Consider the simple linear regression model, y = β0 + β1x + u. Which of the following statements is correct?

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BlackTom AI · Accepted Answer

The prompt asks us to consider the simple linear regression model y = β0 + β1x + u and to identify which statement is correct.

First, note that the provided data shows an answer string but there are no actual answer options listed in the question payload. Without a set of options to evaluate, we cannot systematically compare multiple candidate statements or rule out alternatives. This absence is important because, in multiple-choice format, correctness depends on how each option stacks up against the model assumptions.

Despite the missing options, we can still reflect on the content of the provided answer: 'SLR.4, the fourth assumption of the simple linear regression model, is about the zero conditional mean of the error term u.' In classic ordinary least squares (OLS) regression theory, the standard SLR assumptions are often labeled SLR.1, SLR.2, SLR.3, SLR.4. The SLR.4 assumption is typically stated as E[u|X] = 0, meaning the expected value of the error term conditional on the regressor X is zero. This implies that the error term has zero mean and is uncorrelated with the regressor, which is crucial for unbiased and consistent estimation of β0 and β1 via OLS.

Assessing this statement alone (assuming it is intended as the correct option):
- Correctness: It correctly identifies the essence of the fourth assumption in many presentations of SLR assumptions. E[u|X] = 0 is commonly described as the zero conditional mean assumption, and it underpins the unbiasedness of the OLS estimators under the classical projection properties.
- Potential caveats: In some curricula, the labels SLR.1 through SLR.4 may be defined slightly differently, or the wording might emphasize E[u|X] = 0 as the key component of exogeneity or as part of Gauss–Markov assumptions. If the course uses a different labeling or splits exogeneity into separate conditions, there could be subtle differences in interpretation. However, the core idea—that the conditional mean of the error given X is zero—is consistent with the standard zero conditional mean assumption.

If we were to evaluate other, non-existent options, we would examine each for issues such as:
- Violations of the zero conditional mean leading to endogeneity and biased OLS estimates.
- Misstating that errors are purely homoscedastic or uncorrelated with any predictor, which would be a separate assumption (e.g., homoscedasticity or no autocorrelation) and not the zero conditional mean.
- Claiming that E[u] = 0 alone suffices without conditioning on X, which is weaker than the zero conditional mean and does not guarantee exogeneity when X is random.

In summary, given the standard interpretation, the statement about SLR.4 describing the zero conditional mean of the error term u is correct; but due to the missing answer options in this item, a definitive selection cannot be demonstrated from the provided list. If your instructor uses the conventional labeling where SLR.4 corresponds to E[u|X] = 0, then this statement aligns with that core exogeneity condition.

AS.440.606.80.SP25 M03: Assignment

Consider the simple linear regression model, y = β0 + β1x + u. Which of the following statements is correct?

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