Consider the linear regression model y
i = β
0 + β
1x
i + ∈i , i = 1, 2, … , 6, where β
0 and β
1 are unknown parameters and ∈
i ’s are independent and identically distributed random variables having N(0, 1) distribution. The data on (x
i, y
i) are given in the following table.
xi | 1.0 | 2.0 | 2.5 | 3.0 | 3.5 | 4.5 |
yi | 2.0 | 3.0 | 3.5 | 4.2 | 5.0 | 5.4 |
If
\(\hat{\beta}_0\) and
\(\hat{\beta}_1\) are the least squares estimates of β
0 and β
1, respectively, based on the above data, then
\(\hat{β}0 + \hat{β}1\) equals __________ (round off to 2 decimal places)