Parameter Estimates
Estimate
The maximum likelihood estimate
is the value of
that maximizes the likelihood function over a valid range of parameter values. The computation of the maximum likelihood estimate requires an iterative computational procedure. The Fisher scoring algorithm is used, and the parameter estimates are the values obtained from the last iteration of the algorithm. For more information, see
Overview of Logistic Regression Model.
Standard Error
The standard error estimates are the square roots of the diagonals of the estimated variance-covariance matrix. For more information, see Estimated Variance-Covariance Matrix.