Southern New Hampshire University ECO 301: Decision-Making Analysis Paper Guidelines and Rubric Overview Your final project for this course is a detailed analysis of a specific problem statement. How economic themes, such as demand, production, cost, and market structure relate to a particular company will be a focus of this analysis. You will analyze these components with quantitative techniques, like regression analysis and linear programming. You will select a product or service with substantial data. Some possible topics: unemployment and crime, exports and underdeveloped countries, demand/supply of higher education, air pollution and population, etc. The final deliverable will include an introduction, problem statement, listing of data sources collected, estimate and analyses of data, and a conclusion addressing how your findings can inform future real-world decision making, at both an organizational level and an individual level. The project is divided into three milestones and a final product, which will be submitted at various points throughout the course to scaffold learning and ensure quality final submissions. Prompt Your final project should answer the following prompt: What economic theories and quantitative techniques are used to solve business decision problems and how are they applicable in real-world settings? Specifically, the following critical elements must be addressed: 1. Introduction/Statement of Problem A. What problem are you trying to solve? Discuss the history and key information about the problem and address why the issue/problem is important. B. Describe the model, hypothesis, and theoretical framework that will be used to explain and forecast variables. The model should be in the form of functional equations. QD = f(P, Y, ..} C. What data sources do you plan to use? A minimum sample size of 15 is required. Include a complete description of the data sources and assess their validity, accuracy, creditability, and reliability for the chosen issue. Make sure all data sources are referenced. D. Which variables are used in the model? Why are they used? Considering the relation among measurable variables, what is the impact of an independent variable X on a dependent variable Y? Are there additional independent variables that could influence variable Y? If so, explain. E. What assumptions can you make about the data? In your analysis, consider the following: accuracy, consistence, sample as representative of the population, biased/unbiased, efficient, and weakness of data (currency, not a complete data set, biased, not scientifically accurate). F. What estimation procedure do you plan to use? If you are using time series data, be sure to account for the identification problem. Why did you choose is this particular procedure