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Basic Question 0 of 11
Some of the following assumptions of the linear regression model are not satisfied when we work with time series. They are:
II. Independence of the errors (no serial correlation).
III. Homoscedasticity (constant variance) of the errors.
IV. Normality of the error distribution.
I. Linearity of the relationship between dependent and independent variables.
II. Independence of the errors (no serial correlation).
III. Homoscedasticity (constant variance) of the errors.
IV. Normality of the error distribution.
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I am happy to say that I passed! Your study notes certainly helped prepare me for what was the most difficult exam I had ever taken.
Andrea Schildbach
Subject 1. Trend Models