Calculadora de Detección de Valores Atípicos Gratis
Detecta valores atípicos usando el método IQR o puntaje Z. Identifica datos inusuales en tu conjunto.
IQR
40.0000
IQR vs Value to Test
Fórmula
## 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.
Ejemplo Resuelto
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
Preguntas Frecuentes
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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