the forest data are from kdd.ics.uci.edu/databases/covertype/covertype.data.html (blackard, 1998). they consist of a subset of the measurements from 581,012 30×30m cells from region 2 of the u.s. forest service resource information system. the original data were used in a data mining application, predicting forest cover type from covariates. data-mining methods are often used to explore relationships in very large data sets; in many cases, the data sets are so large that statistical software packages cannot analyze them. many data-mining problems, however, can be alternatively approached by analyzing probability samples from the population. in these exercises, we treat forest as a population. select an srs of size 2000 from the 581,012 records. set 710 as the random number seed you used to generate the sample. (1pt) using your srs sample in part a), estimate the percentage of cells in each of the 7 forest cover types, along with 95% cis. (3.5pts) estimate the average elevation in the population, with 95% ci. (1.5pts)