A local city council’s study found that the correlation between number of liquor stores in a neighborhood and neighborhood crime rates across all city neighborhoods was r = 0.88.Which one of the following statements is true? As the number of liquor stores increase the crime rate will increase in that neighborhood. Closing liquor stores in a neighborhood will help to reduce the crime rate of that neighborhood. Population is a possible lurking variable in this scenario, since areas that are more densely populated are more likely to have higher crime rates. There is a pretty strong positive linear relationship between number of liquor stores and crime rates.

statistics concerning high crime areas and community trust

True or false?Excessive drinking can impact local neighbourhoods/communities and government services. It has been found to be an important factor in increased employment and jobs and health benefits.Select ONE (1) answer. Stuck? Click here to view a pop-up of the content related to this questionTrue, drinking is an important factor in increased employment and jobs and health benefitsFalse, excessive drinking is an important factor in road deaths, property destruction, violent crimes and the decline of physical and mental health of individuals that drink to excess.

Compared to your hometown, how do you think the crime rate in Melbourne measures up?

Neighborhoods A and B are next to each other in a large urban area.  If Neighborhood A is more gentrified than Neighborhood B, which of the following is most likely?A.Housing and rental prices are more affordable in Neighborhood A than in Neighborhood B.B.Residents have resided in Neighborhood A for a longer median duration than in Neighborhood B.C.More lower-income residents have been displaced from Neighborhood A than from Neighborhood B.D.The tax base for Neighborhood A is smaller than the tax base for Neighborhood B.

For a sample of 30 Australian cities, a sociologist studies the crime rate in each city (crime per 100,000 residents) as a function of its poverty rate (in %) and its average income (in $1,000). A portion of the regression results shows that the coefficients for poverty and average income are 54.22 and 5.10, respectively. Based on this information, the alternative hypothesis that X1 has significant effect on Y should be stated as follows. Note: Y = crime rate in each city (crime per 100,000 residents); X1 = poverty rate (in %); and X2 = average income (in $1,000). a. β1 < 0 b. β2 ≠ 0 c. β1 ≠ 0 d. β1 > 0