Loglinear Model Formation using Hierachial Backward Method
DOI:
https://doi.org/10.32734/jormtt.v2i1.3754Keywords:
Backward Hierarchical Method, Categorical Scale, Loglinear Model, Modeling, Poisson DistributionAbstract
The loglinear model is a special case of a general linear model for poisson
distributed data. The loglinear model is also a number of models in statistics that are used to
determine dependencies between several variables on a categorical scale. The number of
variables discussed in this study were three variables. After the variables are investigated,
the formation of the loglinear model becomes important because not all the model
interaction factors that exist in the complete model become significant in the resulting
model. The formation of the loglinear model in this study uses the Backward Hierarchical
method. This research makes loglinear modeling to get the model using the Hierarchical
Backward method to choose a good method in making models with existing examples.
From the challenging examples that have been done, it is known that the Hierarchical
Reverse method can model the third iteration or scroll. Then, also use better assessment
methods about faster workmanship and computer-sponsored assessments that are used more
efficiently through compatibility testing for each model made
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