What leads to unreliable results in multiple linear regression?

Unreliable results in multiple linear regression can arise from various factors that compromise the model's integrity and predictive capability. When considering the aspects presented, it is essential to understand how each contributes to unreliability.

Excluding a key predictor can significantly affect the model's performance, leading to biased estimates and an incomplete understanding of the relationships among the variables. A key predictor is one that has a strong influence on the dependent variable; omitting it can skew the results and overlook critical dynamics within the data.

Including as many predictors as possible without consideration for their relevance or contribution can lead to overfitting, where the model becomes overly complicated and captures the noise in the data rather than the underlying trend. While having a rich set of predictors may seem beneficial, unnecessary variables can inflate the variance of the coefficient estimates.

High multicollinearity among predictors poses another critical issue, as it leads to inflated standard errors for the coefficients, making it difficult to ascertain the individual effect of each predictor on the dependent variable. This can result in high variability in the model, undermining its effectiveness and interpretability.

Thus, the choice that encompasses all these problematic factors—excluding a key predictor, including excessive or irrelevant predictors, and dealing with high multicollinearity—accur