| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
| 25.1 Linear Programming | ||
| 25.2 Quadratic Programming | ||
| 25.3 Nonlinear Programming | ||
| 25.4 Linear Least Squares |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
| [ < ] | [ > ] | [ << ] | [ Up ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
Each row of y and x is an observation and each column a variable. The return values beta, v, and r are defined as follows.
Each row of y and x is an observation and each column a variable.
The return values beta, sigma, and r are defined as follows.
beta = pinv (x) *
y, where pinv (x) denotes the pseudoinverse of
x.
sigma = (y-x*beta)' * (y-x*beta) / (t-rank(x)) |
r = y - x *
beta.
| [ << ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |