Consider the linear regression model
\[
y_i = \beta_0 + \beta_1 x_i + \epsilon_i, i = 1, 2, \dots, n, \text{where} \epsilon_i \text{ are i.i.d. standard normal random variables. Given that}
\]
\[
\frac{1}{n} \sum_{i=1}^n x_i = 3.2, \frac{1}{n} \sum_{i=1}^n y_i = 4.2, \frac{1}{n} \sum_{j=1}^n \left( x_j - \frac{1}{n} \sum_{i=1}^n x_i \right)^2 = 1.5,
\]
\[
\frac{1}{n} \sum_{j=1}^n \left( x_j - \frac{1}{n} \sum_{i=1}^n x_i \right) \left( y_j - \frac{1}{n} \sum_{i=1}^n y_i \right) = 1.7,
\]
the maximum likelihood estimates of \( \beta_0 \) and \( \beta_1 \), respectively, are