I've been given the simple linear regression model:
$y_i = β_0 + β_1x_i + ε_i$
Under the assumptions of a simple linear regression model, the question they ask is:
Assuming the usual model assumptions hold, show that the least squares and maximum likelihood estimators of $(β_0 β_1)'$ coincide
What I can't seem to comprehend is the $(β_0 β_1)'$ part. I've never seen this before.
Can anyone explain what $(β_0 β_1)'$ means?
Also, how would this proof of LS and MLE differ from the usual $β_0$ and $β_1$ calculations?
Note. I'm not asking for the proofs, I simply want to understand what they are trying to ask.