Here is a data set (n = 117) that has been sorted. 57.6 59.3 61.4 63.3 63.5 64.1 64.7 66.1 66.3 66.9 67 69.4 69.9 71.4 71.9 72.4 72.4 73.1 73.2 73.2 73.5 73.6 73.7 74.1 74.1 74.4 74.5 74.7 74.7 74.7 75.2 75.3 75.7 76 76.2 76.2 76.3 76.3 76.4 76.6 76.6 77 77 77.1 77.2 77.4 77.5 77.5 77.5 77.5 77.6 78 78.1 78.3 78.4 78.4 78.5 79.1 79.2 79.3 79.4 79.6 79.7 80.1 80.2 80.8 80.9 81 81 81 81.1 81.3 81.5 81.8 82 82.3 82.7 82.8 83 83.3 83.3 83.4 83.4 83.7 84.3 84.8 84.9 84.9 85.1 85.2 85.2 85.2 85.4 86 86.1 86.1 86.7 86.8 86.8 86.9 87.1 87.1 88.3 88.5 89.6 90.6 90.9 91.5 91.8 94.2 94.4 94.6 94.8 95.5 97.6 99.4 99.8 30 25 20 Frequency 15 10 55 60 65 70 75 80 85 90 95 100 length (cm) To find P2, what is the value of the locator? L = Use the locator, give the value for the 2-Percentile: P2 =Here is a sample data set (n = 48) that is nearly normal (as can be seen in the histogram provided after the data set). 19.9 24.5 25.1 25.9 26.1 26.7 27.4 28.3 28.3 28.3 28.5 29 30.1 30.5 30.9 30.9 31.2 31.5 32.2 32.6 32.6 32.7 33.2 34.1 34.1 34.9 36 37.2 37.5 38.2 38.2 39.6 40.2 40.5 40.9 41.3 41.3 41.3 41.9 42.5 43.4 43.5 43.5 44.7 46.2 46.3 46.7 48.4 14 12 10 Frequency A 15 20 25 30 35 40 45 50 length (cm) Q How many mild outliers are in this data set? ans = How many extreme outliers are in this data set? ans =Here is a sample data set (n = 48) that is nearly normal (as can be seen in the histogram provided after the data set). 13 13.6 13.6 16.8 17.6 18.8 20.6 23.8 23.8 24.3 24.9 25.1 25.3 25.8 25.8 26 26 27.6 28.2 28.4 28.4 28.5 29.4 29.4 29.6 30 30.8 31.6 31.6 31.8 32 32.2 32.4 34.8 35.1 36.5 36.7 38 39.4 39.8 40.7 41.2 41.6 43.2 43.2 45 46 46.4 14 12 10 Frequency + 10 15 20 25 30 35 40 45 50 data Q How many outliers are in this data set? ans =Here is a data set: 3730 3860 4970 4800 4080 5540 4380 3730 4610 5480 4800 5150 3880 4760 4790 4470 The goal is to construct a grouped frequency distribution table (GFDT} for this data set. The GFDT should have it) classes with a "nice" class width. Each class should contain its lower class limit and each sequential lower class limit should increase by increments of the class width. This problem is to determine what the class width should be. What is the maximum of this data set? max = What is the minimum of this data set? min: What is the range of this data set? range = What is the (most likely) discrete unit for this data set? discrete unit = What is the span ofthe range? span = Using this value, ifthe goal is to have 10 classes, what is the nicest class width? optimal class width =