Data Preparation

Model your data as a series of process observations or measures that are associated with an outcome of interest.  Compose each observation (row) as a common set of features (aka., independent variables or factors).  Both features and outcomes are either numerical (blood pressures, HbA1c, ages, etc.), binary (yes/no, true/false, etc.) or categorical (Gender, Service line , Floor unit, Shift, DRG, etc.).

Provide a CSV file with a header row labeling each column.

1. The first column is an alphanumeric index starting with a letter that is used to uniquely identify the data row.
2. The second column is an alphnumeric label for the group of rows the data belongs to.  Use this if you want to look for anomalies within groups of rows as opposed to across all rows.   If there are no groupings then just label all the values in the 'GROUP' column the same.
3. Provide additional columns of either categorical data or numeric data.
4. The first row is a user supplied alphanumeric header for each column

 Sample Table: