The linear multiple regression model can be represented in matrix notation as $\boldsymbol { Y } = \boldsymbol { X } \boldsymbol { \beta } + \boldsymbol { U }$ where X is of order n x(k+1) . k represents the number of
A) regressors.
B) observations.
C) regressors excluding the "constant" regressor for the intercept.
D) unknown regression coefficients

Question

The linear multiple regression model can be represented in matrix notation as $\boldsymbol { Y } = \boldsymbol { X } \boldsymbol { \beta } + \boldsymbol { U }$ where  X  is of order  n x(k+1) . k  represents the number of
A) regressors.
B) observations.
C) regressors excluding the "constant" regressor for the intercept.
D) unknown regression coefficients

Quizplus · Accepted Answer

In the matrix notation of a linear regression model, $X$ is an $n \times (k+1)$ matrix where $k$ represents the number of regressors excluding the constant term for the intercept. The "+1" accounts for the intercept term.

The Linear Multiple Regression Model Can Be Represented in Matrix Y=Xβ+U\boldsymbol { Y } = \boldsymbol { X } \boldsymbol { \beta } + \boldsymbol { U }Y=Xβ+U

The Linear Multiple Regression Model Can Be Represented in Matrix $\boldsymbol { Y } = \boldsymbol { X } \boldsymbol { \beta } + \boldsymbol { U }$