@article{Sihotang_Zuhri_2020, title={Loglinear Model Formation using Hierachial Backward Method}, volume={2}, url={https://talenta.usu.ac.id/jormtt/article/view/3754}, DOI={10.32734/jormtt.v2i1.3754}, abstractNote={<p><span class="fontstyle0">The loglinear model is a special case of a general linear model for poisson<br>distributed data. The loglinear model is also a number of models in statistics that are used to<br>determine dependencies between several variables on a categorical scale. The number of<br>variables discussed in this study were three variables. After the variables are investigated,<br>the formation of the loglinear model becomes important because not all the model<br>interaction factors that exist in the complete model become significant in the resulting<br>model. The formation of the loglinear model in this study uses the Backward Hierarchical<br>method. This research makes loglinear modeling to get the model using the Hierarchical<br>Backward method to choose a good method in making models with existing examples.<br>From the challenging examples that have been done, it is known that the Hierarchical<br>Reverse method can model the third iteration or scroll. Then, also use better assessment<br>methods about faster workmanship and computer-sponsored assessments that are used more<br>efficiently through compatibility testing for each model made</span></p>}, number={1}, journal={Journal of Research in Mathematics Trends and Technology}, author={Sihotang, Siti Fatimah and Zuhri}, year={2020}, month={Feb.}, pages={28-36} }