個案總覽

Spreadsheet supplement for case UV7699.

In this second part of a two-part case, the finance manager learns that the incentive program for the financing of solar energy installation has been adjusted to incorporate a tiered structure.

Spreadsheet Supplement for UV6694

"Is there statistical evidence of global warming? This case presents two sets of real global temperature data, one from the Met Office and another one from NASA, and asks students to assess if the data indeed support the belief that temperatures have been rising over the last 150+ years. The case is open-ended-it provides the data and references to some popular press articles on the subject. Also available is a B case that presents three sets of analysis typical for MBA students-an efficient starting point for class discussion. "

Spreadsheet Supplement for UV6690

Is there statistical evidence of global warming? The B case presents the results of three different analyses that are representative of MBA students' work. The case series is effective in discussing the analysis of time-series data in the context of business analytics, predictive analytics, big data, or other similar courses. Given that the case is based on global warming-a phenomenon all students are undoubtedly aware of-it is eye-opening with respect to how incorrect data analysis can indeed "support" different points of view.

This field-based case is an efficient vehicle for exposing 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. Although the name of the company and individuals are disguised, the data are real and adjusted by an unspecified constant so that all relationships are preserved.

In the first part of a two-part case, a strategic finance manager at Wells Fargo with experience installing solar panel systems on bank branches crunches the numbers for a similar project in the Los Angeles area given the uncertain future of a government rebate.

This very short and seemingly straightforward case is an efficient vehicle for exploring the nuances of hypothesis testing via regression modeling and t-tests in the context of an MBA or advanced undergraduate analytics course.

Spreadsheet for case UV6484.

Logical conditions that link different elements of a business decision are very common in managerial practice. For example, a firm can ship only to and from warehouses that are open; patients needing MRIs can only get service in clinics that have MRI equipment; regarding an old power plant, one can decide to close it or retrofit it, but one obviously cannot retrofit a closed plant. The list goes on. In quantitative modeling of such situations, a natural step is to use IF statements such as IF(a warehouse in city N is open, then we can ship to/from it; otherwise no shipments can be made in/out of a warehouse in N). In optimization models, however, IF statements lead to non-linearity with all the associated challenges. Fortunately, nearly all logical conditions can be modeled linearly using binary variables. This note describes some helpful modeling techniques for doing that.

This technical note introduces business students to the concepts of modeling discrete choice (e.g., a consumer purchasing brand A versus brand B) using logistic regression and maximum-likelihood estimation. It draws the analogy between modeling discrete choice and building a regression model with a dummy dependent variable and on an example illustrates the need for estimating the probability of a choice rather than the choice itself, which leads to a special kind of regression - logistic regression. The note presents the concepts of utility and a random utility choice model, of which the logistic regression model is the most commonly used. It shows how choice probabilities can be constructed from utilities leading to the logit model. It then presents the maximum-likelihood estimation (MLE) method of fitting the logit model to the choice data. Working through a detailed example using Solver and accompanying spreadsheet model, the note gives students deep understanding for how MLE works and how it is similar and different to the standard least-squared estimation in linear regression. The note concludes by presenting the results of estimation using StatTools, a commercial statistical software. The note avoids the use of heavy mathematical machinery but still requires rudimentary knowledge of exponent and logarithmic functions, probability, and optimization with Solver, as well as familiarity with the "standard" linear regression. Applications include building of models for consumer choice, estimating price elasticity, price optimization, product versioning, product line design, and conjoint analysis.

Spreadsheet supplement for case UV6335.

These exercises, designed for use in a spreadsheet modeling pre-matriculation course, provide a baseline education in spreadsheet modeling and Microsoft Excel to incoming MBA students who have not had extensive modeling experience or exposure to Excel.

Spreadsheet supplement for case UV6325.

This case describes an IQ experiment in which 66 students, under the direction of their professor, used Excel to generate simulated IQs. The instructions were such that the generated IQs should have come from a normal distribution with a mean of 100 and standard deviation of 15. The sample average IQ, however, was 104.3. Students are asked to use their data-analysis skills to make sense of the results. The data set also contains gender (male or female) and fall section (A, B, C, D, or E) for each student, which allow for additional statistical analyses (tests of equality of means).

Suitable as an assignment or as an exam case, this short case is effective in illustrating the use of linear and integer programming in an assignment problem, wrapped around R&D project selection in a global IT firm. A twist in the case requires the use of binary variables to implement certain logical conditions on which projects can be undertaken by which project teams. The case is suitable for graduate or advanced undergraduate courses or modules on optimization, decision models, management science and the like, both for class discussion and as a part of assignment or exam. A spreadsheet is available to enhance student learning.

Spreadsheet for case UV6310

A Canadian online floral delivery company considers whether to source more flowers from its existing growing locations and which of those would be optimal given its current market. What would be the advantages of opening a logistics facility in Miami?

Spreadsheet for case UV6304