To create an exponential model for the given dataset, we need to identify the pattern in the y-values corresponding to the x-values.
Let's analyze the y-values:
- When \( x = 0 \), \( y = 6 \)
- When \( x = 1 \), \( y = 12 \)
- When \( x = 2 \), \( y = 24 \)
- When \( x = 3 \), \( y = 48 \)
We can see that as \( x \) increases by 1, \( y \) doubles:
- From \( 6 \) to \( 12 \) (doubles)
- From \( 12 \) to \( 24 \) (doubles)
- From \( 24 \) to \( 48 \) (doubles)
This indicates that the relationship can be modeled by an exponential function of the form:
\[ f(x) = a \cdot b^x \]
From the data, we see that when \( x = 0 \), \( f(0) = 6 \), thus:
\[ a = 6 \]
The doubling happens for each increment of \( x \), which means \( b = 2 \).
Thus, we can write the function as:
\[ f(x) = 6(2)^x \]
Therefore, the correct exponential model is:
\[ f(x) = 6(2)^x \]
So the response is:
f(x) = 6(2)^x