Interquartile Range and Outliers

Interquartile Range and Outliers

• Interquartile Range (IQR) and Outliers are crucial for understanding data distribution.
• IQR represents the spread of the middle 50% of data points, calculated by subtracting lower quartile (25th percentile) from upper quartile (75th percentile).
• IQR is useful in analysing data variability as it’s less impacted by extreme values.
• Outliers are unusually high or low data points that significantly deviate from majority of data.
• Outliers may provide insights into rare occurrences or suggest errors in measurement or data entry.
• One method to detect outliers is the 1.5x IQR rule; any data point falling below Q1 - 1.5x IQR or above Q3 + 1.5x IQR is an outlier.
• Understanding IQR and identifying outliers are crucial for accurately interpreting data distribution and characteristics.