In regression analysis,the existence of a significant pattern in successive values of the error term constitutes:
A) heteroscedasticity
B) autocorrelation
C) multicollinearity
D) nonlinearities
E) a simultaneous equation relationship

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Autocorrelation occurs when there is a correlation between successive error terms in a regression model. This violates the assumption that the error terms are independent and can lead to biased and unreliable estimates of the regression coefficients. Heteroscedasticity refers to a situation where the variance of the error terms is not constant across the range of the independent variable(s). Multicollinearity refers to a high degree of correlation between independent variables, which can also lead to unreliable estimates of the regression coefficients. Nonlinearities refer to situations where the relationship between the independent and dependent variables is not linear. A simultaneous equation relationship occurs when there are feedback loops between the dependent and independent variables, which can lead to endogeneity and biased estimates.

In Regression Analysis,the Existence of a Significant Pattern in Successive