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Logarithmic graphs and modellingEdexcel International A Level Maths: Revision notes

Section 1

Reducing y=axny=ax^n to a straight line

A relationship of the form y=axny=ax^{n} is not linear, but taking logarithms of both sides (base 1010, written lg⁡\lg, or ln⁡\ln) gives a linear equation. Using the laws of logarithms: lg⁡y=lg⁡a+nlg⁡x.\lg y=\lg a+n\lg x. Compare with Y=mX+cY=mX+c where Y=lg⁡yY=\lg y and X=lg⁡xX=\lg x. A graph of lg⁡y\lg y against lg⁡x\lg x is a straight line with gradient nn and vertical intercept lg⁡a\lg a. The two logarithm laws used are lg⁡(AB)=lg⁡A+lg⁡B\lg(AB)=\lg A+\lg B and lg⁡(Ap)=plg⁡A\lg(A^{p})=p\lg A.

Key termsgradientintercept
Common mistake

Giving the intercept as aa. The graph shows lg⁡a\lg a, so a=10intercepta=10^{\text{intercept}}.

Section 2

Reducing y=kbxy=kb^x to a straight line

For an exponential relationship y=kbxy=kb^{x}, taking logarithms gives lg⁡y=lg⁡k+xlg⁡b.\lg y=\lg k+x\lg b. Now Y=lg⁡yY=\lg y is plotted against X=xX=x (not lg⁡x\lg x). The line has gradient lg⁡b\lg b and vertical intercept lg⁡k\lg k. Hence b=10gradientb=10^{\text{gradient}} and k=10interceptk=10^{\text{intercept}}. Quick test: if the data give a straight line against lg⁡x\lg x, the relationship is a power law; if a straight line against xx (when lg⁡y\lg y is plotted), it is exponential.

Key termsexponential relationshippower law
Exam tip

Write down the two forms side by side: lg⁡y=lg⁡a+nlg⁡x\lg y=\lg a+n\lg x and lg⁡y=lg⁡k+xlg⁡b\lg y=\lg k+x\lg b. The only difference is lg⁡x\lg x against xx.

Section 3

Estimating constants from a graph or data

Pick two points on the line (or use two data points converted to logarithms) and find the gradient =ΔYΔX=\frac{\Delta Y}{\Delta X}. Then find the intercept using c=Y−mXc=Y-mX with one point. Worked example (power law): the line passes through (0.2,0.8)(0.2,0.8) and (0.6,1.6)(0.6,1.6). Gradient =0.80.4=2=\frac{0.8}{0.4}=2, so n=2n=2. Intercept =0.8−2(0.2)=0.4=0.8-2(0.2)=0.4, so a=100.4=2.51a=10^{0.4}=2.51. Worked example (exponential): the line passes through (2,3.4)(2,3.4) and (6,4.2)(6,4.2). Gradient 0.20.2 gives b=100.2=1.58b=10^{0.2}=1.58; intercept 3.4−2(0.2)=3.03.4-2(0.2)=3.0 gives k=1000k=1000.

Common mistake

Forgetting to convert back: the gradient or intercept is a logarithm until you apply 10(…)10^{(\ldots)}.

Section 4

Using and evaluating the model

Once aa and nn (or kk and bb) are known you can predict values by substituting into y=axny=ax^n or y=kbxy=kb^x, or by working with the straight-line equation directly: for example lg⁡y=0.3x+0.6\lg y=0.3x+0.6 at x=8x=8 gives lg⁡y=3\lg y=3, so y=1000y=1000. To find the input for a given output, take logarithms of the output first: lg⁡500=2lg⁡x+0.4\lg500=2\lg x+0.4, so lg⁡x=lg⁡500−0.42\lg x=\frac{\lg500-0.4}{2} and x=101.1495=14.1x=10^{1.1495}=14.1. Evaluate a model by commenting on its limits: a simple exponential model of growth predicts unlimited increase, but real systems are limited by resources, so the model is only valid over a restricted range.

Exam tip

Keep full calculator values until the end, then round to 3 significant figures.

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Exam questions on Logarithmic graphs and modelling

  1. A scientist believes that two variables are related by y=axny=ax^{n}, where aa and nn are constants. She plots lg⁡y\lg y against lg⁡x\lg x and obtains a straight line with gradient 33 that crosses the vertical axis at 22.
    Find the value of yy when x=4x=4.2 marks
  2. Two variables are related by y=kbxy=kb^{x}, where kk and bb are positive constants. A graph of lg⁡y\lg y against xx is a straight line through the points (0, 0.6)(0,\,0.6) and (5, 2.1)(5,\,2.1).
    Use the model to find the value of yy when x=8x=8.2 marks
  3. The variables xx and yy are related by y=axny=ax^{n}. A graph of lg⁡y\lg y against lg⁡x\lg x is a straight line through the points (0.2, 0.8)(0.2,\,0.8) and (0.6, 1.6)(0.6,\,1.6).
    Show that n=2n=2 and find the value of aa to 3 significant figures.3 marks
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Written by the Exaim team, led by Shaun Daswani (Head of Upper Secondary, Improve ME Institute; MSc Financial Mathematics, Imperial College London; BSc, UCL) and Jason Daswani (operational lead, Improve ME Institute; LSE).