Oracle Analytics Cloud (OAC) provides various ways to identify and analyze outliers in your data set. Outliers are data points that deviate significantly from the rest of the data and may indicate errors, anomalies, or opportunities. Two layers that you can use to determine which values of a given data set may provide the greatest opportunity due to their outlier qualities are:
Add an outlier visualization to your canvas. This will highlight data points that stand out and may justify further investigation. An outlier visualization is a type of scatter plot that automatically detects and labels outliers in your data using machine learning algorithms. You can use the outlier visualization to explore the characteristics and causes of the outliers and compare them with the normal data points.
Create a box plot visualization of your data set. A box plot is a type of graph that shows the distribution of a numerical variable using quartiles and whiskers. A box plot can also identify outliers as data points that lie beyond the whiskers or outside a certain range. You can use the box plot to see how the outliers affect the overall distribution and variation of your data.