We are asked to use R to solve this following problem, please help with the screenshot of your code and plots. thanks in advance.

Question 2 (3 Points] Consider the situation where we may want to explain each response variable Y ER by a p-dimensional variable X~ Unif([0, 1]"). Suppose our data consists of n i.i.d. observations (Y;, X:){i=1...n) of the variables Y and X. We could then model them with the classic regression equation Y = f(x) +Ei i = 1,...,n with f : [0,1]+ R and 1,..., Er are independent and centered random variables. It is typical to assume that the function f is smooth and we can estimate f(x) by some averaging of the Y, associated to the X, in the vicinity of x. The simplest version of this idea is the k-nearest neighbor estimator where f(x) is estimated by the mean of the Y associated with the k points X, that are nearest to x. This works well in a low-dimensional setting as it is easy to make sense of what nearest points" means. (a) Show that the notion of nearest points vanishes as the dimensionality p increases by plotting the histogram of the distribution of pairwise-distances {||X; -X;1:1