Outlier Detection Calculator
Detect potential outliers using the IQR method by checking if a value falls outside the inner fences (Q1 - 1.5*IQR, Q3 + 1.5*IQR).
IQR
40.0000
IQR vs Value to Test
सूत्र
## How to Detect Outliers Using the IQR Method ### Formula **IQR = Q3 - Q1** **Lower Fence = Q1 - 1.5 * IQR** **Upper Fence = Q3 + 1.5 * IQR** Values below the lower fence or above the upper fence are flagged as potential outliers. The 1.5 multiplier is Tukey's convention. Using 3.0 instead identifies extreme outliers.
हल किया गया उदाहरण
Q1 = 30, Q3 = 70. Is 95 an outlier?
- 01IQR = 70 - 30 = 40
- 02Lower fence = 30 - 1.5*40 = 30 - 60 = -30
- 03Upper fence = 70 + 1.5*40 = 70 + 60 = 130
- 0495 is between -30 and 130, so it is NOT an outlier
- 05It would need to exceed 130 to be flagged
अक्सर पूछे जाने वाले प्रश्न
Why use 1.5 times the IQR?
John Tukey chose 1.5 because, for a normal distribution, this captures about 99.3% of the data, flagging only the most extreme 0.7%. It provides a good balance between identifying true outliers and avoiding false flags.
Are outliers always errors?
No. Outliers may be data entry errors, measurement errors, or genuine extreme observations. Investigate each case before removing. In some fields (fraud detection, rare events), outliers are the most important data points.
What are alternatives to the IQR method?
Z-score method (flag |z| > 2 or 3), Grubbs' test, Dixon's Q test, and the modified Z-score using the median absolute deviation (MAD). The IQR method is robust because Q1 and Q3 are resistant to outliers themselves.
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