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The problem asks for the "average rate of change from 3 to 6 storms" using the data in the table.

We can calculate the average rate of change as:

\[
\text{Average rate of change} = \frac{\text{Change in bread price}}{\text{Change in number of storms}}
\]

From the table:
- At 3 storms, the bread price is \$2.30.
- At 6 storms, the bread price is \$2.68.

Now, let's calculate the change in bread price and number of storms:
\[
\text{Change in bread price} = 2.68 - 2.30 = 0.38
\]
\[
\text{Change in number of storms} = 6 - 3 = 3
\]

Now, calculate the average rate of change:
\[
\text{Average rate of change} = \frac{0.38}{3} = 0.1267 \approx 0.13
\]

However, the options provided are slightly different, so checking closely, it seems like the closest answer should be **0.13** (if available) or an approximation like **0.09**, depending on rounding.

Would you like further details or explanations on this?

Here are five related questions to explore:
1. How do we interpret the average rate of change in a real-world context?
2. How does a positive correlation between storms and bread prices impact predictions?
3. How can we visualize this data using a graph?
4. What happens to the rate of change when the data shows non-linear relationships?
5. How would the calculation change if we had more precise values?

**Tip:** When calculating rates of change, ensure both the numerator and denominator represent changes over the same interval length.

The price of bread has been increasing over the last month. Brian believes there is a positive correlation between the number of predicted storms and the price of bread. Use the table to determine the average rate of change from 3 to 6 storms.

This problem asks students to calculate the average rate of change between the price of bread and the number of predicted storms. Using the formula for rate of change, the solution demonstrates how to determine the change in bread prices from 3 to 6 storms.

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