Regression Analysis on a Boot Manufacturer
Regression Analysis of Army Jackboots
Ochirmunkh Boldbaatar, Myriam Hirscher, Bastian Latz, and Manuel Padutsch
ECON 510
Aun Hassan
November 26, 2012
Introduction
The German company we established the data from sells cloths and shoes. The customers are not private customers but mostly national divisions like the military or fire departments. The company has around 20 stores in Germany; however, the stores have different prices for the same products.
The data package we received includes prices and demands for Bergstiefel (Mountain boots) for more than four years, beginning at February 2008, ending in September 2012. As the prices of the Bergstiefel changed almost every month and the prices as well as the quantity demanded is wide-spread we used the regression analysis in order to determine the demand function of the product, the total revenue, and the revenue maximizing optimal price.
Data
The data that are used in this paper have been received from a German career apparel company that is specialized on national divisions like the German Army, the German fire departments, and German state justice. The analyzed data illustrate the quantity and the price to which boots called Bergstiefel have been sold from February 2008 to September 2012. These 56 observations will be used to run a regression analysis. Table 1 shows the quantity of sold Bergstiefel in every month to a certain price. The company has around 20 stores spread out in whole Germany. Caused by different special offers in these branches, the monthly average price that is presented in Table 1 differs almost every month. Another impact for price changes is the launch of new competitive products and derivatives of the Bergsiefel. In the observation period, 14,667 boots have been sold and generated total revenue of around 1.8 million Euro. The lowest quantity sold (62 pairs of boots) can be traced back to the launch of the product in February 2008. Only one month later, a high price reduction
Ochirmunkh Boldbaatar, Myriam Hirscher, Bastian Latz, and Manuel Padutsch
ECON 510
Aun Hassan
November 26, 2012
Introduction
The German company we established the data from sells cloths and shoes. The customers are not private customers but mostly national divisions like the military or fire departments. The company has around 20 stores in Germany; however, the stores have different prices for the same products.
The data package we received includes prices and demands for Bergstiefel (Mountain boots) for more than four years, beginning at February 2008, ending in September 2012. As the prices of the Bergstiefel changed almost every month and the prices as well as the quantity demanded is wide-spread we used the regression analysis in order to determine the demand function of the product, the total revenue, and the revenue maximizing optimal price.
Data
The data that are used in this paper have been received from a German career apparel company that is specialized on national divisions like the German Army, the German fire departments, and German state justice. The analyzed data illustrate the quantity and the price to which boots called Bergstiefel have been sold from February 2008 to September 2012. These 56 observations will be used to run a regression analysis. Table 1 shows the quantity of sold Bergstiefel in every month to a certain price. The company has around 20 stores spread out in whole Germany. Caused by different special offers in these branches, the monthly average price that is presented in Table 1 differs almost every month. Another impact for price changes is the launch of new competitive products and derivatives of the Bergsiefel. In the observation period, 14,667 boots have been sold and generated total revenue of around 1.8 million Euro. The lowest quantity sold (62 pairs of boots) can be traced back to the launch of the product in February 2008. Only one month later, a high price reduction
References: Baye, M. R. (2010). Managerial Economics and Business Strategy. (7 International Edition), McGrawHill.