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A Level · Cambridge (CIE) · 9709

Sketch natural exponential and logarithmic graphs

Use inverse domains, ranges, intercepts and asymptotes, and distinguish positive, negative and zero exponential rates.

Section 2.2 · Logarithmic and exponential functions

Pure Mathematics 2 & 3 · Logarithmic and exponential functions · 2.2 / 3.2
Exam scope and assessment notes

Pure Mathematics 2 (Paper 2, AS only) and Pure Mathematics 3 (Paper 3, A Level) · Mathematics 9709, 2026–2027. Paper 1 knowledge is assumed. The AS route using Papers 1 + 2 cannot be carried forward to A Level. Shared sections 2.2 and 3.2 include log laws excluding change of base, natural exponential and logarithmic graphs, equations and inequalities, and straight-line transformations. Written practice is self-checked.

Before you begin

  • Understand inverse functions and reflection in y = x.
  • Use logarithms and indices with positive arguments.

Work on paper. Try each question before revealing help, and explain why your method works.

Quick readiness check

What is the domain of y = ln x over the reals?

What you will learn

  • Sketch eˣ and ln x as inverse functions with correctly stated domains and ranges.
  • Describe e^(kx) for positive, negative and zero k without treating an asymptote as an attained value.
Natural exponential and its inverse-2-2-1-1112233(0, 1)(1, 0)y = xxyy = eˣy = ln x
The blue graph y = eˣ and red graph y = ln x are inverses, reflected in the dashed line y = x. Equal drawing units on the two axes preserve that reflection. The exponential has intercept (0, 1), all-real input domain and positive range. The logarithm has intercept (1, 0), positive input domain and all-real range. Their respective asymptotes are y = 0 and x = 0; neither is attained. The window is −2 ≤ x, y ≤ 3, and each graph continues beyond it in its domain.
The sign of the rate changes direction-2-10121234xyGrowth: y = eˣDecay: y = e^(−x)
Positive and negative exponential rates: y = eˣ increases, while y = e^(−x) decreases. Both have all-real input domain, pass through (0, 1) and stay positive. Their horizontal asymptote y = 0 is approached towards negative x for growth and positive x for decay, never reached at a finite input. Only the portions within −2 ≤ x ≤ 2 and 0 ≤ y ≤ 4 are drawn. For zero rate, e^(0x) is instead the constant 1 with range {1}.

The inverse functions exchange domains and ranges

The natural exponential base e is approximately 2.71828. The function y = eˣ has domain all real x, range y > 0 and intercept (0, 1). It increases and approaches the horizontal asymptote y = 0 as x tends to negative infinity, while remaining positive at every finite input. Its inverse y = ln x has domain x > 0, range all real y and intercept (1, 0). Its vertical asymptote is x = 0, approached from the positive side.

Their graphs reflect in y = x: for example (ln 2, 2) on eˣ corresponds to (2, ln 2) on ln x. The inverse identities ln(eˣ) = x for real x and e^(ln x) = x for positive x use different input conditions. A negative logarithm output is compatible with a positive argument below 1. Neither graph crosses its asymptote, and zero is not a valid input for ln x.

The sign of an exponential rate determines its direction

For y = e^(kx), every real x is allowed and the output is positive. If k > 0, the graph increases and approaches zero towards negative x; if k < 0 it decreases and approaches zero towards positive x. Both pass through (0, 1), and their range is y > 0. If k = 0, the function is the constant 1, so its range is just {1}. Reflecting eˣ in the y-axis gives e^(−x), illustrating a negative rate.

For y = e^(−2x), the graph decreases, has intercept (0, 1) and gives y = 1/2 at x = (ln 2)/2. In a supplied decay model C(t) = e^(−t/2) for t ≥ 0, the restricted range is 0 < C ≤ 1; it reaches 1/2 at t = 2 ln 2 but never reaches zero at a finite time. Restrict the model's domain separately from the all-real domain of the mathematical exponential.

Pause and explain

For y = e^(−2x), which statement is correct?

Put the idea to work

Worked example

Describe and sketch y = e^(−2x), including its domain, range, intercept and a point where y = 1/2.

Show the worked solution
  1. The negative coefficient gives a decreasing positive exponential with domain all real x and intercept (0, 1).
  2. Its range is y > 0; as x grows it approaches y = 0 without attaining it.
  3. Set −2x = ln(1/2) = −ln 2 to obtain x = (ln 2)/2.

Answer Decreasing; domain ℝ; range y > 0; intercept (0, 1); point ((ln 2)/2, 1/2).

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From guided practice to a new situation

Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.

1 · Guided

Describe y = e^(2x) and find its point with y = 3.

Give me a hint

Use the sign of 2 and invert the equation e^(2x) = 3.

Compare my reasoning
  1. The graph is increasing with domain ℝ, range y > 0 and intercept (0, 1).
  2. Taking ln gives 2x = ln 3, hence x = (ln 3)/2.
  3. The point is ((ln 3)/2, 3); the graph approaches y = 0 towards negative x but does not meet it.

Increasing; domain ℝ; range y > 0; point ((ln 3)/2, 3).

Look for these in your work

  • State domain, range and direction correctly.
  • Use the inverse relation to locate an exact point.
2 · Independent

Sketch y = ln x, stating its inverse, domain, range, intercept and value at x = e^(−2).

Give me a hint

Exchange the domain and range of eˣ and use ln(eᵘ) = u.

Compare my reasoning
  1. The inverse is eˣ; ln x has domain x > 0 and range ℝ.
  2. It crosses at (1, 0) and has vertical asymptote x = 0 approached from the right.
  3. At positive input e^(−2), its value is −2, demonstrating that valid log outputs can be negative.

Inverse eˣ; domain x > 0; range ℝ; intercept (1, 0); ln(e^(−2)) = −2.

Look for these in your work

  • Keep the positive input domain separate from the all-real output range.
  • Label the intercept and correct side of the asymptote.
3 · Transfer

A supplied normalised signal model is C(t) = e^(−t/2) for t ≥ 0. Find its initial value, time when C = 1/2 and range on the stated domain. Can it reach zero at a finite time in this model?

Give me a hint

Use the restricted time domain and invert the half-value equation exactly.

Compare my reasoning
  1. C(0) = 1, and the negative rate makes the model decrease for t ≥ 0.
  2. From −t/2 = ln(1/2) = −ln 2, the half-value time is t = 2 ln 2.
  3. The restricted range is 0 < C ≤ 1; no finite t gives zero because an exponential remains positive.

Initial value 1; half-value time 2 ln 2; range 0 < C ≤ 1; no finite zero.

Look for these in your work

  • Apply the model's non-negative time domain.
  • Distinguish an asymptotic limit from an attained output.

Common mistakes

  • An exponential approaches zero without taking value zero at a finite real input.
  • The domain of ln x is positive x, while its output may be negative.
  • A model's restricted domain changes its range; k = 0 gives a constant rather than growth or decay.
Recall without your notes

What can you explain now?

Evaluate ln(e³), e^(ln 4) and ln(e^(−2)).

Compare with the explanation

3, 4 and −2 respectively.

Use the inverse identities; the last logarithm has positive argument e^(−2) and a negative output.

After trying it yourself, choose your next review. This is your self-assessment.

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Make it stick

Sketch eˣ, ln x and e^(−x) with their key boundaries. Explain why an inverse can have negative outputs while its input domain stays positive.

If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.

Keep learning Mathematics

Course scope and assessment

312 sequenced lessons provide 936 guided, independent and application problems covering the 160 objectives in sections 1.1–1.8, 2.1–2.6, 3.1–3.9, 4.1–4.5, 5.1–5.5 and 6.1–6.5. The map lists all 38 sections across six alternative components; all eight Pure Mathematics 1 sections and all six Pure Mathematics 2 sections, from Paper 2 Algebra through Numerical solution of equations, currently have full teaching sequences. Paper 3 Algebra, section 3.1, links the six shared foundation lessons plus six lessons on partial fractions and rational binomial expansions; Paper 3 logarithms, trigonometry and numerical equations use the shared lessons, with the Paper 1 graph foundation linked into trigonometry. Paper 3 differentiation combines the shared methods with its inverse-tangent derivative lesson. Paper 3 Integration links four shared standard and identity lessons plus seven advanced lessons, covering all six section 3.5 requirements. Eight Vectors lessons cover all six section 3.7 requirements, with 2D and 3D line and solid applications. Nine Differential equations lessons cover all four section 3.8 requirements, including Paper 3 integration techniques, initial conditions, equilibrium solutions and conditional model interpretations. Eleven Complex numbers lessons cover all eight section 3.9 requirements, with seven Argand diagrams and explicit argument and boundary checks. All nine Pure Mathematics 3 sections now have teaching linked to every mapped requirement. Nine Forces and equilibrium lessons cover all seven section 4.1 requirements, with six force diagrams, signed incline-friction intervals, reaction-dependent pull and push limits and separate-body third-law checks. Nine Kinematics lessons cover the four official section 4.2 requirements, with the graph requirement mapped to three relationships. Six motion diagrams support directed quantities, graph slopes and signed areas, Paper 1 time-calculus methods, reversal distances, constant-acceleration stages and shared-clock particle meetings. Six Momentum lessons cover both section 4.3 requirements, including signed system totals, conservation assumptions, outgoing velocities, coalescence, unknown masses and successive impacts. Four diagrams show system momentum through the stipulated events, and supplied kinetic-energy checks distinguish conservation of momentum from changes in kinetic energy. Twelve Newton’s laws lessons cover all four section 4.4 requirements, with vertical support and release stages, rough horizontal and incline motion, inferred parameters, hanging and surface pulley systems, rigid tow-bar thrust and slack-string transitions. Seven force diagrams support the separate-particle balances. Nine Energy, work and power lessons cover all five section 4.5 requirements, with angle-dependent work, kinetic and reference potential energy, signed net-work balances, smooth curved slides, rough and driven slopes, ideal connected-system energy and instantaneous power on hills. Five diagrams distinguish the energy inputs, losses and valid positive-speed force model. All five Mechanics sections now have teaching and staged practice linked to their mapped requirements. Thirteen Representation of data lessons cover all five section 5.1 requirements, including choice and limitations of displays, ordered and back-to-back stems, raw summaries and box plots, unequal-width density histograms, cumulative-frequency interpolation, descriptive moment calculations, grouped estimates, coding, combining two datasets and correcting totals. Six accessible diagrams retain repeated observations and distinguish numerical spread, frequency area and estimated readings. Ten Permutations and combinations lessons cover both official section 5.2 requirements, with selection and ordered roles, repeated objects, adjacency blocks, non-adjacency gaps, leading-zero cases, labelled multi-row seating and mixed restrictions. Six accessible diagrams show outcome trees, ordered versus unordered choices, repeated copies, blocks, gaps and distinct row seats. Eleven Probability lessons cover all four section 5.3 requirements, with equiprobable elementary outcomes, selection-based probabilities, complements and disjoint regions, independence tests, restricted sample spaces, two-way tables, replacement and removal trees, reversed conditions and unknown parameters. Seven accessible diagrams show dice outcomes, random subsets, event regions, a conditioned grid, a count table and two probability trees. Twelve Discrete random variables lessons cover all three official section 5.4 requirements, with complete probability tables, moment and parameter checks, binomial assumptions, exact probabilities and moments, geometric first-success probabilities, tails, expectation and integer reliability thresholds. Six accessible diagrams distinguish outcome grouping, weighted moments, fixed trials, discrete probability bars, waiting conventions and integer tail deadlines. Twelve Normal distribution lessons cover all three official section 5.5 requirements, with continuous-area interpretation, MF19 cumulative tables, full standardisation, tail and interval regions, conditional probabilities, percentiles, one- and two-parameter inference, binomial suitability, continuity corrections and approximate integer thresholds. Seven accessible diagrams show model areas, table reading, aligned scales, intervals, quantiles, matched binomial and normal shapes and whole integer cells. All five Statistics 1 sections now have teaching and staged practice linked to their mapped requirements. Fourteen Poisson distribution lessons cover all five official section 6.1 requirements, with homogeneous event-count assumptions, exposure scaling, exact masses and tails, conditional count events, moments, parameter inference, probability duration targets, disjoint and overlapping windows, rare-count binomial approximations, matched normal approximations and continuity corrections. Nine accessible diagrams show event exposures, unit conversions, discrete bars, complete tail complements, population moments, shared windows, approximate count models and integer cells. Exact checks distinguish approximate and exact close-target decisions. Fourteen Linear combinations lessons cover the six listed results in official section 6.2, with affine moments, signed scales and constant cases, expectation without independence, independent squared variance contributions, fixed and random fees, independent copies versus reused values, affine normal events, independent normal sums and differences, weighted and conditional areas, coefficient inference, exact independent Poisson sums and overlapping exposure limits. Nine accessible diagrams distinguish scale changes, variance contributions, component costs, copy mechanisms, aligned normal scales, summed areas, Poisson count pairs and unsupported distribution claims. Fourteen Continuous random variables lessons cover both official section 6.3 requirements, with density validity and units, support-aware intervals, complements and conditional regions, first and second moments, percentiles by direct density areas, symmetry checks, parameter inference, infinite-support power and exponential integrals, divergent-moment limits and combined applications. Nine accessible diagrams distinguish density height from probability, normalisation, negative-density failures, central and conditional areas, mean versus median, percentile locations and partial infinite tails. Explicit cumulative distribution function knowledge is not required. Eighteen Sampling and estimation lessons cover all eight official section 6.4 requirements, with population and frame definitions, random-number selection, sampling-quality limits, independent sample-mean moments, normal means, CLT conditions, SE scaling, raw, summarised and coded unbiased estimates, known-variance and large-sample mean intervals, repeated-sampling interpretation, precision planning, proportion intervals, sparse-outcome limits and contextual applications. Ten accessible diagrams distinguish selection frames, number rejection, repeated sample means, population versus mean density, CLT models, variance divisors, interval targets, illustrative coverage, integer size checks and proportion margins. Eighteen Hypothesis tests lessons cover all five official section 6.5 requirements, with pre-specified alternatives, inclusive count tails, explicit equal-tail conventions, integer critical regions and actual significance, exposure-aware Poisson tests, continuity-corrected count approximations, normal and large-sample mean tests, confidence-interval connections, error classifications, fixed-region binomial, Poisson and normal error probabilities and sample-size power comparisons. Nine accessible diagrams show the testing procedure, exact count regions, unequal Poisson tails, discrete significance jumps, corrected count boundaries, matching mean intervals and alternative error distributions. All shared lessons are counted once in the course total. All 38 sections now link teaching and practice. Eight earlier overview lessons remain available, but do not count as full section coverage. This is a content-availability measure, not full programme completion or exam readiness. Written practice is self-checked, not officially marked. Check the 2028–2030 syllabus separately if sitting an examination from 2028; this teaching map uses 2026–2027 requirements.

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