Model the linear relationship between numeric response and one or more explanatory variables by fitting a linear regression model.
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.
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
|outcome_var||Yes||Column with data type one of: Decimal, Integer||
The dependent variable to be modeled by the selections in
|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|