The Linear Regression method in Predictive Modelling is used when the response column is numeric and continuous, and the relationship between the response and the predictors is assumed to be linear. The Linear Regression method models the numeric response column as a weighted sum of the predictor columns, plus an intercept term. The weights, or coefficients, are estimated by minimizing the sum of squared errors between the observed and predicted values1. The Linear Regression method can be used to assess the significance and effect of each predictor, as well as to make predictions for new data2. References: Building a Regression Model in Spotfire, TIBCO Spotfire - Use Regression Modeling to Predict Numerical Values