It's ok, I've got it done already. Thx alot for explaining patiently 🙂
But if you don't mind to help me one more time..
I did the regression analysis. I know that generally, the value of coefficient determination will become higher as we add more variables into the model. But the problem is, eventhough the coefficient determination is high as much as 99%, I got some variables insignificant, even at alpha 10%. Do you know what's the reason it may possibly happen?
Coefficient of determination (R-squared) and insignificant variables are independent. One does not imply the other.
Then why do you think of model which has insignificant variables and high coefficient of detemination at the same time?
Just because you put a term into a model that fits well, it does not mean that the term is going to be significant. It means that this term is really not contributing to the fit.
Ok then, thankyou so much
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