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Análisis multivariado aplicado: regresión múltiple, componentes principales y agrupamiento.

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Análisis multivariado aplicado: regresión múltiple, componentes principales y agrupamiento.

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Sentencias sobre análisis multivariado.

Created by

Universidad FUCS
Universidad FUCS
Colombia

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Análisis multivariado aplicado: regresión múltiple, componentes principales y agrupamiento.
 

Análisis multivariado aplicado: regresión múltiple, componentes principales y agrupamiento.Online version

Sentencias sobre análisis multivariado.

by Universidad FUCS
1

La regresión múltiple modela la relación entre una variable dependiente y varios predictores.

2

La regresión múltiple asume que los predictores no están correlacionados entre sí.

3

El clustering jerárquico requiere especificar el número exacto de clusters al inicio.

4

El algoritmo k-means asigna cada observación al grupo cuyo centroide está más cercano.

5

El PCA garantiza que las dos primeras componentes explican siempre más del 90% de la varianza.

6

El PCA sintetiza grandes conjuntos de variables en componentes que capturan la mayor parte de la variabilidad de los datos.

7

La inercia dentro del cluster no tiene relación con la coherencia de los grupos.

8

La multicolinealidad puede afectar la estabilidad de los coeficientes estimados en regresión.

9

El índice de inflación de la varianza (VIF) mide la variabilidad de la variable dependiente.

10

Después de obtener clusters, se pueden evaluar su calidad usando métricas como la inercia y el coeficiente de silueta.

 
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