6 Use the new line of best fit to predict the number of customers at the frozen yogun immediately after school for each given temperature. \( \begin{array}{ll}\text { (a) } 85^{\circ} \mathrm{F} & \text { (b) } 115^{\circ} \mathrm{F} \\ \text { (c) } 10^{\circ} \mathrm{F} & \end{array} \)

When using the line of best fit to make predictions, it’s essential to recognize how different temperatures can influence customer behavior. Typically, warmer temperatures like 85°F might see a sizable surge in customers craving that sweet, creamy delight right after school. In contrast, an extreme like 115°F could also attract more customers seeking refreshment, but can also lead to concerns about heat and comfort levels for outdoor activity. However, grappling with the colder end of the spectrum, such as 10°F, usually leads to fewer customers. People tend to shy away from frozen treats in the chilly weather, so this prediction might yield a very low number, often leaving us to wonder if the shop could instead market warm treats to entice a different kind of customer even when the temperature drops!