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EuroPet S.A. was a multinational company operating gas stations in many European countries. There was a growing propensity for supermarkets to attach gas stations to their retail operations, which was developing into a major threat to EuroPet. As a result, in the mid-1990s, the company began to develop and brand its own convenience stores co-located with its gas stations. However, the company was spending much more on advertising the convenience stores than its competitors did. Management now had to decide if the increase in sales attributed to advertising efforts justified the advertising spend by analyzing the market data from one large metropolitan area: Marseille, France.

Students will learn: how to use cross-tabs and other marketing research tools to identify segmentation descriptors; how to analyze data and interpret results; and how these research results could guide new product development and positioning strategies in order to effectively target relevant customer segments.

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.

Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

During one of the most nerve-wracking football matches of the 2012–2013 Bundesliga season, life-long friends Franz Dully and Max Vogel begin arguing about whether the wealth of a football club determines its success during the season. In order to disprove Vogel's claim that “money scores goals,” Dully must analyze the Bundesliga's current market values, points earned, and mid-season leader data.

After analyzing the case, students will be able to compute prediction intervals, develop regression models, and interpret data. The development of the regression models asks students to choose the relevant set of independent variables, as well as determine an appropriate functional form for the regression equation. The models derived have to be evaluated as well as compared to one another. Further, the students have to interpret the quantitative findings in the context of the application.

The financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.

The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.