To estimate the standard deviation from a histogram, we can follow these steps:
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Identify the data vision range: The given bins from the histogram are as follows:
- 2 to 6
- 6 to 12
- 10 to 15
- 14 to 21
- 18 to 27
- 22 to 27
- 26 to 18
- 30 to 15
- 34 to 9
- 38 to 6
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Determine the midpoints for each bin: The midpoint of each bin can be calculated as follows:
- Midpoint for 2-6: (2 + 6) / 2 = 4
- Midpoint for 6-12: (6 + 12) / 2 = 9
- Midpoint for 10-15: (10 + 15) / 2 = 12.5
- Midpoint for 14-21: (14 + 21) / 2 = 17.5
- Midpoint for 18-27: (18 + 27) / 2 = 22.5
- Midpoint for 22-27: (22 + 27) / 2 = 24.5
- Midpoint for 26-18: (26 + 18) / 2 = 22
- Midpoint for 30-15: (30 + 15) / 2 = 22.5
- Midpoint for 34-9: (34 + 9) / 2 = 21.5
- Midpoint for 38-6: (38 + 6) / 2 = 22
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Count the frequencies of each bin. It appears the frequencies are possibly written right after the x-values (the data you provided is mixed):
- Frequency for 2-6: 6
- Frequency for 6-12: 12
- Frequency for 10-15: 10
- Frequency for 14-21: 15
- Frequency for 18-27: 21
- Frequency for 22-27: 27
- Frequency for 26-18: 18
- Frequency for 30-15: 15
- Frequency for 34-9: 9
- Frequency for 38-6: 6
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Calculate the mean of the data set.
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Calculate the variance from the frequencies and midpoints.
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Take the square root of the variance to find the standard deviation.
Given the data and the options provided, if I go by a standard consideration in identifying potential standard deviation values based on usual distributions likely around the center of the data range (e.g., 16 and 8 are common), and when estimating from a histogram intuitively, the closest estimate would likely be 16 given that the options available.
Important: The analytical steps above provide the conceptual method, but actual calculations based on exact frequency and subsequent calculations are necessary for a definitive answer. Please recheck the actual histogram data or calculations for clarity.