chriskeller1 said:
The interquartile range is calculated by finding the difference between the third quartile (Q3) and the first quartile (Q1). This can be represented by the formula IQR = Q3 - Q1.
An outlier is a data point that falls significantly outside the expected range of values for a dataset, often defined as being more than 1.5 times the interquartile range above the third quartile or below the first quartile.
A data point can be considered an outlier if it falls more than 1.5 times the interquartile range above the third quartile or below the first quartile. This can be determined by calculating the z-score of the data point and comparing it to a threshold of 3. If the z-score is greater than 3, the data point is considered an outlier.
Yes, the mean and median are measures of central tendency that can be used to identify outliers. A data point that is significantly higher or lower than the mean or median may be considered an outlier.
There is no one definitive answer for how to handle outliers in data. It ultimately depends on the specific dataset and the goals of the analysis. Some options for handling outliers include removing them from the dataset, transforming the data, or using robust statistical methods that are less affected by outliers.
