Linear regression methods and application in marketing

U ovom diplomskom radu proučili smo linearnu regresiju s naglaskom na jednostavnu i višestruku regresiju. Za početak, rekli smo nešto o povijesti regresije, bivarijantnoj normalnoj distribuciji te njenim svojstvima. Proučili smo metodu maksimalne vjerodostojnosti te metodu najmanjih kvadrata, koja je i najstarija metoda, te je radi svoje jednostavnosti najčešće korištena za procjenu parametara. Od interesa nam je bila i analiza varijance te smo kroz primjer vidjeli i njenu primjenu. Kraj ovog diplomskog rada ilustrira primjenu suvremenih pristupa na skupu podataka tvrtke Nepa. Cilj posljednjeg poglavlja bio je konstruirati strategiju kako napraviti model prikladan za visoku razinu složenosti podataka. Marketing Mix model bavi se svim elementima problema koji se proučava. Jedan od tih elemenata je izbor odgovarajuće funkcionalne forme. U ovom slučaju, dijagram reziduala sugerira da je log-linearan model najprikladniji.In this thesis, we are introduced to the linear regression with an emphasis on simple and multiple regression. To begin with, we said something about the origins of regression, the bivariate normal distribution and its facts. We have seen maximum likelihood method and the method of least squares, which is also the oldest method and, due to its simplicity, is most often used for parameter estimation. The analysis of variance was also of interest to us, and we saw its purpose of using through an example. The end of this thesis illustrates an application of modern approaches of statistical learning on a set of data provided by Nepa. The goal was to construct a model building strategy suitable for a high level of complexity of the data. A Marketing Mix model must address all elements of the problem being studied. One of such elements is the choice of the appropriate functional form. At the end, the plot of the residuals against each predictor variable suggest that the log-linear specification is appropriate in this case