Answer :
Final answer:
To identify outliers in a dataset, calculate the interquartile range (IQR), then find the boundaries by adding and subtracting 1.5 times the IQR from the third and first quartiles, respectively. Any data points outside these boundaries are outliers. What to do with an outlier depends on the context of the data and the research question.
Explanation:
To determine how many outliers are in a data set, we typically use the interquartile range (IQR) method. The IQR is the difference between the third quartile (Q3) and the first quartile (Q1) of the data. Once we have the IQR, we can find the boundaries for potential outliers by calculating Q1 - 1.5 * IQR and Q3 + 1.5 * IQR. Any data points outside these boundaries are considered outliers.
For the dataset provided, the IQR needs to be calculated first, followed by the lower and upper bounds. Then, each data point will be compared to these bounds to identify any outliers. Since the exact data points within the IQR are not specified, I am unable to complete the calculation here. However, once you determine the IQR and the bounds, any data points below the lower bound or above the upper bound would be considered outliers.
It's also worth mentioning that if a data point is identified as an outlier, it doesn't automatically mean it should be removed from the dataset. Outliers may contain valuable information depending on the context. Deciding what to do with an outlier should be based on the research question and the nature of the data.
