= Solution
The <generalized cross-validation> curve reaches its minimum at \b[$\lambda=5$] among the supplied grid values. This balances improved conditioning against shrinkage bias using an estimate of prediction error, rather than choosing the penalty with the smallest training <residual sum of squares>. Nearby values have similar errors, so the plot does not establish a highly precise optimal penalty.
Reading the PDF table at $\lambda=5$ gives the fitted <regression intercept> and slopes:
$$
\boxed{\widehat\alpha=0.8695819,\quad
\widehat\beta=(0.5363716,\ 0.4143406,\ -0.01248621,\ 0.10434493,\ 0.7284822,\ -0.002794344)^T.}
$$
These table entries are necessary because the TeX stores the table only inside a figure. The <ridge regression> slopes are shrunk relative to the zero-penalty fit; their small nonzero values do not represent variable exclusion.
There is a minor source inconsistency: the prose says the predictors are centered, but the table's intercept varies with $\lambda$. With exactly centered predictor columns and an unpenalized intercept it would remain $\overline Y$. The numbers above faithfully report the printed table, while part (a) gives the centered formula and the general uncentered conversion. The table therefore reflects a different or incompletely described preprocessing convention.
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