# Trouble in comprehending question: Simple linear regression - LS and MLE

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.

• I expect it's a formatting typo, and the intention is $(β_0, β_1)'$ (equivalently $(β_0\;\; β_1)^T$)- which denotes the transpose of the row vector $(β_0, β_1)$ -- that is a column vector, consisting of those two elements. Commented Mar 12, 2014 at 23:13
• Thanks, this makes a lot of sense now. I'm still trying to comprehend how to go about this solution, but with the "," in place, the question is much clearer now.
– Joe
Commented Mar 12, 2014 at 23:25