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Probit is a common transformation for linearising sigmoid distributions of proportions (Armitage and Berry, 1994). The probit is defined as 5 + the 1-p quantile from the standard normal distribution, where p is a proportion. For example, the number of insects killed by the log dose of an insecticide might describe a sigmoid relationship, which is a rectangular hyperbolic relationship to the non-log transformed dose. This sort of quantal response situation can be treated as a linear problem after probit transformation. Probit analysis uses these principles.
You can supply proportions or discrete data for logit transformation. If you specify discrete data then StatsDirect converts these to proportions by taking each value as a proportion of the maximum of the supplied data. The results are stored in a new column that is marked Probit:<name> where <name> is the original column label.
StatsDirect marks indeterminable values as missing data, i.e. p=0 or p=1. StatsDirect probit analysis, on the other hand, treats p=0 and p=1 in a more complex way that includes them in the overall analysis.