What will be the conclusion if regression analysis is better logarithmically than linearly?

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Ehsan Khedive

Dear Parmit,

It shows that the relationship between your variables are non-linear rather than linear. Depend on the regression coefficient sign it van be negative or positive. It tells that the scale of the relationship changes as the amount of two variables change.

Parmit Kumar Singh

I have predictor on x axis and the response due to change in the value of predictor is on y axis. The R is better for logarithmic option in excel regression analysis than linear. I understand theta relationship is non-linear and not linear. But my question is what the significance of log relation than linear in biology. why it is not linear and but log.

Jochen Wilhelm

Ok, so it seems that the predictor is logarithmic then. This means that the response changes by a constant amount when the predictor changes by a constant factor. The function/model is Y = b1*log(X)+b0. You can read it as a straight line with slope b1 and intercept b0. If you "remove" the log for X by exponentiating you get exp(Y)=X^b1 * exp(b0). So the response changes exponentially with a power of the predictor. This might help you to get an idea where a biological relevance might be (actually, i don't have an idea or example).