This post is following above post.
In the previous post, I did multiple regression, s_ni_kg ~ s_po_kg + s_ni_to.
Let's add 'time' variables.
All time variables are not statistically significant by individual variable base.
Let's check if all time variables are jointly significant or not.
Firstly, I load car package.
Next, I use linerHypothesis() function.
p-value is 0.9999, so Null Hypothesis: All time variables are 0, cannot be rejected.
It means time is jointly insignificant.
Then, let's check if there is heteroskedasticity or not.
Fisrstly, manual method.
p-value is very less than 0.05. So, I see there is heteroskedasticity in reg3.
Next, let's use lmtest packages's bptest().
p-vallue is less than 0.05. So, there is heteroskedasticity.
If there is heteroskedasticity, er cannot use normoal inference,
so we need heteroskedasticity robust inference.
I use lmtest::coeftest() function and car::hccm().
We see s_po_kg and s_ni_to are still significant variables.
That's it. Thank you!
To read from the first post,