To determine the best fitting model for the given dataset, we need to analyze the pattern of growth in the y-values relative to the x-values.
The given data points are:
- \( (0, 5) \)
- \( (1, 35) \)
- \( (2, 245) \)
- \( (3, 1715) \)
- \( (4, 12005) \)
- \( (5, 84035) \)
Analysis:
At first glance, we can observe that the y-values are increasing rapidly as the x-values increase. This suggests that the relationship might not be linear since linear functions grow at a constant rate.
To further analyze the growth, we can look at the ratios of consecutive y-values:
- From \( 5 \) to \( 35 \): \( \frac{35}{5} = 7 \)
- From \( 35 \) to \( 245 \): \( \frac{245}{35} = 7 \)
- From \( 245 \) to \( 1715 \): \( \frac{1715}{245} = 7 \)
- From \( 1715 \) to \( 12005 \): \( \frac{12005}{1715} = 7 \)
- From \( 12005 \) to \( 84035 \): \( \frac{84035}{12005} = 7 \)
Conclusion:
Since the ratios of consecutive y-values are constant (specifically, each value is multiplied by 7 to get the next value), this indicates that the function is indeed exponential.
The pattern can be translated into the function model:
\[ f(x) = 5 \cdot 7^x \]
This matches one of the response options:
f(x) = 5(7^x) and exponential function
So, the correct answer is:
f(x) = 5(7^x) and exponential function.