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When should we use the log-linear model? | by Robert Soczewica | Towards Data Science

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The world is not linear. It is a simple statement that everybody is aware of. However, it entails meaningful consequences to our modeling approaches. The vast majority of models used in academia and industry are linear models. The assumption of the linearity of phenomena under consideration is highly arbitrary. It is usually necessary for research that encompasses a small number of observations because it facilitates parameter estimations. When we have a larger sample of observations, we may consider non-linear dependencies between dependent and independent variables. To afford this, we may want to estimate a non-linear model. These kinds of models require more advanced estimation techniques and computation power. However, there is one vital alternative to this approach. Namely, we can approximate non-linear relations with the mean of linear models on transformed variables. The vastly utilized model that can be reduced to a linear model is the log-linear model described by below functi

When should we use the log-linear model? | Towards Data Science Skip to content Data Science When should we use the log-linear model? An attractive alternative to linear models Robert Soczewica Jan 26, 2021 2 min read Share Image by Gerd Altmann Pixabay The world is not linear. It is a simple statement that everybody is aware of. However, it entails meaningful consequences to our modeling approaches. The vast majority of models used in academia and industry are linear models. The assumption of the linearity of phenomena under consideration is highly arbitrary. It is usually necessary for resea

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