Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. The goal of multiple linear regression (MLR) is to model the relationship between the explanatory and response variables.
MLR takes a group of random variables and tries to find a mathematical relationship between them. The model creates a relationship in the form of a straight line (linear) that best approximates all the individual data points.
MLR is often used to determine how many specific factors such as the price of a commodity, interest rates, and particular industries or sectors, influence the price movement of an asset. For example, the current price of oil, lending rates, and the price movement of oil futures, can all have an effect on the price of an oil company's stock price. MLR could be used to model the impact that each of these variables has on stock's price. (Source)
We use MLR when we take an in depth audit of a company to verify, with a high level of certainty, the correlation between independent and dependent variables. We run this equation with our measurements, and then check if multicollinearity exists by checking the Varians Inflation Factor (VIF) which quantifies the degree of multicollinearity.