Stepwise: The selection process starts by adding the variable with the largest contribution to the model (the criterion used is Student's t statistic).Furthermore, the user can choose several 'criteria' to determine the best model: Adjusted R², Mean Square of Errors (MSE), Mallows Cp, Akaike's AIC, Schwarz's SBC, Amemiya's PC. Best model: This method lets you choose the best model from amongst all the models which can handle a number of variables varying from 'Min variables' to 'Max Variables'.It is possible to select the variables that are part of the model using one of the four available methods in XLSTAT: The linear regression hypotheses are that the errors e i follow the same normal distribution N(0,s) and are independent. The model is found by using the least squares method (the sum of squared errors e i² is minimized). Where y i is the value observed for the dependent variable for observation i, x ki is the value taken by variable k for observation i, and e i is the error of the model. The determinist is written for observation i as follows: The principle of linear regression is to model a quantitative dependent variable Y through a linear combination of p quantitative explanatory variables, X 1, X 2, …, X p. A distinction is usually made between simple regression (with only one explanatory variable) and multiple regression (several explanatory variables) although the overall concept and calculation methods are identical. Linear regression is, without doubt, one of the most frequently used statistical modeling methods.

Excel linear regression plot