Data Analysis Project: Demonstrate Your Skills

Data Analysis Project 3for This Project You Will Demonstrate Competenc

Research and analyze five distinct research questions based on data from the categories of the National Economy, Wealth Income and Poverty, Business Statistics, Labor Statistics, and Government. Formulate the questions with causal phrasing. For three questions, create Excel datasets, corresponding graphs, and compile statistical metrics, generating each graph with a different type and calculating metrics such as mean, median, variance, standard deviation, coefficient of variation, range, percentiles, quintiles, and skewness. For one question, develop a single-variable regression analysis using at least 30 data points, include a scatterplot with trendline and R-squared value, and conduct regression analysis to interpret the significance of coefficients. For another, perform a multiple-variable regression with at least six independent variables, again using at least 30 data points, and analyze the statistical significance of coefficients. Develop PowerPoint slides for each question, illustrating key graphs, metrics, regression results, with bullet points and links to data sources. Include slides explaining regression R-squared, significance, insignificance, and interpret coefficients' impacts on the dependent variable. Ensure professional presentation quality. Submit both Excel and PowerPoint files by the deadline, with adherence to academic integrity.

Paper For Above instruction

Understanding the intricate dynamics of the economy and its various components requires meticulous research and comprehensive data analysis. This project encapsulates the essence of applied economic research by guiding students through data collection, statistical analysis, regression modeling, and professional presentation. The targeted approach ensures not only a robust understanding of economic variables but also fosters skills in data interpretation, visualization, and effective communication, vital for any aspiring economist or analyst.

The first step involves generating five research questions across key economic categories: the national economy, wealth income and poverty, business statistics, labor statistics, and government data. All questions are to be phrased causally, such as "Higher taxes cause lower GDP" or "Increased worker productivity increases savings." This formulation aims to uncover cause-and-effect relationships rooted in economic theory and empirical data. For three of these questions, students will compile datasets in Excel, compute multiple statistical metrics—mean, median, variance, deviation, coefficient of variation, range, percentiles, quintiles, and skewness—and visualize the data with distinct graph types. These statistical measures elucidate the distribution, variability, and skewness of the data, providing foundational insights into the variables studied.

Next, a single-variable regression analysis is to be performed on one of the questions, with a dataset containing at least 30 data points. The process entails creating a scatterplot with a trendline, highlighting the best functional form based on R-squared, and analyzing regression output to interpret the significance of coefficients. The statistical significance helps determine whether variables meaningfully impact the dependent variable. The regression analysis offers quantitative insights into the strength and nature of relationships, capturing how changes in the independent variable influence the dependent variable.

Complementarily, a multiple-variable regression involves at least six independent variables believed to influence a dependent variable. This analysis, using a dataset with minimally 30 observations, aims to quantify the combined effect of multiple factors. The model's R-squared value indicates how well the model explains variability in the dependent variable. Significance tests on coefficients elucidate which variables have statistically significant impacts, guiding interpretations about the relative importance of each factor.

All findings are to be summarized in a professional PowerPoint presentation, with at least one slide per research question that visually displays the key graphs and metrics. Regressions warrant additional slides to answer specific questions: the meaning of R-squared, the significance or insignificance of coefficients, and how each significant coefficient affects the dependent variable. Clear, precise language and proper economic denominations are crucial—e.g., "each additional square foot increases home value by $123." The presentation should exemplify clarity, accuracy, and visual appeal to effectively communicate findings.

Finally, the submission involves uploading both Excel datasets, statistical outputs, regression analyses, and the PowerPoint presentation via the designated platform by the deadline. Both components are mandatory for a comprehensive evaluation. Emphasizing academic integrity, students must generate original graphs and analyses, avoiding plagiarism or copying from online sources or peers. The project not only assesses technical skills in data analysis but also emphasizes professional communication, critical thinking, and ethical research practices within the realm of economics.

References

• Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics (5th ed.). McGraw-Hill.
• Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.
• Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics (3rd ed.). Pearson.
• Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach (6th ed.). Cengage Learning.
• Newbold, P., Carlson, W. L., & Thorne, B. (2013). Statistics for Business and Economics (8th ed.). Pearson.
• Mitchell, M. (2017). Data-Driven Economics: Mastering Quantitative Methods. Routledge.
• Chen, S., & Liu, H. (2020). Applied Econometrics with R. CRC Press.
• Friedman, M., & Schwartz, A. J. (1963). A Monetary History of the United States. Princeton University Press.
• United States Census Bureau. (n.d.). Data & Research. https://www.census.gov/data.html
• Bureau of Labor Statistics. (n.d.). Data Tools & Tables. https://www.bls.gov/data/