5.9
Diagramas de caja modificados
Un diagrama de caja y bigotes estándar nos informa sobre la dispersión de los datos en una muestra determinada. Se puede identificar el valor mínimo,…
Recuerde que un conjunto de datos puede representarse mediante un resumen de cinco números y visualizarse mediante un diagrama de caja que tenga un valor mínimo, un primer cuartil, un segundo cuartil, un tercer cuartil y un valor máximo.
El diagrama de caja se modifica ligeramente para producir un diagrama de caja modificado que proporciona información más visual sobre la ubicación de los valores atípicos y la propagación de los datos desde el centro.
En primer lugar, reste 1,5 veces el IQR de Q1 y agregue 1,5 veces el IQR a Q3 para obtener los nuevos valores mínimo y máximo. Los valores que superan estos límites se consideran valores atípicos y se marcan con un asterisco.
Ahora modifique los bigotes para unir solo aquellos valores dentro del rango de 1.5 veces IQR.
Esto genera un diagrama de caja modificado con valores atípicos claramente identificados.
Q1: How do you calculate the adjusted minimum and maximum values for a modified boxplot?
The adjusted minimum equals Q1 minus 1.5 times the interquartile range (IQR). The adjusted maximum equals Q3 plus 1.5 times the IQR. These calculated values replace the standard minimum and maximum, creating boundaries that help identify outliers beyond the typical data spread.
Q2: What is the main difference between a standard boxplot and a modified boxplot?
A standard boxplot displays the five-number summary but doesn't identify outliers. A modified boxplot uses adjusted minimum and maximum values based on the IQR to clearly mark values beyond these limits as outliers with asterisks, providing better visual insight into data distribution and what are outliers.
Q3: How are whiskers modified in a modified boxplot compared to a standard boxplot?
In a modified boxplot, whiskers are shortened and repositioned to extend only to the adjusted minimum and maximum values calculated using the 1.5 times IQR formula. This contrasts with standard boxplots where whiskers extend to the actual minimum and maximum data values.
Q4: Why is the 1.5 times IQR rule used to identify outliers in modified boxplots?
The 1.5 times IQR rule establishes a consistent statistical boundary for detecting unusual values that deviate significantly from the central data cluster. Values falling outside this range are marked with asterisks, making outliers visually distinct and helping analysts recognize extreme or anomalous data points.
Q5: What information does a modified boxplot reveal that a standard boxplot does not?
A modified boxplot explicitly identifies and marks outliers with asterisks, showing which values lie far from the center. It also visualizes the actual spread of typical data by adjusting whisker positions, providing clearer insight into data distribution patterns and the presence of extreme values.
Q6: How do you determine if a data value is an outlier using the modified boxplot method?
A value is classified as an outlier if it falls below the adjusted minimum (Q1 minus 1.5 times IQR) or above the adjusted maximum (Q3 plus 1.5 times IQR). These outliers are plotted individually with asterisks rather than included within the whisker range.
Q7: What role does the interquartile range play in constructing a modified boxplot?
The interquartile range (IQR) is the foundation for calculating adjusted boundaries in a modified boxplot. Multiplying the IQR by 1.5 and adding or subtracting from Q3 and Q1 respectively determines the whisker endpoints and defines which values qualify as outliers.