The Linear Regression Analysis is used to determine whether a statistically significant relationship exists between a dependent variable and one or more independent variables. When there is only one independent variable the analysis is called Simple Linear Regression, with two or more independent variables it is called Multiple Linear Regression. In a Regression Analysis, the dependent variable should be continuous and the independent variables also continuous or dummies (variables with values 0 and 1). For each independent variable a coefficient will be estimated that reflects the relationship with the dependent variable. Each of these coefficients is separately tested to see if it is significantly different from zero. Several assumptions should be checked: no perfect multicollinearity, normality, homoscedasticity and linearity. All of these can be assessed by looking at additional statistics and plots.