Data Analysis Project 3 Due 05/09 Until Midnight

Data Analysis Project 3 Due 05/09 until midnight (please focus.)

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 quantitatively 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 type 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. Furthermore: · Make sure n ≥ 30 (that is you should have 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 TookPak regression function 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. Furthermore: · Make sure n ≥ 30 (that is you should have at least 30 data points that correlate across time or space) · Use at least six independent variables. · All independent variables should be believed to influence the dependent variable. · Use Excel’s Data Analysis TookPak regression function to run a multiple-variable regression and generate the Analysis Output.

3. PowerPoint Presentation: For each research question (five total), create at least one PowerPoint slide illustrating the pertinent graphs, statistical metrics, regression results, bullet points (up to 3 and optional), and hyperlinks to your data source website (make sure the links work). The PowerPoint should also contain an introduction slide (e.g. name, project #, and class). For the regressions add at least one slide answering each of the following: · What is the regression R-Squared and what does it mean regarding your data? · What are the statistically significant coefficients and how did you conclude they were statistically significant? · What are the statistically insignificant coefficients and how did you conclude they were insignificant? · Interpret each statistically significant coefficient to determine how your dependent and independent variables are correlated; that is, for a change in each independent variable how does this impact the dependent variable. Make sure you use the proper denominations (e.g. each square foot added to a home increases the home price by $123). If no statistically significant results are found, then you are to do the same thing but indicate that the results cannot be relied upon.

4. Submission: Upload the Excel and PowerPoint file into the link provided in Blackboard by the due date (no e-mailed copies).

5. Grading: Project grade is weighted 50/50 for Excel/PowerPoint; however, both must be submitted to receive a score. Excel graphs must be derived from the data input in Excel. The PowerPoint is graded subjectively as a presentation to your fellow classmates so cosmetics, spelling, character size, color, creativity all matter.

6. Academic Integrity: Do not copy graphs from websites nor replicate another student’s work.

Paper For Above instruction

The project at hand provides an opportunity to integrate and demonstrate a comprehensive understanding of economic data analysis, statistical measures, and regression techniques across various domains within economics. The overarching goal is to craft well-defined research questions rooted in credible data sources, analyze the data statistically and through regression models, and articulate findings effectively via a structured PowerPoint presentation and accompanying Excel datasets. This process not only emphasizes technical skills but also fosters critical thinking about economic phenomena, causality, and real-world implications.

First, selecting five research questions from distinct categories—namely the National Economy, Wealth Income & Poverty, Business Statistics, Labor Statistics, and Government Statistics—is fundamental. Each question should be phrased causally to explore the influence or effect of one economic variable on another, such as "Higher taxes cause lower GDP" or "Increased worker productivity increases savings." This approach encourages examining relationships that imply causation, supported by data. For instance, from the National Economy, a question might investigate the impact of consumer confidence on GDP growth. From Wealth Income & Poverty, one could explore how income inequality affects poverty rates. Business Statistics might examine how market concentration influences corporate profits, while labor statistics could focus on how minimum wage changes impact employment levels. Government statistics might analyze how government debt levels relate to interest rates or economic stability.

The next step involves data collection and analysis within Excel. For three of these questions, the process includes creating datasets, calculating a suite of descriptive statistics—mean, median, variance, standard deviation, coefficient of variation, range, percentiles, quintiles, skewness—and visualizing data with appropriate graphs. Each graph must differ to provide varied visual insights into the data. Excel's functions reviewed in class should be used to ensure accuracy and consistency. For example, plotting a scatterplot for a single-variable regression and adding R-squared and trendlines assesses the strength of relationships over time or space. Additionally, employing Excel's regression tools generates detailed analysis outputs.

Further, one question should involve simple linear regression analysis with at least 30 data points, ensuring the regression model’s validity. This includes evaluating R-squared, interpretating significant and insignificant coefficients, and understanding their implications on the dependent variable. Similarly, a multi-variable regression should analyze six or more independent variables believed to influence a single dependent variable, providing insights into the combined effects and significance of each predictor.

The PowerPoint presentation is designed to synthesize your quantitative findings visually and narratively. Each of the five research questions will have a dedicated slide or set of slides, displaying graphs, statistical summaries, regression outputs, and concise bullet points to interpret findings. Key regressions should include discussions of R-squared values, significance of coefficients, and their economic interpretation—such as how a unit change in an independent variable impacts the dependent variable, with proper monetary or percentage denominations.

Finally, the project emphasizes the importance of integrity and originality. All work, including data visualizations, analyses, and presentations, must be your own or properly cited if derived from external credible sources. Submissions are to be uploaded via Blackboard, ensuring timely delivery. Both the Excel data sheets and PowerPoint slides are essential for a complete assessment, with grading based equally on technical accuracy, clarity, creativity, and professionalism.

This comprehensive exercise prepares students to think critically about economic data, applying rigorous analytical tools to uncover relationships, evaluate policy impacts, and communicate insights effectively. It hones not only technical skills but also the capacity to interpret and convey complex economic phenomena in a clear, professional format suitable for academic and policy discussions.

References

• Blanchard, O. (2017). Macroeconomics (7th ed.). Pearson.
• Fisher, I. (1933). The Purchasing Power of Money. New York: Macmillan.
• Gravelle, J., & Rees, J. (2015). Microeconomics (4th ed.). Pearson.
• Mankiw, N. G. (2018). Principles of Economics (8th ed.). Cengage Learning.
• National Bureau of Economic Research. (2020). Data and Measurement in Economics. NBER.
• U.S. Bureau of Labor Statistics. (2023). Employment and Unemployment Summary. BLS.
• U.S. Census Bureau. (2022). Poverty Estimates and Income Data. Census.
• International Monetary Fund. (2017). World Economic Outlook: Exploring Globalization. IMF Publications.
• World Bank. (2021). Global Economic Prospects. World Bank Reports.
• Statista Research Department. (2023). Economic Data and Statistics. Statista.