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Anscombe y grafismos: decisiones visuales

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Anscombe y grafismos: decisiones visuales

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Explora decisiones en gráficos con ejemplos

Created by

Universidad FUCS
Universidad FUCS
Colombia

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Anscombe y grafismos: decisiones visuales
 

Anscombe y grafismos: decisiones visualesOnline version

Explora decisiones en gráficos con ejemplos

by Universidad FUCS
1

La codificación redundante, usar color y forma, ayuda a que las figuras sean legibles para personas con deficiencias en color.

2

Truncar el eje vertical de una gráfica de barras nunca es problemático.

3

La paleta viridis no es adecuada para valores que crecen en un solo sentido.

4

En Seaborn no hay diferencias entre funciones de nivel de ejes y de nivel de figura.

5

La jerarquía de precisión perceptual de Cleveland y McGill sitúa la posición en una escala común como canal más preciso.

6

Seaborn ofrece una interfaz declarativa que asigna variables de un DataFrame a atributos visuales.

7

Matplotlib es la capa base sobre la que se apoya prácticamente todo el ecosistema de visualización en Python.

8

El mapa arcoíris es siempre la mejor opción para datos con más de tres categorías.

9

El cuarteto de Anscombe muestra que cuatro conjuntos pueden tener la misma media, desviación y correlación, pero figuras distintas.

10

Seaborn reemplaza a Matplotlib y no requiere código de bajo nivel.

 
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