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The countries with the 2 highest GDPs, China and the US, are in the top 4 largest countries. India, #5 in GDP, is #7 in area. I guarantee that if you plot the
by dwaltrip 3y ago
The countries with the 2 highest GDPs, China and the US, are in the top 4 largest countries. India, #5 in GDP, is #7 in area.
I guarantee that if you plot the countries of the world by GDP and area, you will see a trend line.
It also makes sense. More area = higher chance of larger population and more natural resources. And more space to carry out economic activities with said people and resources.
Edit: I just queried Wolfram Alpha about this. It generated a plot for me, which shows what I expected. Check it out: https://www.wolframalpha.com/input?i=list+of+countries+with+area+and+gdp https://www.wolframalpha.com/input?i=list+of+countries+with+...
Edit2: bonus feature, GPT-4 wrote me a script to plot this also, check it out: https://chat.openai.com/share/ffa45c61-8b7a-44e1-b757-041f31df5099 https://chat.openai.com/share/ffa45c61-8b7a-44e1-b757-041f31...
- deleted 3y ago[deleted]
- jgeada 3y agoSeriously, a loglog plot? Even in that, there is a seriously wide dispersion to your correlation. And then look at the same data on a linear plot.
- dwaltrip 3y agoThe linear plot in that Wolfram link is messed up. It doesn't show all the data (caps out at 800 billion GDP). Here's a corrected linear plot, from the script that I linked (commenting out the log-log scaling): https://ibb.co/9bBgwH8 https://ibb.co/9bBgwH8 There is clearly a correlation, even on linear. It's a little messy, but it's undeniably there. The starting point for this discussion was about the relationship between a country's size and population and it's power and influence. The correlation between area and GDP demonstrates that there is a meaningful relationship. Btw, what is your specific complaint about a log-log plot? Country data points for area and GDP span many orders of magnitude, which makes it harder to visualize any patterns on a linear plot. I also don't understand your point about the dispersion. The correlation and trend is pretty clear. No one said the correlation was 99%. Edit: I've calculated Pearson's correlation coefficient for this data [1]. The result is 0.82, which indicates a strong positive correlation. [1] https://en.wikipedia.org/wiki/Pearson_correlation_coefficient https://en.wikipedia.org/wiki/Pearson_correlation_coefficien...
- zimpenfish 3y ago> The result is 0.82, which indicates a strong positive correlation. datamash gave me 0.52 for Pearson. Which is "eh, maybe".
- deleted 3y ago[deleted]
- erwald 3y agoThat's weird, are you looking only at the top 10 countries? I've reproduced dwaltrib's results using World Bank data on 251 countries, and I get a Pearson's r of 0.82 and a p value of 5.6e-61 (!). I.e. a strong correlation, with high confidence. It makes sense too -- larger countries generally have more people, and more people generally generate more economic activity. Code if you want to try yourself: import pandas as pd gdp = pd.read_csv("~/Downloads/API_NY.GDP.MKTP.CD_DS2_en_csv_v2_5551501.csv").set_index("Country Name") land_area = pd.read_csv("~/Downloads/API_AG.LND.TOTL.K2_DS2_en_csv_v2_5552158.csv").set_index("Country Name") gdp["GDP"] = gdp["2020"] gdp["Land"] = land_area["2020"] gdp = gdp.dropna(subset=["GDP", "Land"]) from scipy import stats print(stats.pearsonr(gdp.Land, gdp.GDP)) #+RESULTS: : PearsonRResult(statistic=0.8151313879150333, pvalue=5.621180589722219e-61)
- dwaltrip 3y agoThanks for double checking. I ran out of steam on the convo heh.