Exercise 3.6

The least squares line is given by:

\hat{y} = \hat{\beta_0} + \hat{\beta_1} \times x

where \hat{\beta_0} and \hat{\beta_1} are the least squares coefficient estimates for simple linear regression.

By definition, \hat{\beta_0} is:

\hat{\beta_0} = \bar{y} - \hat{\beta_1} \times \bar{x}

where \bar{y} and \bar{x} are the average values of y and x, respectively.

Since we want to know if the least squares line always passes through the point (\bar{x}, \bar{y}), all we have to do is to substitute (\bar{x}, \bar{y}) into the first equation above and see if the condition is satisfied. We get:

\bar{y} = \hat{\beta_0} + \hat{\beta_1} \times \bar{x}

and substituting the expression above for \hat{\beta_0}, we obtain:

\bar{y} = \bar{y} - \hat{\beta_1} \times \bar{x} + \hat{\beta_1} \times \bar{x}

Since this is always true, we conclude that the least squares line always passes through the point (\bar{x}, \bar{y}).