Which about Mallow's Cp is correct?

Mallow's Cp is a statistical measure used for model selection and to evaluate the goodness-of-fit of regression models. The characteristic of Mallow's Cp that stands out is its ability to provide an unbiased estimate of the test mean squared error (MSE) when applied appropriately. This means that Mallow's Cp not only assesses the goodness-of-fit but does so while taking into account the number of predictors in the model; hence, it helps in comparing models of different complexities.

In relation to the Akaike Information Criterion (AIC), while both Mallow's Cp and AIC are used for model selection and evaluation, they operate under different principles. Mallow’s Cp specifically aims to identify the best model by evaluating how well the model predicts new data, where it tends to add a penalty for the number of predictors. AIC shares a similar philosophy of balancing goodness-of-fit with model complexity, although it does this in its own formula. The relationship between Mallow’s Cp and AIC reveals that both are contrastingly beneficial for similar purposes.

Finally, interpreting the magnitude of Mallow's Cp is crucial; a value significantly lower than the number of predictors (or close to the number of predictors plus the constant) suggests that the model has an