A d x d matrix, where d is the number of predictors plus one. The variance of each coefficient is in the diagonal cell and the covariance of each pair of coefficients is in the appropriate off-diagonal cell. The variance is the standard error of the coefficient squared.
The variance-covariance matrix is from the final iteration of the inverse of the information matrix. The variance-covariance matrix has the following form:
W is a diagonal matrix where the diagonal elements are given by the following formula:
This variance-covariance matrix is based on the observed Hessian matrix as opposed to the Fisher's information matrix. Minitab uses the observed Hessian matrix because the model that results is more robust against any conditional mean misspecification.
If the canonical link is used then the observed Hessian matrix and the Fisher's information matrix are identical.
| yi ||the response value for the ith row|
|the estimated mean response for the ith row|
|V(·)||the variance function given in the table below|
|g(·)||the link function|
|V '(·)||the first derivative of the variance function|
|g'(·)||the first derivative of the link function|
|g''(·)||the second derivative of the link function|
The variance function depends on the model:
See  and  for more information.
 A. Agresti (1990). Categorical Data Analysis. John Wiley & Sons, Inc.
 P. McCullagh and J.A. Nelder (1992). Generalized Linear Model. Chapman & Hall.