Karen is using a linear regression model for her research. During her analysis, she suspects that the error terms in her model might be correlated, which could violate an important assumption. Which…

The correct answer is B. Durbin-Watson test. The Durbin-Watson test is specifically designed to detect autocorrelation (correlation) in the residuals (error terms) of a regression model. Linear regression assumes that error terms are independent and uncorrelated; violation of this assumption, such as correlated errors…

Mathematics and Statistics

Question

Karen is using a linear regression model for her research. During her analysis, she suspects that the error terms in her model might be correlated, which could violate an important assumption. Which of these tests should Karen use to check this assumption?

Options

AShapiro-Wilk test
BDurbin-Watson test
CPearson correlation test
DChi-square test

How the community answered

(29 responses)

A
3% (1)
B
72% (21)
C
17% (5)
D
7% (2)

Explanation

The Durbin-Watson test is specifically designed to detect autocorrelation (correlation) in the residuals (error terms) of a regression model. Linear regression assumes that error terms are independent and uncorrelated; violation of this assumption, such as correlated errors, affects the validity of the model. The Durbin-Watson statistic ranges from 0 to 4, where a value around 2 indicates no autocorrelation, values less than 2 indicate positive autocorrelation, and values greater than 2 indicate negative autocorrelation.

Topics

#Durbin-Watson test#autocorrelation#linear regression assumptions#error terms

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