📊 Outlier Detection Tool (IQR Method)
Quickly find potential outliers in your bivariate data using the Interquartile Range (IQR) technique. Just enter your data and visualize the results!
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⚠️ Detected Outlier Pairs:
📈 Data Visualization
📚 Understanding Outlier Detection (IQR Method)
Outlier detection is a crucial step in data analysis to identify data points that significantly deviate from the rest of the dataset. The Interquartile Range (IQR) method is a robust technique for finding outliers. It's based on quartiles, which divide data into four parts. The IQR is the range between the first quartile (Q1 - 25th percentile) and the third quartile (Q3 - 75th percentile). Values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR are typically considered outliers. This tool helps you apply this method to bivariate data, visualizing potential outliers in a scatter plot for better understanding. Use it to clean your data and improve the accuracy of your analysis.
- IQR Formula: IQR = Q3 - Q1
- Lower Bound: Q1 - 1.5 * IQR
- Upper Bound: Q3 + 1.5 * IQR