To determine the type of function that best fits the dataset provided, we can analyze the progression of the y-values based on the x-values.
Here are the y-values:
- When \( x = 0 \), \( y = 5 \)
- When \( x = 1 \), \( y = 35 \)
- When \( x = 2 \), \( y = 245 \)
- When \( x = 3 \), \( y = 1715 \)
- When \( x = 4 \), \( y = 12005 \)
- When \( x = 5 \), \( y = 84035 \)
As we analyze the pattern:
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Check for Linear: For a linear function, the differences between consecutive y-values should be constant. Calculating these differences shows they are not constant (35 - 5, 245 - 35, etc.), indicating that this is not a linear function.
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Check for Quadratic: For a quadratic function, the second differences of y-values (the differences of the differences) should be constant. However, due to the rapid increase in y-values (the growth appears to be multiplicative rather than additive), it is likely not quadratic as well.
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Check for Exponential: For an exponential function, the ratio of consecutive y-values should be approximately constant. Calculating these ratios:
- \( 35 / 5 = 7 \)
- \( 245 / 35 = 7 \)
- \( 1715 / 245 = 7 \)
- \( 12005 / 1715 = 7 \)
- \( 84035 / 12005 = 7 \)
Since the ratios are consistently 7, this indicates that the function fits the form \( f(x) = a \cdot b^x \), where \( a = 5 \) and \( b = 7 \). Therefore, the model can be expressed as:
\[ f(x) = 5 \cdot 7^x \]
Based on this analysis, the correct response is:
f(x) = 5(7)^x and exponential function.