On Multivariate Linear Regression Applications

Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correlated, but when there is limited amount of data for training, using other larger and more sophisticated models might be impossible. Even though simple linear relations are hard to come by in nature, more complex mathematical dependencies can often be linearized i.e. approximated using linear models. Additionally, by preprocessing the input data using nonlinear mathematical operations linear regression can artificially account for nonlinear relations. This methodology was tested on real data representing measurements in thermal power plant while the goal was predicting the specific energy of the coal that was being burnt in the plant. Estimating coals caloric value is of tremendous value since using this information power plant can be configured to run more efficiently.