Outliers
What are outliers?
The outliers are those points in the dataset that are very far from other data points. These could be very high or very low.
How can outliers be detected?
The Z-score, Interquartile Range (IQR), and visualization tools like scatter plots and box plots are useful for detecting outliers.
How can outliers be handled?
Outliers can be handled by removing them, transforming data, or using robust statistical methods that minimize their impact.
What is the main purpose of outliers?
The main purpose of identifying outliers is to highlight unusual data that may indicate variability, errors, or unique phenomena.
What is the main purpose of outliers?
The main purpose of identifying outliers is to highlight unusual data that may indicate variability, errors, or unique phenomena.
Outlier
Outliers stand for data points that are indicative of a much higher variability than other observations in a given dataset. This can result in skewing statistical studies and wrong conclusions after all the variables are not adequately identified and handled. Identifications of outliers are very relevant for the financial sector, healthcare industry and decision-making processes that depend on data analysis.
In this article, we will learn in detail about outlier, its definition, examples, types, how to find outlier, their uses and how they are different of inliers.