PM01-0217. Detección de outliers en cuartiles

Limpieza y validación de datos

Los "outliers" o valores atípicos son observaciones que se encuentran significativamente alejadas del resto de los datos en un conjunto.

[{"selector":"#anim-2ce84889-0661-40f7-91b8-af9d603da173","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-544ddb68-f982-45b1-b2b8-d5b1b9a0ba2e","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-676e1c5f-ee22-4119-b6de-87c0b1947b6f","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-de9ab804-a731-4f23-b53e-07ac6a5f3e00","keyframes":{"transform":["translate3d(-114.8265%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Son atípicos porque difieren en gran medida de la mayoría de las observaciones y pueden tener un impacto desproporcionado en los resultados de un análisis.

[{"selector":"#anim-127a4f49-df15-4d22-9896-ed35dc55363c","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-9c84600c-942b-4157-ac55-698f3fd2a8aa","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Un método común para detectar outliers es el uso de cuartiles.

[{"selector":"#anim-6776fbb8-6043-4a60-832c-71bf6917fb2c","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-51120c4d-e470-418d-ae0b-b93f5a2f8d33","keyframes":{"transform":["translate3d(118.95425%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Aprende más al respecto:

[{"selector":"#anim-b3a030b5-9f90-4dd2-a3a5-0d7bfddf1552","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-215923ba-b433-4fdf-b808-4c0bfff0461e","keyframes":{"transform":["translate3d(-114.8265%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-97b6cd52-be4f-4fd5-9496-5a5bfc816101","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-a9eaddcf-b01d-42dd-ad01-988b6de0d7c1","keyframes":{"transform":["translate3d(-386.51686%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-474d521b-3d8b-4d2e-9bcf-c250fb35368a","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.4, 0.4, 0.0, 1)","fill":"both"}] [{"selector":"#anim-31b0de92-a0cc-42df-b72a-009222afbdb5","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-4f00e71f-525c-4de5-9d24-a08bdeadce2d","keyframes":{"transform":["translate3d(100.00000%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] Aplica Burst with Arrow

[{"selector":"#anim-4d12c5f2-6eae-4160-87fc-4cd581db6066","keyframes":[{"offset":0,"transform":"translate3d(0, -166.2263%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.29,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.45,"transform":"translate3d(0, -46.74283556%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.61,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.71,"transform":"translate3d(0, -15.89123428%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.8,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.85,"transform":"translate3d(0, -5.967524170000001%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":0.92,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"},{"offset":0.96,"transform":"translate3d(0, -2.59313028%, 0)","easing":"cubic-bezier(.5, 0, 1, 1)"},{"offset":1,"transform":"translate3d(0, 0%, 0)","easing":"cubic-bezier(0, 0, .5, 1)"}],"delay":0,"duration":1400,"fill":"both"}]

Limpieza y validación de datos

Detección de outliers en cuartiles

Los "outliers" o valores atípicos son observaciones que se encuentran significativamente alejadas del resto de los datos en un conjunto.

Outliers

Son atípicos porque difieren en gran medida de la mayoría de las observaciones y pueden tener un impacto desproporcionado en los resultados de un análisis.

Un método común para detectar outliers es el uso de cuartiles.

Aprende más al respecto: