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From |
Phil Schumm <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Log Transform Justification |

Date |
Mon, 27 Aug 2007 08:10:20 -0500 |

On Aug 27, 2007, at 3:43 AM, Md Bilal Hossain wrote:

In my PhD project, I was having problem to have a good model with Thikonov Regularization in inverse problem. I checked the data and try to get a good distribution of the data set. I did log transform of raw data and run the model with transformed data. The model that I get is fantastic. Now I am happy to put the model, however, I think I have to justify the data transformation. So how can I justify?

First, understand that you have not provided enough information for anyone to give a specific answer to your question. The most general answer, of course, is that the justification is this: this is the model that fits the data. Models that clearly don't fit the data are not useful (at least from a data-analytic perspective).

In my experience, reviewers/readers who are bothered by a transformation of the data come from two main groups. The first are those who are having difficulty interpreting the results of the model (this is by far the largest group). For those, translating your results back onto the original scale is usually sufficient (though be careful how you do this). The second are people who genuinely have a problem with the model because it appears to violate some theoretical expectation (these are typically individuals from a mathematical discipline such as physics or economics). In this case, you may be obligated to demonstrate that the model on the untransformed data does not fit well, either graphically or via a formal test. At that point, one of two things must be true: either the theory is wrong, or your data are bad. Either way, this gets you off the topic of your analysis and into other questions.

-- Phil

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**References**:**st: Log Transform Justification***From:*Md Bilal Hossain <[email protected]>

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