Hi All, Good Morning.
In a linear regression model that i am working on i have log transformed dependent variable due to data being skewed so below is the output from the model (Some of the values are mocked up with artificial var names):
Model
Parameter Estimate Standard Error Significance Value (P)
Intercept 7.346 0.12 .000
Carat 1.392 .009 .000
V GOOD -.211 .016 .000
GOOD -.134 .013 .000
Carat_I -0.44 .009 .000
Here as the Dep var is log transformed we need to measure and interpret the coefficient in terms of percent change. So my question is while keeping var Carat as controlled which is most significant variable.
As per the estimates value it should be Good as it has a better slope value, however
the regression equation = 7.346-0.211 = 7.135 (with V Good var)
the regression equation = 7.346-0.134 = 7.212 (with Good var)
This has lead to some confusion. request you to please let me know if i am ok interpreting the results.
Regards, Shivi
Thank you, yes i have checked all the assumptions and they are true.
However if you check the regression equation the variable Good has better value then V Good and that is what is confusing here.
Please ignore the below question of "However if you check the regression equation the variable Good has better value then V Good and that is what is confusing here"
After looking carefully i can relate why good will be than Vgood because these var are impacting the outcome negatively and good is impacting more than v good.
Sometimes you tend to get confused when you work closely with the people who are either new or are only masters on books.
Thanks for the community for the help.
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