In this problem, we fit ridge regression on the same dataset as in problem 1. first, standardize the variables so that they are on the same scale. next, choose a grid of values ranging from = 1010 to = 10−2 , essentially covering the full range of scenarios from the null model containing only the intercept, to the least squares fit. for example: > grid = 10^seq(10, -2, length=100) 1) write an r function to do the following: associated with each value of , compute a vector of ridge regression coefficients (including the intercept), stored in a 20 × 100 matrix, with 20 rows (one for each predictor, plus an intercept) and 100 columns (one for each value of 2) to find the "best" , use ten-fold cross-validation to choose the tuning parameter from the previous grid of values. set a ra.