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library(readxl)

Applied_QM <- read_excel("Applied QM.xlsx")

Dependent Variable-The variable “Total Homelessness” represents the total number of homeless individuals counted in a specific area (likely a country, region, or city) for each given year. From the data, it measures how the number of people experiencing homelessness changes over time specifically from the year 2018 to 2023.

Independent Variable- The independent variables are factors believed to influence or explain changes in total homelessness. These include economic factors such as the unemployment rate, poverty rate, or average income levels, which can directly impact homelessness by leading to financial hardship and the inability to afford housing. Null hypothesis- There is no effect of economic factors, housing availability, or public health crises on total homelessness.

Alternative hypothesis- Economic factors, housing availability, and public health crises have a significant effect on total homelessness.

Hypothesis explanation-I believe understanding the total number of homeless individuals is crucial for addressing social issues related to poverty and housing. By finding independent variables, such as economic conditions and housing availability, we can investigate the root causes of homelessness and develop more effective interventions. The economic environment, for instance, can directly impact people’s ability to maintain stable housing. A recession or high unemployment rate may push individuals and families into homelessness as they lose their incomes and are unable to afford rising housing costs.

We know the availability of affordable housing plays a key role in preventing homelessness. A decrease in affordable housing units or a sharp increase in housing prices may push more individuals into homelessness, even if their income stays stable. In addition, external factors like disease outbreaks can exacerbate homelessness, as it leads to widespread job loss, eviction delays, and disruptions in social services, which directly affect individuals’ ability to secure and maintain housing.

By testing the null hypothesis versus the alternative hypothesis, I aim to find whether the changes in the number of homeless individuals from year to year are linked to these independent variables. If the alternative hypothesis holds, it suggests that effective social and economic policies addressing these factors could reduce homelessness, improving public welfare. If my null hypothesis holds, it will imply that factors other than those identified in the study are driving changes in homelessness, and a different set of factors would need to be considered for intervention.

hist(Applied_QM$`Total Homelessness`)

hist(Applied_QM$year)

Applied_QM_sqrt<-Applied_QM%>% mutate(Applied_QM)