Multiple linear regression analysis using ordinary least squares.
| Name | Type | Identifier | Description | Constraints |
Input | Table | Table (input) | TABLE | - | - |
Output | Results (*) | Table (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 |