(Machine Learning) Serious answer only please, better with explanation, thanks !

Linear regression has the form Elyx wowTx. It is common to include a column of 1's in the design matrix, so we can solve for the offset wo and the other parameters w at the same time using the normal equations. However, it is also possible to solve for w and wo separately. Let (W,Wo) be the optimizer. Show that So no models the difference in the average output from the average predicted output. Also, show that (yi -y)(Xi -X), i=1 i=1 where is the centered input matrix containing X, Xi-i along its rows, and yc-y- is the center output vector. Thus we can first compute w on centered data, and then estimate wo using Linear regression has the form Elyx wowTx. It is common to include a column of 1's in the design matrix, so we can solve for the offset wo and the other parameters w at the same time using the normal equations. However, it is also possible to solve for w and wo separately. Let (W,Wo) be the optimizer. Show that So no models the difference in the average output from the average predicted output. Also, show that (yi -y)(Xi -X), i=1 i=1 where is the centered input matrix containing X, Xi-i along its rows, and yc-y- is the center output vector. Thus we can first compute w on centered data, and then estimate wo using