#urbanlevel

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#urbanlevel #dessindujour #dessinateur #dessincouleurw #dessinart #dessinartistique #art #artistsoninstagram #artistic #artist #encresurpapier #rapidograph #isograph #encrerotring #rotring #a4 https://www.instagram.com/p/CVQYwstsVGP/?utm_medium=tumblr

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Variables: Urbanlevel(level of urbanization), Gdpclevel(level of Gross Domestic Product per capita)

First I divided the degree of urbanization into 2 levels, which are low urbanization and high urbanization, and I also divided the degree of Gross Domestic Product (GDP) per capita in to 4 levels, including low, normal, above average, and high income.

To see whether these two variables are independent or not, I conducted the chi-square test. Here is the result.

From the table, it seems that for the low urbanization countries, they are more likely to have a low GDP per capita, say 95.74% low GDP per capital countries are low urbanization countries. While, for the high urbanization countries, they are less likely to have a low GDP per capita, say 4.26%. Also, we can find that the p value is small enough to reject null hypothesis and accept the alternative which is there are some relationship between level of urbanization and level of Gross Domestic Product per capita.

However, so far it’s hard to tell which groups have different urbanization levels. So by using post hoc test, I can better understand the difference. According to Bonforroni Adjustment, the p-value should be changed into 0.0083 (0.05/6=0.0083) to lower the probability of type one error. In comparison, I noticed that for each group, the degree of urbanization is not independent to GDP per capita no matter in which level of GDP per capita because the p-values are all smaller than 0.0083. As a result, for each group, the degree of urbanization is likely to have some relationship with the GDP per capita.