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The logarithm of a number is the power to which the base must be raised to get the number.

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Why is it okay to take the log (or any other transformation) of the dependent variable?

You asked so many questions , I didn't know which one to ignore ... But I trust my discussion here helps you be less befuddled about the role of variance stabilizing transformations. Optimal Box-Cox …
IrishStat's user avatar
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5 votes
Accepted

How can I recognize when I must apply "log transformation"?

Please review When (and why) should you take the log of a distribution (of numbers)? . I have programmed this in AUTOBOX ( a commercially available time series software package which I have helped de …
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133 votes
Accepted

When (and why) should you take the log of a distribution (of numbers)?

If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specifi …
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1 vote
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Detrending or not and should I always take log first?

See How to detect seasonality from plotted data without using tools or libraries and the link to when and why you should take logs might be of help to you. Untreated deterministic structure .. pulses/ …
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1 vote

Removing Variance in Time Series After Applying Log Transformation

Five (not mutually exclusive) possibilities come to mind: The Slutsky http://www-history.mcs.st-andrews.ac.uk/Biographies/Slutsky.html Effect ... Unnecesaary differencing can INJECT variability. Con …
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