Prof. Constantin Anghelache
„Artifex” University of Bucharest, Bucharest University of Economic Studies
In the theoretical analysis, dependency of variables is stochastic. Consideration of the residual variable within such a model is needed. Other factors that influence the score variable are grouped in the residual. Uni-factorial nonlinear models are linearized transformations that are applied to the variables, the regression model. So, for example, a model of the form turns into a linear model by logarithm the two terms of the above equality, resulting in linear function. This model is recommended when the points are located, that the cloud of points around a line.
Linear regression model is based on the series of data for the two features. They are represented by vectors x (the variable factor) and y (variable score).
Simple regression aim is to highlight the relationship between a dependent variable explained (endogeneous, score) and an independent variable (explanatory note, exogenous factor predictors).
To be able to build a linear regression model we defined total production as the independent variable, while labor force in financial intermediation and insurance; real estate was considered to be a dependent variable.
