An outliner (or "outline processor") is a specialized type of word processor used to view, create, build, modify, and maintain outlines. It is a computer program, or part of one, used for displaying, organizing, and editing hierarchically arranged text in an outline's tree structure . Textual information is contained in discrete sections called "nodes", which are arranged according to their topic-subtopic (parent-child) relationships, sort of like the members of a family tree . When loaded into an outliner, an outline may be collapsed or expanded to display as few or as many levels as desired.

Using SPSS, go to the ANALYZE menu, select REGRESSION, then select BINARY LOGISTIC. Your dependent variable will be the hospital to which the patient was admitted. Covariates are the variables such education, severity of illness and insurance that you want to control. For variables that are categorical, ., insurance, which could be private, public (. MediCal if it hasn’t disappeared in the latest round of state budget cuts) and none, click on the CATEGORICAL button and move those over to the “Categorical covariate” window.

A basic question faced at the outset of analyzing a large set of testing results is whether there is evidence that any of the alternative hypotheses are true. [* citation needed * ] One simple meta-test that can be applied when it is assumed that the tests are independent of each other is to use the Poisson distribution as a model for the number of significant results at a given level α that would be found when all null hypotheses are true. [* citation needed * ] If the observed number of positives is substantially greater than what should be expected, this suggests that there are likely to be some true positives among the significant results. [* citation needed * ] For example, if 1000 independent tests are performed, each at level α = , we expect 50 significant tests to occur when all null hypotheses are true. [* citation needed * ] Based on the Poisson distribution with mean 50, the probability of observing more than 61 significant tests is less than , so if more than 61 significant results are observed, it is very likely that some of them correspond to situations where the alternative hypothesis holds. [* citation needed * ] A drawback of this approach is that it over-states the evidence that some of the alternative hypotheses are true when the test statistics are positively correlated, which commonly occurs in practice. [* citation needed * ] . On the other hand, the approach remains valid even in the presence of correlation among the test statistics, as long as the Poisson distribution can be shown to provide a good approximation for the number of significant results. This scenario arises, for instance, when mining significant frequent itemsets from transactional datasets. Furthermore, a careful two stage analysis can bound the FDR at a pre-specified level. [15]