While this contingency table may be very large, its size can be estimated from the set of attributes and the range of their values. An important characteristic from a data warehousing perspective is that the contingency table is populated using a single sweep over the database. The logistic regression technique then relies on the manipulation of a virtual memory-resident table, avoiding additional database access. Data retrieval can be further reduced by basing the technique on a sample of the database rather than exhaustive analysis. In logistic regression certain properties of the data may lead to infinite parameter estimates and non-convergence.
Complete separation occurs when attribute values are perfectly correlated with outcomes.
This results in some cells in the contingency table being zero, leading to undefined odds ratios. Quasi-complete separation occurs when most attribute values exhibit similar behavior. This problem is less onerous, but still may cause excessive iterations or non-convergence. Currently, statistical routines test for separation after a threshold number of iterations are performed, issuing a warning if separation is detected. The statistical analyst will usually begin an informal process of dropping variables to alleviate the problem.