To determine the "usual" values in the data set and identify any outliers, we can use the Interquartile Range (IQR) method. The steps to find the limits are as follows:
1. **Order the Data**: Sort the data from smallest to largest.
```
20, 20, 21, 22, 23, 24, 25, 25, 26, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, 31, 31, 31, 31, 32, 33, 33, 33, 33, 34, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39, 40, 41, 42, 62
```
2. **Calculate Q1 (First Quartile)**: This is the median of the first half of the data (the lower half).
- The lower half: \(20, 20, 21, 22, 23, 24, 25, 25, 26, 27, 27, 27, 28, 28\)
- Median of the lower half (Q1) = Average of 25th and 26th values = (25 + 26) / 2 = 25.5.
3. **Calculate Q3 (Third Quartile)**: This is the median of the second half of the data (the upper half).
- The upper half: \(28, 28, 29, 29, 29, 29, 30, 31, 31, 31, 31, 32, 33, 33, 33, 34, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39, 40, 41, 42, 62\)
- Median of the upper half (Q3) = Average of 35th and 36th values = (35 + 36) / 2 = 35.5.
4. **Calculate IQR**:
\[
IQR = Q3 - Q1 = 35.5 - 25.5 = 10.
\]
5. **Determine Outlier Limits**:
- Lower limit: \( Q1 - 1.5 \times IQR = 25.5 - 1.5 \times 10 = 25.5 - 15 = 10.5. \)
- Upper limit: \( Q3 + 1.5 \times IQR = 35.5 + 1.5 \times 10 = 35.5 + 15 = 50.5. \)
Thus, the usual range of values is between **10.5** and **50.5**.
6. **Identify Outliers**: Any value outside of these limits is considered an outlier.
- Outliers are: \( 62 \) (as it is greater than 50.5).
In conclusion:
- The limits for usual values are **10.5** to **50.5**.
- There is **1 outlier** in the data set (the value **62**).