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Faeyee Electronics was an electronics company that manufactured and sold smartphones including XS and XT types. The management of Faeyee wanted to pursue the maximum contribution margin as much as possible, especially since they were faced with limited resources. It was necessary to apply analytical tools and cost accounting concepts to study this case including cost-volume-profit analysis, learning curve analysis, regression analysis, definitions of competitive products, constrained non-linear optimization, and contribution margin.

The case uses business analytics tools and cost accounting concepts, including regression models and constrained optimization approaches, to study how to maximize business outcomes, such as contribution margin and profits when limited business resources are available. The company and individuals are disguised.

This case can be used in any junior-, senior-, masters- or MBA-level managerial accounting course. Students need to have at least one course of introduction to statistics or instructors review required statistics concepts or techniques before assigning this case. Students are exposed to the challenges of deriving learning curve models, using regression analysis to study collected data and allocating limited resources to maximize contribution margin.

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit platform launched by Shanghai Mobanker Co. Ltd. (referred to as “Mobanker”, previously named as “Shanghai Mobanker Financial Information Service Co., Ltd.”) which provides technical services for inclusive finance industry.

Greg Mills describes his search for the perfect engagement ring which includes an analysis of the prices of 6,000 diamonds. An engineer, Greg hopes to impress Sarah Staggers by using regression to find an underpriced diamond. Students are asked to either select one of the 6,000 diamonds or provide point forecasts for prices of 3,142 diamonds in a hold-out sample. The instructor can use the actual prices of the held-out diamonds to evaluate student pricing models.

This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study.

This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies’ success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing.

Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment.

This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers’ understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes.

The case deals with marketing research study undertaken to introduce a new product in the market. The company was planning to introduce Cerenity, a toilet seat sanitizer for women who frequently use shared restrooms. The case discusses the conclusive study undertaken involving quantitative marketing research. The research team carried out quantitative survey and collected the data. It applied various quantitative research methods such as factor analysis, multiple regression, cluster analysis and conjoint analysis for analysis the collected and drawing managerial inferences.

It was early 2015 and executives in iShares' Factor Strategies Group were considering the launch of a new class of exchange-traded funds (ETFs) called smart beta funds. Specifically, the group was considering smart beta multifactor ETFs that would provide investors with simultaneous exposure to four fundamental factors that had shown themselves historically to be significant in driving stock returns: the stock market value of a firm, the relative value of a firm's financial position, the quality of a firm's financial position, and the momentum of a firm's stock price. The executives at iShares were unsure whether there would be demand in the marketplace for such multifactor ETFs, since their value added from an investor's portfolio perspective was unknown. Students will act as researchers for iShares' Factor Strategies Group and conduct detailed analysis of Fama and French's five-factor model and the momentum effect, smart beta ETFs including multifactor ETFs, and factor investing with smart beta ETFs to help iShares make its decision.

Nicolas Dupont, the owner of Chateau de Montana, a struggling (and old) boutique hotel in Crans-Montana Ski Resort, Switzerland, wished to renovate and reposition his family-owned hotel to target higher room rates. Dupont commissioned Olga Mitireva and Yulia Belopilskaya as consultants to assess the proposition. The consultants had to extract cues for the room rate of the repositioned hotel from comparable hotels. However, the room rates varied significantly across similar hotels due to their differing characteristics and locations. It was a cognitive challenge to read the patterns from a few comparable hotels. They collected the data of 200 hotels from similar locations and simulated room prices using hedonic regression models.

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.

In the early months of the 2007-08 financial crises, a loan manager faces a real estate financing decision. Should he approve a bullet structure three-year loan to a longstanding client, a legendary Texan developer? The developer, who near retirement downsized his business, is seeking financing for his only project: residential or commercial development on an attractive piece of land in suburban Houston. The loan manager considers the decision in light of the mortgage market turmoil, seeing commercial projects as safer, but also factoring that the residential market could bring higher returns if the market stabilizes soon. The manager collects the data and asks an analyst to assess the risks; that ultimately requires assessing the economics of both projects from both the bank's and the developer's perspectives. The bank could still change the interest rate on the loan to receive adequate compensation for the risk it carries, but the loan manager knows that doing so will change their long-term client willingness to take on the loan.