In order to earn a little extra money, Anna set up a lemonade stand on her block over the summer. Suspecting that there might be a relationship between the temperature and the amount of lemonade she sells, Anna recorded the day’s high temperature and the number of cups of lemonade she sold for $10$ days. After plotting her results, Anna noticed that the relationship between her two variables appeared fairly linear, so she used her data to calculate a least squares regression equation. Below is a scatterplot displaying the data she collected along with a computer output of her regression results. [[☃ image 1]] Predictor | Coef | SE Coef |T| P| - | - | - **Constant** | $-33.704$ | $0.134$ | $20.678$ | $0.000$ **Temperature**| $0.6012$| $0.057$| $4.0456$|$0.004$ $S=1.926\quad R\text{-}Sq=67.168\%\quad R\text{-}Sq (adj) = 60.581\%$ Anna decides to head out one more day to sell her lemonade. Although it was a scorcher, $98$ degrees, she was only able to sell $10$ cups of lemonade. **What affect would adding this new data point have on the correlation coefficient $(r)$?** [[☃ radio 1]]