1) The least squares procedure minimizes the sum of
a) the residuals
b) squared maximum error
c) absolute errors
d) squared residuals
e) none of the above
2) a residual is
a) the difference between the mean of Y conditional on X and the unconditional mean
b) the difference between the regression prediction of Y and its actual value
c) the difference between the sum of squared errors before and after X is used to predict Y
d) the difference between the mean of Y and its actual value