I have a regression where I am trying to predict Y, using several variables. My variables are A, B, C.
R for my analysis. My equation is as follows:
mod <-lm( log(Y/B) ~ log(A/B) + log(A/C), data = dataframe)
and a summary of the model is as follows:
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.326698 0.080683 -16.44 <2e-16 *** log(A/B) 0.756779 0.007507 100.81 <2e-16 *** log(A/C) -0.292373 0.012762 -22.91 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6925 on 17063 degrees of freedom Multiple R-squared: 0.4043, Adjusted R-squared: 0.4042 F-statistic: 5790 on 2 and 17063 DF, p-value: < 2.2e-16
I understand that
log(j/k) = log(j) - log(k); so
log(Y/B) can be written similarly.
Is there a way that I can include the
B variable in the dependent part of my regression equation?
Also, it is acceptable to use a variable for normalizing, on both dependent and independent variables? I have seen other examples where the same variable is used in this way, but I was not certain if this makes sense.