The general linear model considers the situation when the response variable is not a scalar (for each observation) but a vector, yi. Conditional linearity of E ( y ∣ x i ) = x i T B {\displaystyle E(\mathbf {y} \mid \mathbf {x} _{i})=\mathbf {x} _{i}^{\mathsf {T}}B} is still assumed, with a matrix B replacing the vector β of the classical linear regression model. Multivariate analogues of ordinary least squares (OLS) and generalized least squares (GLS) have been developed.