To determine which dataset represents a quadratic function, we can inspect the patterns of changes in the y-values relative to the x-values. A quadratic function has a specific structure in its outputs where the second differences of the y-values are constant.
Let's examine each dataset more closely:
Dataset A
| x | y | |---|----| | 1 | 25 | | 2 | 32 | | 3 | 41 | | 4 | 52 |
First differences:
32 - 25 = 7
41 - 32 = 9
52 - 41 = 11
Second differences:
9 - 7 = 2
11 - 9 = 2
Second differences are constant. Possible quadratic function.
Dataset B
| x | y | |---|-------| | 1 | 0.5 | | 2 | 0.25 | | 3 | 0.125 | | 4 | 0.0625|
First differences:
0.25 - 0.5 = -0.25
0.125 - 0.25 = -0.125
0.0625 - 0.125 = -0.0625
The differences become increasingly smaller and do not show a constant second difference. Not a quadratic function.
Dataset C
| x | y | |---|------| | 1 | -23 | | 2 | -26 | | 3 | -29 | | 4 | -32 |
First differences:
-26 - (-23) = -3
-29 - (-26) = -3
-32 - (-29) = -3
Second differences: -3 - (-3) = 0
Constant first differences indicate a linear function, not quadratic.
Dataset D
| x | y | |---|------| | 1 | 4 | | 2 | 16 | | 3 | 64 | | 4 | 256 |
First differences:
16 - 4 = 12
64 - 16 = 48
256 - 64 = 192
Second differences:
48 - 12 = 36
192 - 48 = 144
The differences are not constant. Not a quadratic function.
Conclusion:
Dataset A is the only dataset that represents a quadratic function as evidenced by its constant second differences.