This project explores global Gross Domestic Product (GDP) data by nation and continent from 1999 through 2019 in Excel using ANOVA testing and regression analysis. Using this data, we can see that there is sufficient evidence that there are significant differences in mean of national and continental GDP.
GDP Table in the U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table. https://www.ers.usda.gov/webdocs/DataFiles/51832/HistoricalGDPSharesValues.xlsx?v=1906.2
I pulled national GDP data from USDA website, then aggregated the continental GDP data from individual countries as part of the preparation for further analysis. The numbers have been condensed to show GDP in millions ($).
I calculated the means, standard deviation and variances of the sample.
I created a dynamic Q-Q plot that can be updated for each of the 6 continents. Here, we see that North America's sample data has a normal distribution. I also checked for constant variance to ensure the spread was roughly equal for all groups. With this information, I was able to move on to hypothesis testing.
Having met all the requirements, the data was ready for further analysis and hypothesis testing. Using an f-test, I was able to determine the P-value was very small. This tells us that at least one mean continental GDP was different than the others.
Using a means plot, I was able to illustrate that Group 4 (Asia) had a much higher GDP mean than other continents.
- Veronica Broadway (https://bwayvs.github.io/Professional_Portfolio/)
I'm Veronica, a results-driven Data Analyst with expertise in SAP and process improvement. With a background in translating complex requirements into actionable insights, I leverage SQL, data visualization tools, and Agile methodologies to optimize supply chains and drive business decisions. My passion lies in turning data into meaningful business strategies, ensuring organizational alignment, and fostering cross-functional collaboration.
