There is no consistent way of defining an outlier that everyone agrees upon. For example, some people refer to an outlier that is any observation more than three standard deviations from the mean (type 1. ) other people use the box plot definition, where an outlier (moderate or extreme) is any observation more than 1. 5 iqr from the edges of the box (type 2 moderate), and some people care only about the extreme box plot-type outliers, those that are 3. 0 iqr from the edges of the box (type 2 extreme. ) the file p02 31. Xlsx contains average monthly values of the s

Answer 1

tock market index for a specific company over a period of several years.

To determine the outliers in this data, one approach could be to use the box plot definition.

First, calculate the interquartile range (IQR) by subtracting the 25th percentile (Q1) from the 75th percentile (Q3).

Next, calculate the upper and lower fences by multiplying the IQR by 1.5 and adding/subtracting them from Q3 and Q1 respectively.

Any data point outside of these fences would be considered a moderate outlier. If you want to consider only extreme outliers, multiply the IQR by 3 instead of 1.5.

Using this method, you can identify the outliers in the dataset and analyze their significance based on the type of outliers you decided to consider.

It's important to note that the choice of defining outliers can vary depending on the specific context and purpose of the analysis. Different types of outliers may be relevant in different situations.