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Answer :
To solve this question, we need to determine which temperature most accurately matches the modeled prediction for a specific cooling time using the function [tex]\( f(t) = 349.2 \times (0.98)^t \)[/tex]. This function models the relationship between [tex]\( t \)[/tex], the time in minutes, and the oven temperature [tex]\( f(t) \)[/tex].
Here's how we can approach the solution step by step:
1. Calculate Temperature for Each Time: Use the given function to calculate the oven temperature for each of the cooling times provided (5, 10, 15, 20, and 25 minutes).
2. Compare Calculated Temperatures with Given Data: Compare the modeled temperatures for each time with the provided temperatures in the chart to determine how accurately the function predicts each measured temperature.
3. Identify Temperature With Smallest Difference: For each time, calculate the difference between the modeled temperature and the given temperature. The goal is to find the smallest difference, which indicates the most accurate prediction.
- At 5 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^5 \)[/tex]
- Given: 315°F
- At 10 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{10} \)[/tex]
- Given: 285°F
- At 15 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{15} \)[/tex]
- Given: 260°F
- At 20 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{20} \)[/tex]
- Given: 235°F
- At 25 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{25} \)[/tex]
- Given: 210°F
4. Conclusion: By calculating the differences, we find that the temperature prediction for 10 minutes (approximately 285.32°F) is the most accurate, closely matching the given temperature of 285°F.
Thus, the temperature for which the model most accurately predicts the time spent cooling is 285°F.
Here's how we can approach the solution step by step:
1. Calculate Temperature for Each Time: Use the given function to calculate the oven temperature for each of the cooling times provided (5, 10, 15, 20, and 25 minutes).
2. Compare Calculated Temperatures with Given Data: Compare the modeled temperatures for each time with the provided temperatures in the chart to determine how accurately the function predicts each measured temperature.
3. Identify Temperature With Smallest Difference: For each time, calculate the difference between the modeled temperature and the given temperature. The goal is to find the smallest difference, which indicates the most accurate prediction.
- At 5 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^5 \)[/tex]
- Given: 315°F
- At 10 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{10} \)[/tex]
- Given: 285°F
- At 15 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{15} \)[/tex]
- Given: 260°F
- At 20 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{20} \)[/tex]
- Given: 235°F
- At 25 minutes:
- Modeled: [tex]\( 349.2 \times (0.98)^{25} \)[/tex]
- Given: 210°F
4. Conclusion: By calculating the differences, we find that the temperature prediction for 10 minutes (approximately 285.32°F) is the most accurate, closely matching the given temperature of 285°F.
Thus, the temperature for which the model most accurately predicts the time spent cooling is 285°F.
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