Traditional statistical test procedures are briefly reviewed. It is pointed out that significance testing may not always be reliable. The author has formulated a modelling procedure, the H-principle, for how mathematical modelling should be carried out in the case of uncertain data. Here it is applied to linear regression. Using this procedure, the author has developed a common framework for carrying out linear regression. Six regression methods are analysed by this framework, two stepwise methods: principal component regression, ridge regression, PLS regression and an H-method. The same algorithm is used for all methods. It is shown how model validation and graphic analysis, which is popular in chemometrics, apply to all the methods. Validation of the methods is carried out by using numerical measures, cross-validation and test sets. Furthermore, the methods are tested by a blind test, where 40 samples have been excluded. It is shown how procedures in applied statistics and in chemometrics both apply to the present framework.