In order to learn more about the relationship between the vulnerability index of a given district and its population, I looked at the Bangladesh Floods - August 2017 - Flooding levels & Vulnerability dataset available on data. world. This dataset included variables such as the landtype of the districts, the division within which the districts reside, the population, the poverty rate, the vulnerability index, etc. I decided to primarily focus on the hilly, yet wet districts of the Chittagong division– my explanatory variable being the vulnerability index (0-10) of these districts and my response variable being their human population in the month of August 2017. The vulnerability index has two primary sources– the poverty incidence of the district (likelihood to live below $2.50 a day) and the deprivation index including other socioeconomic variables such as displacement due to flooding, loss of farmland and cattle, etc.