Regression analysis: An evaluation of the inuences behindthe pricing of beer

Abstract: This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find significant factors and, as follows directly, a significant model without any influence of multicollinearity. The regression analysis is based on a data set with 1413 observations which represent beers that sold over 1000 liters, among further restrictions, and is created from Systembolaget's sale statistics for 2016 and Ratebeer. This number of observations represents 43% of Systembolaget's total assortment of beer. The model is developed through a thorough residual analysis, transformations of variables, determination of multicollinearity and a validation of the absence of outliers and high leverage points. All of these in favor for significance at a level of 95%. In addition to the regression model, two submodels with associated box plots for the variable groups Country of Origin and Beer Style are created for analyzing the importance of these variables amongst each other. A k-fold cross validation study and three different variable selections are carried out for further adequacy checking, these are also given as recommendations for continued analysis. The result shows that there are several different factors that affect the pricing of beer. For example, higher alcohol by volume, sour beers and beers from New Zealand yields a higher price while beers with high sales, lagers and Austrian beers show a negative tendency for the price. The result can be used as an example of the influences behind the pricing of beer in Sweden. The first model in the analysis has 41 explanatory variables and in the final model the number of explanatory variables is reduced to 20 where all are significant.