The purpose of sorting objects into percentile groups is to classify and group data based on their relative position within a larger dataset. This allows for a better understanding and analysis of the data, as well as identifying patterns and outliers.
The percentile group for an object is determined by comparing its value to the values of all the other objects in the dataset. The object's percentile is then calculated by dividing the number of values below it by the total number of values in the dataset and multiplying by 100.
Percentile groups and quartiles are both methods of dividing a dataset into groups based on their relative position. However, percentile groups divide the data into 100 equal groups, whereas quartiles divide the data into four equal groups.
Using percentile groups allows for a more accurate representation of the data and eliminates the impact of extreme values. It also provides a better understanding of the distribution of the data and can help identify any potential outliers.
Sorting objects into percentile groups can be useful in a variety of fields, such as finance, education, and healthcare. It can help with market analysis, identifying student performance, and tracking health indicators, among others.