Please help. I don't understand multiple linear regression.

For any multiple linear regression model, the total sum of squares can be decomposed into the sum of squares contributed solely by the predictor vector and the sum of squares contributed solely by the residual vector. To assess the importance of a set of the regressors, they can be taken jointly by regressing the response variable on this set of regressors, either including or excluding other regressors. a) Discuss, using mathematical arguments in vector or matrix expressions, how this set of regressors can be assessed by use of the sums of squares of the residual vectors. b) Will including or excluding other regressors make a difference? Why or why not? Provide proof for your answers