Question 44·Medium·Two-Variable Data: Models and Scatterplots
A chemist measures the amount of a reactant remaining in a solution at regular time intervals. The results are shown below.
| Time (hours) | 0 | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|
| Amount of reactant (mg) | 80 | 60 | 45 | 34 | 26 | 19 |
Based on the data, which of the following types of functions would best model the relationship between time and the amount of reactant remaining?
For questions asking which function type best models a data table, quickly check patterns in the -values as increases by 1: constant first differences mean linear, constant second differences mean quadratic, and roughly constant ratios between consecutive -values mean exponential. Then look at whether the values are going up or down to decide between growth and decay, and eliminate any options that do not match both the pattern (differences vs ratios) and the direction of change.
Hints
Check the changes between rows in the table
Look at how much the amount of reactant changes from one hour to the next. Are those changes (the differences) the same, or are they getting larger or smaller?
Think about linear vs non-linear patterns
If the function were linear, the amount would go down by the same number of milligrams each hour. Does that match what the table shows?
Compare not just differences, but also ratios
Try dividing each amount by the previous one (for example, , , and so on). Are those quotients more similar to each other than the differences are?
Use increase vs decrease to distinguish growth and decay
All of the function types listed can model curves, but some increase over time and some decrease. Think about whether the data in the table are going up or going down.
Desmos Guide
Enter the data as a table
Create a table in Desmos. In the first column (say ), enter the times . In the second column (say ), enter the corresponding amounts .
Visually inspect the plotted points
Look at the scatterplot formed by the points . Decide whether they lie close to a straight line, form a parabolic (U-shaped) curve, or bend smoothly downward in a way that suggests a multiplicative pattern.
Test different regression models (optional)
In the expression lines, you can try different regressions: type y1 ~ m x1 + b for linear, y1 ~ a x1^2 + b x1 + c for quadratic, and y1 ~ a b^{x1} for exponential. Compare which curve matches the plotted points most closely; then match that model type (linear, quadratic, or exponential, and whether it represents increase or decrease) to the best answer choice.
Step-by-step Explanation
Look at how the amount changes each hour
List the amounts and how much they change from one hour to the next:
- Amounts: 80, 60, 45, 34, 26, 19
- First differences (change each hour):
These changes are not equal, so the amount does not decrease by a constant amount each hour.
Eliminate the linear model
A linear function has a constant rate of change, meaning the first differences between -values (amounts) are the same each step.
Here, the first differences are , which are not equal.
So the situation cannot be modeled well by a linear function with a constant rate of decrease (choice A).
Check whether a quadratic model fits
For equally spaced -values (time), a quadratic function has second differences that are constant.
From the first differences , find the second differences:
The second differences are , which are not equal, so a quadratic model (choice B) does not fit well either.
Compare ratios between consecutive amounts
When increases by the same amount each time (here, 1 hour), an exponential relationship shows up as almost constant ratios between consecutive -values.
Compute the ratios of each amount to the previous one:
These ratios are all close to about . That means each hour you multiply by roughly the same factor, so the amount is changing by about the same percentage each hour, not by the same absolute amount.
Decide between growth and decay and choose the model
Because the amounts are decreasing over time and the ratios between consecutive amounts are roughly constant, the data are best modeled by an exponential decay function with a constant percent decrease, which corresponds to answer choice C.