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when severe autocorrelation is indicated after a regression model has been estimated, which underlying regression assumption is violated?

Sagot :

The fundamental regression assumption that the error terms are independent of one another is broken when substantial autocorrelation is shown following the estimation of a regression model. Here option C is the correct answer.

The two error factors must be somewhat co-correlated in order for there to be autocorrelation. Having it indicates that the correlation between the two error factors is not equal to zero. It goes against the fundamental tenet of the traditional linear regression approach.

Autocorrelation measures the relationship between a variable's current value and its historical values. A perfect positive correlation is represented by an autocorrelation of 1, whereas a perfect negative correlation is represented by an autocorrelation of 1.

An explanation of how one or more independent variables relate to the target variable, response variable, or dependent variable that the regression model predicts.

Complete question:

When severe autocorrelation is indicated after a regression model has been estimated, which underlying regression assumption is violated? A - The dispersion of population data points around the population regression line remains constant everywhere along the line

B - The population of values is normally distributed about the population regression line.

C - Heteroscedasticity

D - The error terms are independent of each other.

E - A linear relationship exists between X and Y in the population

To learn more about autocorrelation

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