Jazmine's trend line equation is \( y = -0.69x + 17.22 \). The slope of the line is \(-0.69\), which means that as \(x\) increases, \(y\) decreases. This indicates a negative relationship between the two variables in her data set.
The correlation coefficient, which she found to be \(-0.9153\), is a number that tells us how well the linear model fits the data. It ranges from \(-1\) to \(1\). A value close to \(-1\) (like \(-0.9153\)) indicates a strong negative linear relationship. This suggests that the trend line is a good fit for the data.
Based on this information, Jazmine can conclude that the linear model she found is appropriate for her data. The strong negative correlation suggests that the linear relationship she identified is reliable and the trend line accurately represents the pattern in the data. Therefore, she can use this model to make predictions or understand the relationship between the two variables.