Well, this is 100% off-topic... And I wasn't planning to answer the OP's question.
However, I disagree with your answer. > There is no requirement that the dependent variable in a "regression" type > estimation follows a gaussian distribution. False. It's depends on what type of '"regression" type estimation' one uses, among other things. > You need a model of the > process and then use an estimation technique to estimate your model. If > effects in your model are additive do not use a log transformation. If > effects are multiplicative then use a log transformation. The main question is, does the model satisfy the *assumptions*. > The choice > should be determined by the mechanics of the problem and not by the > statistics. While a mechanistic understanding is definitely valuable... If the criteria for a good model vs a bad model, was whether the model was consistent with mechanistic theory/understanding, then nearly every statistical model I've seen would be a bad model. I would say, a good model is one that is useful... > If you do use a log transformation the trying to reverse the > process using an exponential transformation will be biased. > The extent of > that bias depends on your problem and it would not be possible to estimate > the significance of the bias without a much greater knowledge of the > process and data. Never heard of this before... But I do note back-transformation is not trivial, and I'm not an expert on back-transformations. > I would suggest that you consult a competent > statistician. I agree on that part... ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
