1. What types of business situations or problems might best lend themselves to multiple linear regression? What types may not? Explain.
2. Give an example based on your experience of a situation in which using a multiple regression model or non-linear regression model may have helped you make a better decision.
3. Read the Sanders and Manrodt (1994) article. Which point was most interesting and why?
4. Discuss the strategic importance of forecasting. What strategic decisions do organizations need to make in terms of forecasting? What are some examples?
5. Which business functions are more likely to use optimization models than others and why?
6. Using the Internet resources, find an example of a linear optimization model used in industry. Describe which industry it is or was used in and briefly discuss the problem or challenge it addressed.
7. Explain the importance of correctly stating the objective function and constraints in linear optimization problems. Provide a few examples of the problems that could result if the objective function and constraints are not stated properly. Why would these problems arise?
8. Provide one example each of a workforce scheduling, a blending, and a logistics linear optimization problem not discussed in the Render et al (2011). What is being optimized in each of your examples and why?
9. Describe the key differences between simulation models and the models covered in previous modules, not only from the perspective of their applications, but also from the perspective of computing/solving the models.
10. What key factors of a business situation indicate the need for simulation (versus the other modeling techniques)?
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