In this video, we will discuss one of the problems we may face, if not more than this problem is called
Petros Clandestinity.
If you remember in the lecture in which we found out the confidence interval of Bita.
I told you that this dumb sigma squared, which is the variance in the error term, is helping us find
out the confidence interval of beta.
There was one underlying assumption regarding the Sigma squared, based on which we calculated the standard
errors.
Confidence intervals, etc..
The assumption is that this variance value, it's constant.
Unfortunately, it is often the case that variances of the error terms are non constant.
For instance, the variances of error terms may increase with the value of the response that is deemed
by variable GraphicLy.
We can plot Deidre's do against Y to identify the non constant variance of dieters.
As you can see in this graph, this funnel like shape is suggesting non constant variance.
This issue is known as a cadastre city.
You can see that the magnitude of residuals tends to increase with defeated values.
Why so variance at this point is limited within this range.
Whereas variants here is larger as the boundaries are for the really.
Now, how to handle heteros, cadastre city.
When you faced this problem, where did variances increasing with larger footed values of why?
We need to scale down these large values of a.
So we can use functions like logo fly or under route way so that larger, well lives away are shut down.
Thus leading to reduction in heteros congested city.
You can see the graph of residuals against Longway.
Do they do it now appear to have constant variants?
Although there is some evidence of a slight non-linear relationship in the data.
So this is how we identify, if that is heteros contested city in the model, and if there is, this
is how we handle it.