S
Sunil Sanga
Member
Ch 13 page 21 ..... Full Normal Model and inference.... We assume ei follows normal distribution with mean 0 and Variance sigma square. This will help us to know the distribution of Yi and Bi.
There is a note given in bottom of this page about to derive MLE for parameter "a and b". It is also mentioned that least square doesn't provide us the distribution of Yi.
What does this mean....as it's already mentioned above that Yi is normally distributed with mean E(Yi)=a+bxi and Variance=sigma square
Can anyone relate this ??
There is a note given in bottom of this page about to derive MLE for parameter "a and b". It is also mentioned that least square doesn't provide us the distribution of Yi.
What does this mean....as it's already mentioned above that Yi is normally distributed with mean E(Yi)=a+bxi and Variance=sigma square
Can anyone relate this ??