1. **State the problem:** We have a bivariate data set with an outlier and want to find the correlation coefficient with and without the outlier, then determine if the outlier changes the evidence for a significant linear correlation. 2. **Formula for correlation coefficient $r$:** $$r = \frac{n\sum xy - \sum x \sum y}{\sqrt{(n\sum x^2 - (\sum x)^2)(n\sum y^2 - (\sum y)^2)}}$$ where $n$ is the number of data points, $x$ and $y$ are the variables. 3. **Identify the outlier:** The point $(50.7, 108)$ is the outlier because $x=50.7$ is much larger than other $x$ values. 4. **Calculate $r$ with the outlier:** Using all 15 points, compute sums and apply the formula. This yields approximately $$r_w \approx -0.11$$ (weak negative correlation). 5. **Calculate $r$ without the outlier:** Remove $(50.7,108)$, use remaining 14 points, recompute sums and apply formula. This yields approximately $$r_{wo} \approx 0.68$$ (moderate positive correlation). 6. **Interpretation:** The outlier drastically reduces the correlation coefficient and changes the evidence from moderate positive correlation to weak negative correlation. 7. **Answer to the question:** Yes. Including the outlier changes the evidence regarding a linear correlation. Thus: - Correlation coefficient with outlier $r_w \approx -0.11$ - Correlation coefficient without outlier $r_{wo} \approx 0.68$ - Inclusion of the outlier changes the evidence for a significant linear correlation.