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.dat as a population.

a Select an SRS of size 2000 from the 581,012 records. Keep this sample or the random number seed you used to generate the sample, for later use.

b Using your SRS, estimate the percentage of cells in each of the 7 forest cover types, along with 95% CIs.

c Estimate the average elevation in the population, with 95% CI.