To identify the outliers in the given data set, we can use the IQR (Interquartile Range) method. Here are the steps:
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Order the Data: The data is already in order: 2, 23, 27, 29, 30, 32, 32, 34, 35, 96
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Find the quartiles:
- Q1 (the first quartile) is the median of the first half of the data.
- Q3 (the third quartile) is the median of the second half of the data.
The dataset contains 10 values, so:
- The first half is: 2, 23, 27, 29, 30 (Median for Q1 is 27)
- The second half is: 32, 32, 34, 35, 96 (Median for Q3 is 34)
Q1 = 27, Q3 = 34
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Calculate the IQR: IQR = Q3 - Q1 = 34 - 27 = 7
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Determine the outlier boundaries:
- Lower Bound = Q1 - 1.5 * IQR = 27 - 1.5 * 7 = 27 - 10.5 = 16.5
- Upper Bound = Q3 + 1.5 * IQR = 34 + 1.5 * 7 = 34 + 10.5 = 44.5
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Identify outliers: Any value below 16.5 or above 44.5 is considered an outlier. The only values we have to check against these bounds are as follows:
- 2 (is less than 16.5, so it is an outlier)
- 23 (not an outlier)
- 27 (not an outlier)
- 29 (not an outlier)
- 30 (not an outlier)
- 32 (not an outlier)
- 34 (not an outlier)
- 35 (not an outlier)
- 96 (is greater than 44.5, so it is an outlier)
Thus, the two outliers are:
A. 2
D. 96