The regression model y = Xβ + ε , divides the response into two components: one systematic, Xβ and one random, the error ( ε ). What we want is to choose β so that the systematic components explain as much of the response as possible. We have to find the unknown parameters β that make X β is as close to Y as possible. What is the analytical relationship that best fits our data? The least squares method is a general procedure that allows us to answer this question. Ordinary least squares (OLS) is a method for estimating the unknown parameters β in a linear regression model, with the goal of minimizing the sum of the squares of the differences between the observed response in the given dataset. We have to take into account that It is common to assume the hypothesis that the ordinary least squares method must be used to minimize the residuals (difference between the values in the data set and the adjusted line). Under this ...
