Question

Discuss and explain the log-linear model and the logit model. Give examples of when these models are used?

Answer 1

linear model:-

Linear models describe a continuous response variable as a function of one or more predictor variables.

They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. Linear regression is a statistical log linear m,odelmethod used to create a linear model.

log linear model:-

A log-linear model is a mathematical model that takes the form of a function whose logarithmequals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression.

when these models are used :-

The loglinear show is one of the particular instances of summed up direct models for Poisson-appropriated information.

Loglinear investigation is an augmentation of the two-way possibility table where the contingent connection between at least two discrete, clear cut factors is dissected by taking the characteristic logarithm of the cell frequencies inside a possibility table.

Despite the fact that loglinear models can be utilized to dissect the connection between two all out factors (two-way possibility tables), they are all the more normally used to assess multiway possibility tables that include at least three factors. The factors examined by log direct models are altogether treated as "reaction factors".

As such, no refinement is made among autonomous and subordinate factors. In this way, loglinear models just exhibit relationship between factors.

On the off chance that at least one factors are treated as expressly needy and others as autonomous, at that point logit or calculated relapse ought to be utilized. Additionally, if the factors being explored are consistent and can't be separated into discrete classifications, logit or strategic relapse would again be the fitting investigation.