Let's analyze each prediction based on the given sales data and determine which one is most reasonable:
1. Prediction A: The shop will sell twice as many tulips as daisies.
- According to today's sales, there are 9 daisies sold.
- If the shop were to sell twice as many tulips as daisies, they would sell [tex]\( 2 \times 9 = 18 \)[/tex] tulips.
- Today's sales data show that 11 tulips were sold.
- This prediction implies the tulip sales would change significantly from 11 to 18, which may not be very reasonable without further context.
2. Prediction B: The shop will sell more roses than tulips.
- Today's sales for roses are 18, and for tulips, it is 11.
- Since 18 (roses) is greater than 11 (tulips), the shop selling more roses than tulips is consistent with today's sales data.
- Thus, this prediction is reasonable because it aligns with the current trend.
3. Prediction C: The shop will sell seven more tulips than roses.
- To satisfy this prediction, if today's roses are 18, the tulips should be 18 + 7 = 25.
- Currently, the shop sells 11 tulips, which is significantly less than 25.
- This prediction would require a drastic and unlikely increase in tulip sales, making it less reasonable.
4. Prediction D: The shop will sell equal numbers of roses and lilies.
- Today's sales show 18 roses and 12 lilies.
- For this prediction to hold, the sales numbers would have to shift so lilies match roses.
- Given the difference between the current sales numbers, this prediction seems improbable without additional reasons for such a change.
Based on today's sales data, the most reasonable prediction about tomorrow's flower sales is:
B. The shop will sell more roses than tulips.
