Transforming variables can help meet assumptions such as linearity and homoscedasticity from "summary" of Introduction to Econometrics by Christopher Dougherty
When fitting a linear regression model, it is crucial to ensure that the underlying assumptions are met for the model to be valid. Two key assumptions are linearity and homoscedasticity. Linearity assumes that the relationship between the independent and dependent variables is linear, while homoscedasticity assumes that the variance of the errors is constant across all levels of the independent variables. However, in practice, the relationship between variables may not always be linear or the variance of the errors may not be constant. This is where transforming variables comes into play. By transforming variables, we can potentially meet the assumptions of linearity and ...
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