Assignment 6

Part 1
 After being given a set of data that included the percentage of children who receive free lunch and crime rate per 100,000 people. In order to see if the two variables were related, more specifically, if the percentage of children who receive free lunch directly affects the crime rate of the area. The two variables have a very low R Square value. This means that the two variables are not strongly related and there is a large amount of variance among the data. Given the model below, for example, if an area of town was identified as having 30 percent of children having free lunch the crime rate would be 72.369. Given a significance level of .509, we would reject the null hypothesis as there is not a strong relationship between the two variables.

Figure 1 Relationship between Kids who receive free lunch and crime rate

Part 2
The city of Portland is interested in their ability to adequately responded to 911 calls. A company is interested in building a new hospital in the city and would like to know where to optimally place the new hospital. To begin my analysis I was given statistical data on Portland's census tracts for the following variables, number of 911 calls, jobs, renters, people without high school degree, alcohol sales, unemployment, foreign born, median income and number of college graduates. Looking for connections between different variables and their connection to the number of 911 calls. I chose to look at three variables unemployment, median income and alcohol sales. Looking at the connection between alcohol sales and 911 calls (Figure 2), the R Square value was .152. This means that alcohol sales are a poor predictor of 911 calls.

Figure 2 Relationship between 911 calls and alcohol sales

After looking a alcohol sales I examined median income (Figure 3). Median income also had a small R Squared vale being .163 meaning that alcohol sales are also a poor predictor of 911 calls. For every alcohol sale there is only a .001 change in the number of 911 calls.

Figure 3 Relationship between 911 and median income

Looking at unemployment rates and 911 calls (Figure 4), they have a R Square value of .543. This means that there is a fairly strong connection between the variables, especially when compared to the previous two variables. For every one 1.6 unemployed people per census tract, we on average one 911 call.

Figure 4 Relationship between 911 calls and unemployment

Looking at the data spatially, two maps were created. One being the number of 911 calls per census tract and the other being the standardized residuals between unemployment and 911 calls. Looking at the number of calls per census tract (Figure 5), census tracts 62 and 79 have highest number of 911 calls. Areas with the second highest amount of 911 calls are found just north of census tract 62. Looking at the standard residual map (Figure 6) the areas in red are areas with high amounts of residual. This means that there are other variables that are also influencing the amount of 911 calls per census tract. The beige, salmon and grey areas are areas that can be more accurately explained by unemployment rates as they have lower residuals.

Figure 5 Number of calls per census tract

Figure 6 Standardized residuals between 911 calls and unemployment