When constructing a confidence interval using the percentile method in R, We use the sort() command to sort the bootstrap statistics from smallest to largest. We follow with the quantile() command to determine the values of the percentiles corresponding to the lower and upper bounds. Up to this point, we have not actually looked at the results of the sort() command, but will do so here to try to determine the values for the lower and upper bounds. Suppose we want to construct a 90% confidence interval for the population proportion of middle school students who have their own cellphone. We take a random sample of students and record whether or not each student has their own cellphone. Then we generate 20 bootstrap samples and calculate the sample proportion who have their own cellphone for each bootstrap sample. (Of course, we'd want to generate several thousand bootstrap samples, but we'll generate only 20 for illustration purposes.) After using the sort() command, here are the 20 bootstrap sample proportions: 988 0.70 0.70 0.72 0.72 0.72 88 To determine the lower and upper bounds just using this information, recall that the lower bound of a 90% confidence interval is at the 5th percentile. With 20 bootstrap statistics, the 5th percentile is the (0.05/20) = 1st ordered value = 0.60. Therefore, 0.60 is the lower bound. (In general, to determine which ordered value is at a certain percentile, multiply (n) (desired percentile as a proportion).) 1. The upper bound of a 90% confidence interval is at the th percentile. 2. Therefore, of the 20 bootstrap statistics above, which ordered value is the upper bound? (Be careful: this question is not asking for the value of the proportion. It's asking for the ordered value, such as the 1st, 2nd, 3rd, etc.) Enter an integer value only. 3. Based on the 20 ordered bootstrap sample proportions above, report the lower and upper bounds of the 90% confidence interval for the proportion of all middle school students who have their own phone using the percentile method.