Criterion.Analysis

Outliers from sample data, calculated using the boxplot technique.

A description of the extent to which outliers in the sample data affect the sample mean and standard deviation.

Analysis of the extent to which outliers in a sample affect its standard deviation (and to some extent, its mean).

Result of a bootstrap analysis of a non-parametric sample.

Perform an analysis of a measurement.

Multiply the Estimates in an analysis by the given value, using scale.

Display the mean of a Sample, and characterise the outliers present in the sample.

Count the total number of outliers in a sample.

Classify outliers in a data set, using the boxplot technique.

Display a report of the Outliers present in a Sample.

Compute the extent to which outliers in the sample data affect the sample mean and standard deviation.

Given a list of accessor names (see measureKeys), return either a mapping from accessor name to function or an error message if any names are wrong.

Given predictor and responder names, do some basic validation, then hand back the relevant accessors.

Regress the given predictors against the responder.

Errors may be returned under various circumstances, such as invalid names or lack of needed data.

See olsRegress for details of the regression performed.