Negative Feature Importance on Linear Regression Using the statsmodels OLS and checking the coefficients of the independent variables, some had negative coefficients. How to interpret them exactly?
Context:
In a dataset with ecommerce transactional data, I used the revenue from the datapoint period, the average amount sold by the datapoint store and the average revenue from the datapoint store.
The datapoint store's average revenue coefficient was negative. What does it mean ?
edit:
The values of x and y are in natural logarithm in the model.

Linear regression parameters are not the same as the importance of the parameters. First, the scale of the parameters depends on the scale of the features, so to use it as an important measure you need to scale the features. Second, as you noticed, the coefficients can be either positive or negative, and obviously "negative importance" is rather meaningless. When the coefficient has a positive sign it means that the predictions raise as the values of the feature raise, while with a negative sign the predictions decrease as the values of the feature raise. Moreover, keep in mind that you cannot interpret those parameters independently of other parameters in the model.
