MSE tables [Software]
Hi Angus,
Indeed.
In matrix terms we use
y is the vector of log-transformed metrics (logged observations).
X is the design matrix.
b is the effect vector (a vector of all the constants mentioned above with the redundant ones removed)
e is a vector of error.
Accordingly, max. likelihood is when ete is minimised which gives us a useful
❝ ELmaestro: Thank you for your comments and encouragement. It seems that we have multiple linear regression used in conjunction with matrix alegebra....yes?
❝
❝ Would that be the case?
Indeed.
In matrix terms we use
y=Xb+ey is the vector of log-transformed metrics (logged observations).
X is the design matrix.
b is the effect vector (a vector of all the constants mentioned above with the redundant ones removed)
e is a vector of error.
Accordingly, max. likelihood is when ete is minimised which gives us a useful
b-vector.—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- Phoenix WinNonlin ANOVA AngusMcLean 2014-02-24 00:07
- Intercept Helmut 2014-02-24 00:51
- Intercept AngusMcLean 2014-02-24 15:08
- MSE tables Helmut 2014-02-24 15:58
- MSE tables AngusMcLean 2014-02-24 16:56
- MSE tables ElMaestro 2014-02-24 17:26
- MSE tables AngusMcLean 2014-02-24 18:02
- MSE tablesElMaestro 2014-02-24 18:17
- MSE tables AngusMcLean 2014-02-24 19:56
- MSE tables ElMaestro 2014-02-24 23:58
- MSE tables AngusMcLean 2014-02-25 01:25
- MSE tables ElMaestro 2014-02-24 23:58
- MSE tables AngusMcLean 2014-02-24 19:56
- MSE tablesElMaestro 2014-02-24 18:17
- MSE tables AngusMcLean 2014-02-24 18:02
- MSE tables ElMaestro 2014-02-24 17:26
- MSE tables AngusMcLean 2014-02-24 16:56
- MSE tables Helmut 2014-02-24 15:58
- Intercept AngusMcLean 2014-02-24 15:08
- Intercept Helmut 2014-02-24 00:51
