In the previous video we have seen examples of how different types of errors propagate into the estimated parameters. In this video we will show how the error propagation laws can be used for this purpose.

Note that the propagation laws presented in this video have been applied already in the previous modules, specifically when deriving the properties of weighted least squares and best linear unbiased estimation, specifically the unbiased (\(E\{\underline{\hat{x}}\}=x\)) property and the precision (\(Q_{\hat{x}\hat{x}}\)).

We do repeat them here in order to get a better appreciation of why these properties could be derived, and also to allow you to study this video as a stand-alone source to learn about the propagation laws.