Model the linear relationship between numeric response and one or more explanatory variables by fitting a linear regression model.
Details
This module will create a linear model, modelling one dependent variable as a linear combination of one or more independent variables. Linear regression is often used for the purpose of fitting a predictive model to dataset containing the response and predictor variables. Linear regression can also be used to assess the relationship between different variables in a dataset and analyse whether the variance in some variables can be explained or modeled by linear combinations of other variables.
Output
The module output is the summary output of a linear model created in R. More details on this output can be found in the R documentation for lm.
Parameters
| Variable name | Required | Constraints | Description |
|---|---|---|---|
| outcome_var | Yes | Column with data type one of: Decimal, Integer |
The dependent variable to be modeled by the selections in model_var1, model_var2, ...
|
| model_var1 | Yes | Any column other than the column chosen for outcome_var. | The first independent variable or predictor variable to include in the linear model. |
| model_var2 | No | Any column other than the column chosen for outcome_var. | An optional second predictor variable. |
| model_var3 | No | Any column other than the column chosen for outcome_var. | An optional third predictor variable. |
| model_var4 | No | Any column other than the column chosen for outcome_var. | An optional fourth predictor variable. |
| model_var5 | No | Any column other than the column chosen for outcome_var. | An optional fifth predictor variable. |
| include_intercept | Yes | Boolean | Whether to include an intercept term in the model |