Consider the linear regression model without the intercept term $y=x+lon,$ where $=({}_1,$cdots,${}_p)$ are regression coefficients, the number of regressors $p$ equals the number of observations, $y=(y_1,$cdots,$y_p)$ represents the response variable, the design matrix $x$ is the identity matrix of size $p$ and the random error $lon$ has zero mean and variance$-$covariance matrix equal to $4x.$ For a given penalty parameter $=20,$ the variance and the bias of the ridge estimator of ${}_j,$ the $j-$th regression coefficient, for any jin$\{1,2,$cdots,$p\},$ are

$0\mathrm{.}009$ and $-0\mathrm{.}952{}_j$ respectively

$0\mathrm{.}036$ and $0\mathrm{.}952{}_j$ respectively

$0\mathrm{.}036$ and $-0\mathrm{.}952{}_j$ respectively

$0\mathrm{.}009$ and $0\mathrm{.}952{}_j$ respectively