[MUSIC] The regression analysis is one of the most useful tools while dealing with financial and economic models. The regression analysis describes and evaluates the relationship between a given variable and one or more other variables. The final goal is to explain movements in a variable y due to movements in one or more other variables. xs such that x1, x2, x3 up to xk which cause changes in some other variables y. We need to be aware that regressions does not mean correlation between two variables. The correlation measures the degree of linear association between variables. This means that y and x are treated in a symmetrical way. In case of regression, instead the dependent variable y and the independent variable x are treated very differently. The y variable is assumed to be random or stochastic. And then, it has a probability distribution. The x variables are however assumed to have fixed, that is, non-stochastic values even in repeated samples. Suppose that, based on previous studies, there should be a relationship between two variables, y and x. And that this theory suggest that, an increase in x will lead to an increase in y. If this is the case then the relationship between them can be described by a straight line, which is the line which fit our data. Therefore, it is of our interest determining to what extent this relationship can be described by an equation that can be estimated using a certain procedure. We use the general equation for a straight line in order to get the line that best fits the data, such that y equal to alpha plus beta x. Basically, we are aimed at finding the values of the parameters or coefficients with alpha and beta. Which would place the line as close as possible to all of the data points taken together. However, this equation is not complete. Even if we add more explanatory variables in order to explain better the behavior of y, there will always be some omitted determinants of y from the model. This is, for example, because some determinants are not observable or not measurable. There will be always what we call an error term or the random disturbance term. Therefore, our equation becomes yt = alpha + Beta xt + ut, where alpha is the constant term, and beta is the slope of our straight line. Adding the error term makes our model more realistic. The errors represent the vertical distance between the estimate line and the data. Therefore, our goal is to find the straight line which best fits the data, given that a random error does exist. The best way is to estimate the values of alpha and beta which minimize this vertical distance. [MUSIC]