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What happens if log transformation does not normalize data?
1 Answer. Log transformation leads to a normal distribution only for log-normal distributions. Not all distributions are log-normal, meaning they will not become normal after the log transformation.
Why is a log transformation not possible?
Unfortunately, data arising from many studies do not approximate the log-normal distribution so applying this transformation does not reduce the skewness of the distribution. In fact, in some cases applying the transformation can make the distribution more skewed than the original data.
Why do we do log transformation?
The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics. Figure 1 shows an example of how a log transformation can make patterns more visible.
Why is log transformation skewed?
Notice how large values on the x-axis are relatively smaller on the y-axis. Now, in a right-skewed distribution you have a few very large values. The log transformation essentially reels these values into the center of the distribution making it look more like a Normal distribution.
What does log transformation do to data?
Log Transformations. The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics.
Why does log transformation make data normal?
2.1. Using the log transformation to make data conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution. In this case, the log-transformation does remove or reduce skewness.
How do you interpret log transformed data?
Only the dependent/response variable is log-transformed. Exponential the coefficient, subtract one from this number, and multiply by 100. Only independent/predictor variable(s) is log-transformed. Both dependent/response variable and independent/predictor variable(s) are log-transformed.
Why is data transformation important in statistics?
Transforms are usually applied so that the data appear to more closely meet the assumptions of a statistical inference procedure that is to be applied, or to improve the interpretability or appearance of graphs. Nearly always, the function that is used to transform the data is invertible, and generally is continuous.
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