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Trivia de Residuos, Durbin‑Watson, multicolinealidad, R², Pearson.

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Trivia de Residuos, Durbin‑Watson, multicolinealidad, R², Pearson.

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Quiz sobre residuos, Durbin-Watson y más

Created by

Romano Morles
Romano Morles
Venezuela

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Trivia de Residuos, Durbin‑Watson, multicolinealidad, R², Pearson.
 

Trivia de Residuos, Durbin‑Watson, multicolinealidad, R², Pearson.Online version

Quiz sobre residuos, Durbin-Watson y más

by Romano Morles
1

¿Qué mide la R² en un modelo de regresión?

2

¿Qué son los residuos en un modelo de regresión?

3

¿Qué indica la multicolinealidad en un modelo?

4

¿Qué es el estadístico Durbin–Watson?

5

¿Qué indica un Durbin–Watson cercano a 2?

6

¿Qué significa Pearon en este contexto?

7

¿Qué valor de R² indica mejor ajuste?

8

¿Qué herramienta usar para detectar multicolinealidad?

9

¿Qué situación describe mejor residuos aleatorios?

10

¿Qué relación tiene R² y Pearson en un modelo lineal simple?

Explicación

La R² indica qué tanto de la variabilidad de la Y se explica por el modelo.

Los residuos = Y observada - Ŷ (valor predicho).

La multicolinealidad genera incertidumbre en estimaciones de coeficientes.

DW evalúa si existe autocorrelación en los residuos.},{

DW≈2 sugiere ausencia de autocorrelación significativa.

Pearson r mide la correlación lineal entre dos variables.

Un R² alto sugiere mayor proporción de varianza explicada.

VIF evalúa cuánto inflan las varianzas de coeficientes por colinealidad.

Residuos aleatorios indican un buen modelo.

En regresión simple, R² = r², donde r es Pearson.

 
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