Linear Regression

Linear Regression icon

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

See Also

Updated on October 18, 2018

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