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Topic 2.10 · HL only

Scaling using logarithms, linearizing data with logs + interpreting: notes and practice questions

Summary
  • Logarithmic scales convert exponentially increasing values (e.g., 1, 10, 100) to a manageable linear scale (e.g., 0, 1, 2) by using logarithms.
  • Semi-log graphs have one logarithmic axis and one linear axis.
  • Log-log graphs have both axes on a logarithmic scale, plotting log⁡y\log y against log⁡x\log x.
  • To linearise a power relationship y=axby = ax^b:
  • Take logarithms of both sides: ln⁡y=ln⁡a+bln⁡x\ln y = \ln a + b \ln x
  • Plot ln⁡y\ln y against ln⁡x\ln x (log-log graph) to get a straight line.
  • The gradient of this line is bb.
  • The y-intercept of this line is ln⁡a\ln a.
  • To linearise an exponential relationship y=abxy = ab^x:
  • Take logarithms of both sides: ln⁡y=ln⁡a+xln⁡b\ln y = \ln a + x \ln b
  • Plot ln⁡y\ln y against xx (semi-log graph) to get a straight line.
  • The gradient of this line is ln⁡b\ln b.
  • The y-intercept of this line is ln⁡a\ln a.
  • Linearisation techniques work identically with natural logarithm ln⁡\ln or base-10 logarithm log⁡\log, but consistency is crucial.
  • When estimating from a log-linearised graph:
  • Convert the known value to its logarithm.
  • Read the corresponding logarithmic value from the graph.
  • Inverse the logarithm to find the actual value:
  • If log⁡x=k\log x = k, then x=10kx = 10^k.
  • If ln⁡x=k\ln x = k, then x=ekx = e^k.
  • Use GDC to plot original data and model curve to visually check fit.
  • Store exact regression parameters in GDC memory to avoid compounding rounding errors in predictions.
  • Always confirm the logarithm base (log⁡\log or ln⁡\ln) used in the question for correct inverse operations.

How it is examined

The discriminating idea is which transform straightens which relationship: a semi-log plot (log⁡y\log y against xx) is a straight line when y=abxy = ab^x, and a log-log plot (log⁡y\log y against log⁡x\log x) is a straight line when y=axny = ax^n. Students then read the gradient and intercept back into aa, bb or nn. Since they are never asked to draw the graph, a question either supplies the plot or supplies the linear regression output. This is one of the places AHL 1.9 is actually needed.

Key ideas
  • Scale very large or small numbers using logarithms.
  • Linearise data using logarithms, to determine whether the data has an exponential or a power relationship, using best-fit straight lines to determine parameters.
  • Interpret log-log and semi-log graphs.
Not assessed

In examinations, students will not be expected to draw or sketch log-log or semi-log graphs. They read and interpret them.

Linking questions

  • Other contexts: growth of bacteria, or of traffic to websites and social media. Exponential graphs that show alarming absolute figures but reasonable rates of growth.
  • Links to other subjects: pH semi-log curves and finding activation energy from experimental data (chemistry); exponential decay (physics); experimental work (sciences).
  • TOK: does the applicability of knowledge vary across the different areas of knowledge? What would the implications be if the value of all knowledge was measured solely in terms of its applicability?
  • Links to websites: Gapminder makes use of log-log graphs, www.gapminder.org.

Practice questions

8 questions · 8 medium
Showing 8 of 8

Question 1

MediumPaper 1 · calculator5 marks
(a)

The following table shows the number of days, dd, since a new mobile application was launched and the percentage of its initial active user base, xx, remaining at the beginning of that day.

Days since launch (dd)259141822
Percentage of active users left (xx)854832242017

The following table shows the natural logarithm of both dd and xx on these days, rounded to 2 decimal places.

ln⁡(d)\ln (d)0.691.612.202.642.893.09
ln⁡(x)\ln (x)4.443.873.473.183.002.83

Use the data in the second table to find the value of mm and the value of bb for the regression line, ln⁡x=m(ln⁡d)+b\ln x = m(\ln d) + b.

[2]
(b)

Assuming that the model found in part (a) remains valid, estimate the percentage of active users remaining when d=25d = 25.

[3]

Question 2

MediumPaper 1 · calculator6 marks
(a)

A biologist is studying the growth of a certain bacterial population, NN, over time, TT (in hours). She created a scatter plot with TT on the x-axis and log⁡10N\log_{10}N on the y-axis.

The biologist noticed that the points had a strong linear correlation, so she drew a line of best fit, as shown in the diagram. The line passes through the points (0,−0.8)(0, -0.8) and (5,12.7)(5, 12.7).

Scatter plot with Time (T) on x-axis and log10N on y-axis, showing a line of best fit passing through (0, -0.8) and (5, 12.7)

Find an equation for NN in terms of TT.

[3]
(b)

The biologist also investigates the relationship between the population size, NN, and the nutrient level, SS. She believes that the data can be modelled by N=cS+dN = c\sqrt{S} + d, where cc and dd are constants, and she decides to create a scatter plot to verify her belief.

State what expression the biologist should plot on each axis to verify her belief.

[1]
(c)

The scatter plot from part (b) has a linear relationship, and the biologist finds c=3.5c = 3.5 and d=8.2d = 8.2.

Find an equation for TT in terms of SS.

[2]

Question 3

MediumPaper 1 · calculator6 marks

A team of engineers is studying the relationship between the input power (P, in Watts) and the output efficiency (E, as a percentage) of a new prototype device. When they plot their experimental data on a log-log graph, they observe that the data points form a perfect straight line. Given that the line passes through the points (3, 12.0411) and (6, 20.9648), find the equation of the relationship connecting P and E. Your final answer should not include logarithms.

Question 4

MediumPaper 1 · calculator7 marks
(a)

(a) The growth of a bacterial colony's area, AA, over time, tt, is being studied under specific conditions. The area AA is measured in square micrometers (μm2\mu m^2) at the end of each hour tt for five consecutive hours. The data is recorded in Table 1.

**Table 1: Time tt (hours) and Area AA (μm2\mu m^2)**

Time tt (hours)Area AA (μm2\mu m^2)
152
2140
3265
4398
5560

It is believed that the growth of the colony can be modelled by an equation of the form A=k×tpA = k \times t^p.

Use power regression on your graphic display calculator to find the value of kk and the value of pp. Give your answers to three significant figures.

[2]
(b)

(b) The values of tt and AA can be transformed such that x=ln⁡tx = \ln t and y=ln⁡Ay = \ln A. Table 2 shows data for xx and yy to three decimal places.

**Table 2: Transformed data x=ln⁡tx = \ln t and y=ln⁡Ay = \ln A**

x=ln⁡tx = \ln ty=ln⁡Ay = \ln A
0.0003.951
0.6934.942
1.0995.580
1.3865.986
1.6096.328

Find the linear regression equation of yy on xx, in the form y=cx+dy = cx + d. Give the values of cc and dd to three decimal places.

[2]
(c)

(c) Hence, show that this linear regression is equivalent to the power regression found in part (a).

[3]

Question 5

MediumPaper 2 · calculator16 marks
(a)

A school administrator is tracking the spread of a rumour among students. The number of students, SS, who have heard the rumour is recorded tt hours after it began. The first results obtained are shown in the top two rows of the table below.

tt224466881010
SS11111616252537375555
log⁡10S\log_{10} S

Complete the last row of the table, giving your answers to three decimal places.

[2]
(b)

Draw a graph of log⁡10S\log_{10} S against tt, using appropriate scales on the axes.

[4]
(c)

Hence, state the type of model that best fits the data displayed in part (b).

[1]
(d)

The school counsellor suggests that the number of students who have heard the rumour can be modelled by S(t)=A×BtS(t) = A \times B^t.

Explain why the school counsellor is correct.

[4]
(e)

Hence, determine the values of the parameters AA and BB of the school counsellor's model, showing all your calculations. Give your answers to three significant figures.

[5]

Question 6

MediumPaper 1 · calculator10 marks
(a)(i)

The intensity of sound, II (in W/m2^2), from a point source varies inversely as the square of the distance, rr (in metres), from the source.

When r=10r = 10 m, the intensity I=0.005I = 0.005 W/m2^2.

Find an equation for II in terms of rr.

[4]
(a)(ii)

Find the value of II when r=25r = 25 m.

[2]
(b)(i)

Identify which two of the following graphs would form a straight line.

A. II against rr

B. II against r2r^2

C. II against r−2r^{-2}

D. log⁡10I\log_{10}I against log⁡10r\log_{10}r

E. log⁡10I\log_{10}I against rr

F. II against log⁡10r\log_{10}r

[2]
(b)(ii)

For each of these two graphs identified in part (b.i), find the value of the gradient of the line formed.

[2]

Question 7

MediumPaper 1 · calculator7 marks

In a physics experiment, the relationship between the intensity of light, II (in candela), and the distance from the source, dd (in meters), is modelled by the equation I=kdnI = k d^{n}, where kk and nn are constants.

To determine kk and nn, experimental values of II and dd are obtained. A graph of log⁡10I\log_{10} I against log⁡10d\log_{10} d shows a straight line passing through the points (0.5,1.2)(0.5, 1.2) and (1.5,−0.3)(1.5, -0.3).

Find the value of kk and of nn.

Question 8

MediumPaper 1 · calculator7 marks
(a)

A cup of hot tea is placed in a room with a constant ambient temperature of 22°C. The temperature of the tea, TT in degrees Celsius, is recorded at various times, tt in minutes.

It is assumed that the temperature difference, D=T−22D = T - 22, follows an exponential decay model. To linearize the data, a graph of ln(D)\text{ln}(D) is plotted against tt. This graph is a straight line that passes through the points (4,3.8)(4, 3.8) and (12,2.2)(12, 2.2).

(a) Find the equation of the straight line in the form ln(D)=mt+c\text{ln}(D) = mt + c, where mm and cc are constants.

[3]
(b)(i)

(b) Hence,

(i) find an expression for DD in terms of tt, writing your answer in the form D=AektD = Ae^{kt}.

[2]
(b)(ii)

(ii) calculate the temperature of the tea, TT, when t=20t = 20 minutes.

[2]

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What does Scaling using logarithms, linearizing data with logs + interpreting cover in IB Maths AI?

Logarithmic scales convert exponentially increasing values (e.g., 1, 10, 100) to a manageable linear scale (e.g., 0, 1, 2) by using logarithms. Semi-log graphs have one logarithmic axis and one linear axis. Log-log graphs have both axes on a logarithmic scale, plotting log y against log x.

Is Scaling using logarithms, linearizing data with logs + interpreting SL or HL?

Scaling using logarithms, linearizing data with logs + interpreting is HL only. SL students are not examined on it.

How do I revise Scaling using logarithms, linearizing data with logs + interpreting for IB Maths AI?

Start from the core idea: logarithmic scales convert exponentially increasing values (e.g., 1, 10, 100) to a manageable linear scale (e.g., 0, 1, 2) by using logarithms. In the exam: the discriminating idea is which transform straightens which relationship: a semi-log plot (log y against x) is a straight line when y = ab^x, and a log-log plot (log y against log x) is a straight line when y = ax^n. Students then read the gradient and intercept back into a, b or n. Then practise exam-style questions, easiest first, writing out every step of your working before you check it.

How does FourtyFive help me practise Scaling using logarithms, linearizing data with logs + interpreting?

FourtyFive has 8 Scaling using logarithms, linearizing data with logs + interpreting questions. Every answer you write is marked mark by mark, IB-style, and you see where each mark was won or lost. Every part has a hint, the AI tutor helps you through the step you are stuck on, and your Study Profile picks what to practise next.

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