To determine the type of functions that best model the success rates for Method A and Method B, we can analyze the data provided.
Method A
The values for Method A are:
- Day 0: 1
- Day 1: 3
- Day 2: 5
- Day 3: 7
- Day 4: 9
- Day 5: 11
- Day 6: 13
- Day 7: 15
The differences between consecutive days are constant:
- (3-1) = 2
- (5-3) = 2
- (7-5) = 2
- (9-7) = 2
- (11-9) = 2
- (13-11) = 2
- (15-13) = 2
Since the differences are constant, this indicates that Method A can be modeled with a linear equation.
Method B
The values for Method B are:
- Day 0: 7
- Day 1: 3
- Day 2: 1
- Day 3: 1
- Day 4: 3
- Day 5: 7
- Day 6: 13
- Day 7: 21
The differences between consecutive days are not constant:
- (3-7) = -4
- (1-3) = -2
- (1-1) = 0
- (3-1) = 2
- (7-3) = 4
- (13-7) = 6
- (21-13) = 8
The differences themselves increase as we go further in the days, which suggests that the function has a more complicated relationship than linear — likely quadratic or possibly exponential. However, a closer analysis of the pattern leads to the interpretation that Method B is best modeled by a quadratic equation since its differences increase in a systematic fashion.
Based on this analysis, the correct response is:
Method A should be modeled with a linear equation, and Method B should be modeled with a quadratic equation.