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Análisis Integral de Datos

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Desafío de análisis de datos

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Mexico

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Análisis Integral de Datos
 

Análisis Integral de DatosOnline version

Desafío de análisis de datos

by Lizbeth Alvarez Enriquez
1

La regresión lineal modela la relación entre una variable dependiente y una o más variables independientes.

2

La validación cruzada se utiliza para evaluar la capacidad predictiva de un modelo.

3

La limpieza de datos es una etapa previa al análisis para evitar sesgos.

4

La estadística descriptiva resume datos con medidas como media, mediana y desviación típica.

5

El muestreo aleatorio simple asigna igual probabilidad a cada elemento de la población ser incluido en la muestra.

6

La reducción de dimensionalidad siempre mejora la interpretabilidad sin perder información.

7

La correlación implica causalidad.

8

La normalidad de los datos es un requisito obligatorio para aplicar cualquier prueba estadística.

9

El sesgo de selección mejora la generalización del modelo.

10

La outlier siempre debe retirarse de un dataset.

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