The following 10 scores were input in the gradebook for Prof. Torres's class. \[ 17,73,75,84,84,88,89,93,94,190 \] Identify all values that are outliers. If there is more than one outlier, separate them with commas. If there are no outliers, click on "None".
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To identify outliers, we can use the interquartile range (IQR) method. First, we need to find the first quartile (Q1), the third quartile (Q3), and then calculate the IQR, which is Q3 - Q1. 1. Arrange the scores in ascending order: \[ 17, 73, 75, 84, 84, 88, 89, 93, 94, 190 \] 2. Q1 (25th percentile) is the average of the 2nd and 3rd values: \[ Q1 = \frac{73 + 75}{2} = 74 \] 3. Q3 (75th percentile) is the average of the 8th and 9th values: \[ Q3 = \frac{93 + 94}{2} = 93.5 \] 4. Calculate the IQR: \[ IQR = Q3 - Q1 = 93.5 - 74 = 19.5 \] 5. Determine the lower and upper bounds for outliers: - Lower bound: \( Q1 - 1.5 \times IQR = 74 - 29.25 = 44.75 \) - Upper bound: \( Q3 + 1.5 \times IQR = 93.5 + 29.25 = 122.75 \) The outliers are any values below the lower bound or above the upper bound. Therefore, the scores greater than 122.75 are considered outliers. The values that are outliers in this dataset are: 190