Reference no: EM13995946
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:
I. Introduction/Statement of Problem
A. What problem are you trying to solve? Discuss the history and key informationabout 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?
II. Regression Analysis - Perform a regression analysis on the data.
A. Compute for the following:
1. Standard errors
2. T-stats
3. R2
4. Coefficients, 5% and 1% levels
5. Possible other models to test
III. Computer Output and Evaluation of Results - What are the results of your regression analysis? Include the data you used to estimate the demand model, the results of your analysis, an analysis of the results, and a description of remaining issues that need to be addressed, i.e. other models to test. Be sure to include standard errors, t-stats, R2, coefficients, 5% and 1% levels.
IV. Statistical Analysis of Results- What are the implications of the t-stats, F test and Adjusted R2? Are they consistent or contradictory? If they seem to be contradictory, how can this be resolved?
V. Conclusion and General Comments -What are key take-a-ways that can be applied in your own personal or professional real-world settings? Provide concrete or hypothetical examples (if you are not in an applicable field) that support your conclusions. How might this model change in the future given assumptions?
Is there is any possibility that SOMA BASAK will do this project?