Final answer:
Without specific regression results, it is not possible to state definitively whether there is a positive or negative relationship between annual income and borrower interest rates. The correlation coefficient helps to measure the strength and direction of this relationship, with significant correlations being further from 0.
Step-by-step explanation:
To understand the relationship between annual income (annual_inc) and borrower interest rates, we must look at the correlation indicated by a regression analysis. If there is a positive correlation, this would mean that as annual income increases, the borrower interest rates tend to increase as well. Conversely, if there is a negative correlation, an increase in annual income is associated with a decrease in borrower interest rates. According to economic principles, normally, higher income could result in better creditworthiness and potentially lower interest rates, suggesting a negative correlation. However, without specific regression results, we cannot definitively state which relationship is correct.
The correlation coefficient is a statistic that measures the strength and direction of a linear relationship between two variables. According to the given information, for example, the savings function has a negative intercept and a positive slope, which indicates that as income increases, savings increase because the marginal propensity to save is positive. This principle can be applied to understand how interest rates might vary with income if they behaved similarly. The weakest correlation is when the correlation coefficient is closest to 0, irrespective of being positive or negative.