Multiple linear regression analysis using ordinary least squares.
| | Name | Type | Identifier | Description | Constraints |
| Input | Shapes | Shapes (input) | TABLE | - | - |
| Output | Results (*) | Shapes (optional output) | RESULTS | - | - |
| Details: Coefficients (*) | Table (optional output) | INFO_COEFF | - | - |
| Details: Model (*) | Table (optional output) | INFO_MODEL | - | - |
| Details: Steps (*) | Table (optional output) | INFO_STEPS | - | - |
| Options | Dependent Variable | Table field | DEPENDENT | - | - |
| Independent Variables | Parameters | PREDICTORS | - | 0 Parameters:
|
| Method | Choice | METHOD | - | Available Choices: [0] include all [1] forward [2] backward [3] stepwise Default: 3 |
| P in | Floating point | P_IN | Level of significance for automated predictor selection, given as percentage | Minimum: 0.000000 Maximum: 100.000000 Default: 5.000000 |
| P out | Floating point | P_OUT | Level of significance for automated predictor selection, given as percentage | Minimum: 0.000000 Maximum: 100.000000 Default: 5.000000 |
| Cross Validation | Choice | CROSSVAL | - | Available Choices: [0] none [1] leave one out [2] 2-fold [3] k-fold Default: 0 |
| Cross Validation Subsamples | Integer | CROSSVAL_K | number of subsamples for k-fold cross validation | Minimum: 2 Default: 10 |
| (*) optional |