Secondary 3 & 4 Additional Mathematics · Study guide
Choose what to plot, interpret gradient and intercept, and return to the original variables. Connect algebraic, power and exponential models to straight-line graphs.
A straight line in new variables
A curved relationship can become a straight line when we change what goes on the axes. In , the capital letters represent the quantities plotted; they need not be the original and . A graph of against has horizontally and vertically.
This chapter is taught in Sec 3 and revisited in Sec 4. Start with algebraic rearrangement, then use logarithms for power and exponential models. Revise indices and logarithms if the logarithm laws are unfamiliar.
Why it works. After an equivalent rearrangement, every valid original pair gives a transformed pair satisfying the same straight-line equation. Thus its gradient and intercept encode the original constants. The transformation must be defined: denominators cannot be zero and real logarithms require positive arguments.
- Rearrange to , where and contain only constants.
- State both axes and match to the constants.
- Read or calculate the gradient using transformed coordinates: .
- Solve for the original constants; undo any logarithm or reciprocal.
- Check by substituting into the original model, retaining its restrictions.
| Relationship | Vertical , horizontal | Gradient and intercept |
|---|---|---|
| , | ||
| , | ||
| , | ||
| , | ||
| , |
Here means logarithm to base 10. Natural logarithms also work for power and exponential models, but use the same base consistently and undo with . The constant exponential case gives a horizontal line.
Algebraic rearrangements and restrictions
Example 1: dividing by a variable
For , the graph of against passes through and . Find and explain what happens at .
The given points are , so
Therefore . At , and , agreeing with the graph. The original formula gives at , but is undefined there. The extended straight line has intercept 5; this is not an observed transformed point at .
Example 2: reciprocal axes
Let , where . The graph of against passes through and . Find the model.
Hence , with . At , , so . Although the extended line meets the horizontal axis at , there is no original finite value of there: has no finite solution.
A transformation can exclude values. Dividing by gives , so one possible graph is against , for . Do not divide by a variable without stating its restriction. If matters, handle it in the original equation.
Power and exponential models
Example 3: a power law
For , , a graph of against passes through and . Find , then when .
Taking logarithms uses the product and power laws:
The model is on the stated domain . Thus . The transformed point means , and checks it.
Example 4: exponential decay
The graph of against for passes through and . Find the model and at .
Therefore , , and
Since , the model decreases as increases but stays positive. At , . At , , whose base-10 logarithm is 1 as required. A negative logarithmic gradient does not mean a negative exponential base.
Example 5: subtract the background before taking logs
A cooling model is , with and minutes. On the graph of against , the line passes through and . Find the initial temperature and the time until it halves.
Thus , and . Half the initial temperature is 50, so
Substitution gives . The excess above 20 halves every 10 minutes, but the temperature itself does not. For every finite , , so the logarithm is defined; the model approaches 20 without reaching it.
Explore the meaning of a logarithmic gradient
For , the transformed graph is . In the default case, and : increasing by one adds to , and multiplies by . Changing the intercept changes the starting scale; changing the gradient changes the growth factor.
Gradient k = 0.50; intercept ln A = 1.00. A = e ≈ 2.718. Each increase of 1 in x multiplies y by e^0.5 ≈ 1.649. At x = 2, ln y = 2 and y = e² ≈ 7.389. The model grows.
From a table to a useful line of best fit
Calculate the transformed coordinates first. Use clear scales and axis labels, plot every point, then draw one straight line that represents the trend. For measurement data, do not join points one by one or force the line through the origin. Retain extra digits in calculations; round only the plotted table as needed.
Choose two well-separated points on your drawn line to estimate the gradient. They need not be data points. Graph readings are estimates, so use and a precision supported by the scale. A prediction within the measured range is interpolation; outside it is extrapolation and depends more strongly on whether the model remains appropriate.
Example 6: fit data and undo the logarithm
Measurements approximately follow :
| x | 0 | 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|
| y | 3.02 | 4.45 | 6.78 | 10.03 | 15.20 |
| lg y (3 d.p.) | 0.480 | 0.648 | 0.831 | 1.001 | 1.182 |
Plot against . A suitable line of best fit passes approximately through and . Thus
To estimate at , read . Hence . The answer is not 0.742. As a check, the line predicts at , close to the measurement 6.78; it need not reproduce every measurement exactly.
Example 7: recognise an anomalous reading
Positive data are expected to follow . The readings are , , , and . Four lie on the line . Identify the inconsistent reading and estimate its corrected , assuming is correct.
Undoing logarithms gives . The predictions at are respectively , which match. At , the recorded value 2 differs from
Equivalently, the line predicts , not . Thus is anomalous, and the model suggests . A data point away from a fitted line should be investigated; the graph alone does not establish which measurement caused the discrepancy unless the question states an assumption.
Solve a new equation on the same axes
Example 8: translate the second condition too
For , measurements follow . A graph plots against . Use a second straight line to find where .
Multiplying the model by gives
The new condition is equivalent to , since here . Draw the horizontal line . At its intersection with the model,
The exact illustrative model gives ; on a measured graph these would be estimates. Check: . The horizontal coordinate read from the transformed graph is , not .
For a logarithmic graph, transform the extra condition with the same logarithm and the same axes. For example, if , , then becomes . Draw that line and recover the original variable from the intersection.
Common mistakes and a final check
- Using original coordinates for a transformed gradient. Convert both axes first; a point means .
- Confusing the model types. For , log both variables. For , log only the vertical variable when the horizontal variable is .
- Reading the constant straight from the intercept. If , recover ; if , recover .
- Taking logarithms of a sum term by term. . First isolate the exponential part.
- Ignoring missing points. A transformed straight line can extend through coordinates that do not correspond to valid original values.
- Overstating accuracy. Use a sensible line of best fit, well-separated line points and approximate answers for graph estimates.
Before finishing, write the original model with all constants recovered, check one original data pair or stated transformed point, and state any restriction introduced by division or logarithms.
Continue with topic practice
Choose the bank for your current year: Sec 3 Linear Law questions and worked solutions or Sec 4 Linear Law questions and worked solutions. Work on paper before opening a solution.