the first aspect, we consider the repeated measures model, which has only one factor within units, one factor between units and an interaction factor and includes three univariate random effects in addition to the error term of the model. And we search in the analysis of variance and we also estimated the model parameters using two methods, the first is the least squares method and the second is the greatest weighting method, as well as checking some properties of the estimators, unbiased, consistent, efficient and sufficient, and we found confidence intervals for the variance components of the repeated measures model, and the hypotheses related to the model parameters were tested. Using the weighting ratio test
The purpose of the lecture
We chose real data for an experiment to illustrate the practical side of the methods used from the data set is to study the effect of season, plant variety, different levels of sulfur, two different levels of superphosphate and levels of soil leaching process on the height of lettuce plants grown in highly saline environment.