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
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.
Based on the information provided, it seems like you are referring to different ways of defining outliers in statistical analysis. There are multiple approaches to identifying outliers, and they can vary depending on the context and the specific method used.
Type 1 outliers are defined as observations that are more than three standard deviations away from the mean. This method assumes that the data are normally distributed.
Type 2 moderate outliers are identified using the box plot definition. In this approach, an observation is considered a moderate outlier if it lies more than 1.5 times the interquartile range (IQR) away from the edges of the box in a box plot.
Type 2 extreme outliers are also identified using the box plot definition. In this case, an observation is considered an extreme outlier if it lies more than 3 times the IQR away from the edges of the box.
Without access to the specific data in the file "p02 31.xlsx," it is not possible to provide further analysis on the average monthly values of the variable of interest or identify any outliers using these methods.