# Diagnostic Tests and Robustness Checks

Revisit diagnostic tests and robustness checks.

As noted in the previous lesson, whether the estimation and inference results are valid depends on whether the Gauss-Markov conditions are satisfied or not. Therefore, it’s important that we double-check whether the results in Braithwaite are based on violated assumptions and whether they’re sensitive to robustness checks.

Following the example in the previous chapter, we first use the `augment_columns()`

function in the `broom`

package to add various diagnostic statistics as new variables to the original data `mid`

and then create a new diagnostic dataset `mid.v2`

.

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