Loglinear models:
Loglinear models are models that postulate a linear relationship between the independent variables and the logarithm of the dependent variable, for example:

where y is the dependent variable; x_{i}, i=1,...,N are independent variables, and {a_{i}, i=0,...,N} are parameters (coefficients) of the model.
Loglinear models, for example, are widely used to analyze categorical data represented as a contingency table . In this case, the main reason to transform frequencies (counts) or probabilities to their logvalues is that, provided the independent variables are not correlated with each other, the relationship between the new transformed dependent variable and the independent variables is a linear (additive) one. For example, a simple bivariate independence model for two categorical variables X and Y

transforms to:

where
