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1.5 IQR Rule | Statistical Methods in Anomaly Detection
Data Anomaly Detection
course content

Course Content

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
2. Statistical Methods in Anomaly Detection
3. Machine Learning Techniques

book1.5 IQR Rule

The 1.5 IQR (Interquartile Range) rule is a simple but effective method for identifying outliers in a dataset. It's based on the spread of data around the median and is commonly used in anomaly detection.

How to use 1.5 IQR rule

  1. Calculate the IQR, which is the range between the 75th percentile (Q3) and the 25th percentile (Q1) of the dataset;
  2. Define the lower threshold as Q1 - 1.5 * IQR and the upper threshold as Q3 + 1.5 * IQR;
  3. Any data point below the lower threshold or above the upper threshold is considered an outlier.

Here is the implementation of this rule:

We simply calculate threshold values and condenser all points out of IQR range as outliers.

1.5 IQR rule for commonly used distributions

Pros and cons of using 1.5 IQR rule

What does an outlier represent in the context of the 1.5 IQR rule?

What does an outlier represent in the context of the 1.5 IQR rule?

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Section 2. Chapter 3
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