How I interpret the beta coefficient with log outcome

[dode]

I am doing linear regression when i check my outcome was not normally distributed so I transformed into log and it's worked. Now how I interpret the beta coefficient in log scal?
Is there a way to expandcit it back to interpretation would be easier ?

[Ksharp]

log(y1)-log(y2)= beta*(x+1 - x)

==>

log(y1/y2)=beta

==>

y1/y2=exp(beta)

it is odds .

[PaigeMiller]

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@dode wrote:
I am doing linear regression when i check my outcome was not normally distributed so I transformed into log and it's worked. Now how I interpret the beta coefficient in log scal?
Is there a way to expandcit it back to interpretation would be easier ?

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The response variable does not have to be normally distributed to use linear regression.

The residuals have to be normally distributed to use linear regression, and then the requirement to be normally distributed is used only when doing statistical testing; it is not used when fitting the model.

So it is possible you did not need to perform the log transformation at all.

[dode]

Yes I checked the residual and it's not normally did so I had to transform my outcome using log. Now how I interpret the beta coefficient?

[PaigeMiller]

@Ksharp has already explained this.

Wikipedia has even more details.

[dode]

Sir, I am doing linear regression not logistic regression.