reply to the following statement in agreement with an in text ciation in the answer. My criminal justice inquiry explores the relationship between rainfall per day and crime. The independent variable (x) is the amount of rainfall (measured per day in inches), and the dependent variable (y) is the amount of crime (measured by the number of reported incidents per day). A Pearson's r value of -0.70 suggests that there is a negative correlation between the two variables, meaning that as rainfall increases, crime decreases. The coefficient of determination tells us the amount of the variance in the dependent variable that can be explained by the regression model. In this case, an R^2 value of 0.49 means that the amount of rain predicts about 49% of the variance in the crime rate. This determination can temper our confidence in the regression model's prediction of y, since values near 1 mean that the predicted value of y is going to be closer to the observed value of y. Since this coefficient of determination is farther from 1, I would not expect the actual, measured amount of crime to match the predicted amount of crime