First, we sort the given data to facilitate the calculation of quartiles and the five-number summary. The sorted data is:
\[ [1, 3, 4, 5, 6, 7, 8, 9, 9, 9, 10] \]
To find the quartiles, we use the formula for the rank of the quantile:
\[ \text{Rank} = Q \times (N + 1) \]
where \( Q \) is the quantile value and \( N \) is the number of data points.
First Quartile (\(Q_1\)):
\[
\text{Rank} = 0.25 \times (11 + 1) = 3.0
\]
The value at position 3 is 4. Thus, \( Q_1 = 4 \).
Second Quartile (\(Q_2\)) or Median:
\[
\text{Rank} = 0.5 \times (11 + 1) = 6.0
\]
The value at position 6 is 7. Thus, \( Q_2 = 7 \).
Third Quartile (\(Q_3\)):
\[
\text{Rank} = 0.75 \times (11 + 1) = 9.0
\]
The value at position 9 is 9. Thus, \( Q_3 = 9 \).
The interquartile range is calculated as:
\[ \text{IQR} = Q_3 - Q_1 = 9 - 4 = 5 \]
The five-number summary consists of the minimum, first quartile, median, third quartile, and maximum values:
- Minimum: 1
- First Quartile (\(Q_1\)): 4
- Median (\(Q_2\)): 7
- Third Quartile (\(Q_3\)): 9
- Maximum: 10
Thus, the five-number summary is:
\[ [1, 4, 7, 9, 10] \]
- Quartiles:
- \( Q_1 = \boxed{4} \)
- \( Q_2 = \boxed{7} \)
- \( Q_3 = \boxed{9} \)
- Interquartile Range (IQR): \( \boxed{5} \)
- Five-Number Summary: \( \boxed{[1, 4, 7, 9, 10]} \)