Which of the following leads to unreliable results from a multiple linear regression?

In the context of multiple linear regression, each of the factors listed can contribute to unreliable results in different ways.

Excluding a key predictor can lead to a specification error, which occurs when an important variable that influences the dependent variable is omitted from the model. This can result in biased estimates of the coefficients for the included predictors, leading to incorrect conclusions about their relationships with the outcome variable.

Including too many predictors, especially those that may not have a theoretical or practical justification for their inclusion, can introduce noise into the model. This can lead to overfitting, where the model fits the training data rather well but performs poorly on unseen data. As a result, this compromises the model's ability to accurately predict outcomes and generalize to other contexts.

Errors in the data not following a normal distribution can affect the validity of hypothesis tests and confidence intervals derived from the regression model. While the Central Limit Theorem often mitigates this for large sample sizes, significant departures from normality, especially in smaller samples, can lead to unreliable inference.

Each of these issues can independently result in unreliable or misleading outcomes from the regression analysis, indicating that all of them collectively contribute to compromised results in multiple linear regression. Thus, recognizing how each aspect can affect the analysis underscores