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## Main Question or Discussion Point

In

∑ (Y_i hat -Y bar)^2 ???

My textbook defines regression SS in the chapters for simple linear regression as ∑ (Y_i hat -Y bar)^2, and then in the chapters for multiple linear regression, the regression SS is defined in MATRIX form, and it did not say anywhere whether it is still equal to ∑ (Y_i hat -Y bar)^2 or not, so I am confused...

If it is still equal to ∑ (Y_i hat -Y bar)^2 in MULTIPLE linear regression (this is such a simple formula), what is the whole point of expressing the regression SS in terms of matrices in mutliple linear regression? I don't see any point of doing so when the formula ∑ (Y_i hat -Y bar)^2 is already so simple. There is no need to develop additional headaches...

Thanks for explaining!

__MULTIPLE__linear regression, is it still true that the regression sum of squares is equal to∑ (Y_i hat -Y bar)^2 ???

My textbook defines regression SS in the chapters for simple linear regression as ∑ (Y_i hat -Y bar)^2, and then in the chapters for multiple linear regression, the regression SS is defined in MATRIX form, and it did not say anywhere whether it is still equal to ∑ (Y_i hat -Y bar)^2 or not, so I am confused...

If it is still equal to ∑ (Y_i hat -Y bar)^2 in MULTIPLE linear regression (this is such a simple formula), what is the whole point of expressing the regression SS in terms of matrices in mutliple linear regression? I don't see any point of doing so when the formula ∑ (Y_i hat -Y bar)^2 is already so simple. There is no need to develop additional headaches...

Thanks for explaining!