![]() The actual quantity you will be modeling after exponentiation of predictions would depend on the distribution you are assuming for the underlying data. $exp(\cdot)$ ) and still claim that you are modeling the expected value. Given that linear regression has a linearity (not the same as affine) baked into the Gauss-Markov theorem you cannot simply transform the results via anti-logs (e.g. The more subtle aspect of the change is that the logarithm transformation is non-linear. ![]() Note that it's highly unlikely that real data would all be the same value in the $y_i$. Transforming the numeric values of the $y_i$s will change the results of your analysis and interpretation.Ĭaveat: Unless all $y_i$ values are the same value and are the fixed point of the logarithm, your regression will be changed. I am assuming in my answer that the $ln(GDP per capita)$ is the response variable or target, denoted $y_i$. And if I use ln(GDP per capita) for a linear regression, does that change my results?
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