1. The problem is to find the missing Google Demand value for June 10 given the previous data points of Google Price and Demand. 2. We observe from the data that as the Google Price decreases, the Google Demand increases, suggesting a negative correlation. 3. To estimate the missing demand, we can use linear interpolation or linear regression between Price and Demand. 4. Using the two closest known points before June 10: (543, 170) and (550, 155), we calculate the slope $m$ of the line: $$m = \frac{170 - 155}{543 - 550} = \frac{15}{-7} = -\frac{15}{7} \approx -2.1429$$ 5. The linear equation relating Demand $D$ to Price $P$ is: $$D - 170 = m (P - 543)$$ 6. Substitute $m$ and $P=542$ to find $D$: $$D - 170 = -\frac{15}{7} (542 - 543) = -\frac{15}{7} (-1) = \frac{15}{7} \approx 2.1429$$ 7. Therefore, $$D = 170 + 2.1429 = 172.1429$$ 8. Rounding to the nearest whole number, the estimated Google Demand on June 10 is 172. Final answer: The missing Google Demand value for June 10 is approximately 172.