For This Project You Will Demonstrate Competency In Research

For This Project You Will Demonstrate Competency In Researching Econom

For this project you will demonstrate competency in researching economics; that is, creatively designing a research question, locating pertinent and credible data to support an answer, and presenting results in a professional and articulate manner. Furthermore, you will also be applying fundamental statistical and regression concepts to your data sets to more quantitively answer your research questions. Follow these steps to complete the project:

1. Using the data covered in the National Economy, Wealth Income and Poverty, Business Statistics, Labor Statistics, and Government, generate five research questions to study (one from each category). For this project, use causal phrasing (e.g., “Higher taxes cause lower GDP,” “Increased worker productivity increases savings,” etc.).

2. Excel File:

  • A. For three of the five research questions create an Excel sheet with your data set, one graph, and the statistical metrics listed below. Compile all the statistical metrics below and use a different type of graph for each question. All statistical metrics and graphs are to be calculated/generated in Excel using the functions reviewed in class:
    • Mean (weighted, arithmetic, or geometric)
    • Median
    • Sample Variance
    • Standard Deviation
    • Coefficient of Variation
    • Range
    • Percentiles
    • Quintiles
    • Skewness
  • B. Single-variable regression: For one of the five research questions create an Excel sheet with your data set, one scatterplot graph, and the analysis output. Ensure n ≥ 30 (at least 30 data points that correlate across time or space). Add the R-squared and trendline to the scatterplot; use the functional form with the highest R-squared. Use Excel’s Data Analysis ToolPak to run a single-variable regression and generate the Analysis Output.
  • C. Multiple-variable regression: For one of the five research questions create an Excel sheet with your data set and the analysis output. Ensure n ≥ 30. Use at least six independent variables believed to influence the dependent variable. Use Excel’s Data Analysis ToolPak to run a multiple-variable regression and generate the Analysis Output.

3. PowerPoint Presentation: For each research question, create at least one slide illustrating the relevant graphs, statistical metrics, and regression results. Include bullet points (up to 3, optionally), and hyperlinks to your data source websites (ensure links are functional). The presentation should also include an introduction slide with your name, project number, and class information. For the regression slides, include:

  • The regression R-squared value and its interpretation
  • The statistically significant coefficients and how significance was determined
  • The statistically insignificant coefficients and their determination
  • An interpretation of each significant coefficient, explaining how changes in independent variables impact the dependent variable

Use appropriate units and descriptions, e.g., “each square foot added increases home price by $123.” If no significant results are found, indicate that the results cannot be reliably interpreted.

4. Submission: Upload Excel and PowerPoint files via Blackboard by the specified deadline. No email submissions are allowed.

5. Grading: The project is weighted 50% for Excel and 50% for PowerPoint. Both must be submitted to receive a grade. Excel graphs should originate from your own data input. The PowerPoint will be graded on layout, spelling, character size, color, creativity, and professionalism.

6. Academic Integrity: Do not copy graphs from websites or from other students’ work.

Paper For Above instruction

This project aims to develop a comprehensive understanding of economic research through the creation and analysis of data-driven questions across multiple economic domains. By engaging in this process, students will hone their skills in data collection, statistical analysis, and effective communication of findings, essential competencies for any aspiring economist or data analyst.

Initially, students are tasked with formulating five research questions, each derived from a different area within economic datasets, such as the National Economy, Wealth Income and Poverty, Business Statistics, Labor Statistics, and Government data. The questions must be causal in nature, focusing on how changes in one economic variable influence another. For example, a student might explore whether increased taxes lead to reduced GDP or whether higher worker productivity boosts savings rates. This step encourages critical thinking and the formulation of testable hypotheses grounded in economic theory.

Following question formulation, students are required to collect pertinent data and conduct detailed statistical analyses. For three of their questions, they will construct an Excel dataset, generate at least one relevant graph, and compute fundamental statistical metrics, including mean, median, variance, standard deviation, coefficient of variation, range, percentiles, quintiles, and skewness. Each metric and graph should be created using functions reviewed during coursework, ensuring mastery of Excel's analytical capabilities.

In addition to descriptive statistics, students will explore inferential relationships via regression analyses. For one research question, a single-variable regression analysis will be performed. This involves selecting a dataset with at least 30 data points, plotting the data with a trendline, calculating the R-squared value to determine the best functional form, and interpreting the regression output using Excel’s Data Analysis ToolPak. Significance of coefficients will be assessed—those with p-values below a standard threshold (typically 0.05) will be deemed statistically significant, and their impact interpreted accordingly.

The project expands further with multiple regression analysis. Students choose one question and include at least six independent variables believed to influence the dependent variable. This analysis will illustrate the combined effects of multiple factors, with interpretation focused on the statistical significance, coefficient magnitudes, and signs of each independent variable. The larger goal is to understand how various factors interact within an economic context and to communicate these complexities effectively in a clear, professional format.

The final deliverables include a PowerPoint presentation summarizing each research question and its analysis. The slides should visually display graphs, highlight key statistical findings, and articulate interpretations. An emphasis on clarity, visual appeal, and professionalism is necessary, as the presentation will be evaluated holistically. For regression slides, students must explain the significance of R-squared, interpret significant coefficients, and assess how changes in variables influence the outcome. If analysis reveals no statistically significant relationships, students must accurately state these results without overstating their importance.

Finally, the project emphasizes academic honesty. Students must produce original work, avoid copying graphs or analyses from external sources, and ensure their submissions adhere to ethical standards. The entire project is a rigorous exercise in applying statistical tools to economic data and communicating insights effectively, preparing students for real-world economic research tasks.

References

  • Gravelle, J. G., & Rees, D. A. (2014). Microeconomics. Pearson.
  • Hubbard, R. G., & O'Brien, A. P. (2017). Microeconomics. Pearson.
  • Samuelson, P. A., & Nordhaus, W. D. (2010). Economics. McGraw-Hill Education.
  • Stock, J. H., & Watson, M. W. (2019). Introduction to Econometrics. Pearson.
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning.
  • International Monetary Fund. (2022). World Economic Outlook Database. https://www.imf.org/en/Data
  • Bureau of Economic Analysis. (2023). National Income and Product Accounts. https://www.bea.gov/data
  • U.S. Census Bureau. (2023). Income and Poverty Data. https://www.census.gov/data.html
  • Federal Reserve Economic Data (FRED). (2023). St. Louis Fed. https://fred.stlouisfed.org/
  • OECD. (2022). Main Economic Indicators. https://data.oecd.org/

This comprehensive approach ensures that students not only learn statistical techniques but also understand their application within economic research, fostering skills applicable to academic pursuits, policy analysis, and business decision-making.

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