<p>As stated already there is no problem if log(x) is negative. However, you would want to be careful if x=0 or if x&lt;0 because then the logs are undefined. So as long as x>0 then there are no problems at all.</p>

CC BY-SA 3.0
2016-11-13T21:26:10.507Z
2016-11-13T21:26:10.507Z
Andrew M
<p>No, $\log(x)$ being negative is not a problem what so ever.</p>

CC BY-SA 3.0
2016-11-13T16:29:48.143Z
2016-11-13T16:29:48.143Z
chan1142
<p>It is common to taking logs of variables when perform a regression analysis. But the observations will become negative after logs transformation when it is less than 1. Is this the possible concerns?
(e.g. log(0.5) = -0.6931, but log(5)= 1.6094) </p>

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2016-09-06T13:42:59.743Z
Can you use negative temperatures in a weather forecast or does it break the regression?
Giskard
CC BY-SA 3.0
2016-09-06T13:50:24.290Z
If your variables of concern are always positive, and your regression extrapolates to give a negative prediction, you could have problems.   But if you have taken logarithms and then get a negative prediction then take anti-logarithms (exponentiation if working in base $e$) of that to give you a positive result
Henry
CC BY-SA 3.0
2016-09-06T14:22:31.500Z
@Henry I do not understand. Why would it be a concern that the predicted value of log of a variable is negative? This does not make the variable itself negative.
Giskard
CC BY-SA 3.0
2016-09-06T14:26:41.587Z
@denesp: exactly the opposite.  If you are modelling, for example, hours worked, and your extrapolation gives a negative result for certain circumstances, that may be an issue.  But if you are modelling logarithms of hours worked and your extrapolation gives a negative result for certain circumstances, that is less of a problem as it translates into a small positive number of hours ($\lt 1$)
Henry
CC BY-SA 3.0
2016-11-13T16:37:03.280Z
What is the underlying problem you are tring to solve? The question as it stands is barely meaningful.
410 gone
<regression>
Taking logarithms of variables
XJ.C
# Taking logarithms of variables

(Asked by: username_0 on 2016-09-06)

It is common to taking logs of variables when perform a regression analysis. But the observations will become negative after logs transformation when it is less than 1. Is this the possible concerns?
(e.g. log(0.5) = -0.6931, but log(5)= 1.6094)

**Tags:** regression

> **Comments:**
> **username_1**: Can you use negative temperatures in a weather forecast or does it break the regression?  
> **username_2**: If your variables of concern are always positive, and your regression extrapolates to give a negative prediction, you could have problems.   But if you have taken logarithms and then get a negative prediction then take anti-logarithms (exponentiation if working in base $e$) of that to give you a positive result  
> **username_1**: @username_2 I do not understand. Why would it be a concern that the predicted value of log of a variable is negative? This does not make the variable itself negative.  
> **username_2**: @denesp: exactly the opposite.  If you are modelling, for example, hours worked, and your extrapolation gives a negative result for certain circumstances, that may be an issue.  But if you are modelling logarithms of hours worked and your extrapolation gives a negative result for certain circumstances, that is less of a problem as it translates into a small positive number of hours ($\lt 1$)  
> **username_3**: What is the underlying problem you are tring to solve? The question as it stands is barely meaningful.  

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## 2 answers

### Answer by username_4 on 2016-11-13. Score: 1 | Accepted answer

As stated already there is no problem if log(x) is negative. However, you would want to be careful if x=0 or if x<0 because then the logs are undefined. So as long as x>0 then there are no problems at all.


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### Answer by username_5 on 2016-11-13. Score: 0

No, $\log(x)$ being negative is not a problem what so ever.


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