A Level · Cambridge (CIE) · 9709

Use logarithm laws with valid arguments and bases

Connect logarithms with indices, combine products and quotients correctly and preserve the domain of every original logarithm.

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

Use index laws, including fractional and negative powers.
Solve simple polynomial equations and check sign restrictions.

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

Quick readiness check

What does log₂(1/4) equal?

What you will learn

Interpret logₐ x = p as aᵖ = x with a > 0, a ≠ 1 and x > 0.
Apply product, quotient and power laws while checking original arguments are positive.

A logarithm names the exponent of a positive base

For a > 0 with a ≠ 1, logₐ x = p means aᵖ = x, requiring x > 0 for a real logarithm. Thus log₂ 8 = 3 and log₂(1/4) = −2. A negative logarithm value is allowed; a negative argument is not. The base 1 is excluded because 1ᵖ does not determine a unique exponent. In ln x the base is the natural exponential constant e.

With a valid common base and positive x and y, logₐ(xy) = logₐ x + logₐ y, logₐ(x/y) = logₐ x − logₐ y, and logₐ(xʳ) = r logₐ x. There is no matching law for a sum: ln(x + y) does not generally equal ln x + ln y. The stated positive-argument conditions matter when applying a power law; a rewritten expression must keep the original permitted inputs. Change of base is outside this section's required log-laws content.

Combine the expression, then recheck the original domains

For positive x and y, ln(8x³/√y) = ln 8 + 3 ln x − (1/2)ln y = 3 ln 2 + 3 ln x − (1/2)ln y. The coefficient of a logarithm records an exponent, and a division becomes a difference. Keep one common base throughout a calculation rather than combine logarithms with different bases as if they were the same quantity.

For ln(x − 1) + ln(x + 1) = ln 8, the original arguments require x > 1. Combining gives ln(x² − 1) = ln 8, hence x² = 9. The algebraic candidates are ±3, but only x = 3 satisfies the original domain. The combined argument x² − 1 alone would also be positive at −3; checking it alone loses the restrictions of the two original logarithms.

Pause and explain

The combined equation ln(x² − 1) = ln 8 gives ±3. Which solves ln(x − 1) + ln(x + 1) = ln 8?

Put the idea to work

Worked example

For x > 0 and y > 0, expand ln(8x³/√y) using log laws.

Show the worked solution

Use the quotient law to separate ln(8x³) − ln(√y).
Use product and power laws to obtain ln 8 + 3 ln x − (1/2)ln y.
Since 8 = 2³, ln 8 = 3 ln 2; retain the stated positive x and y domain.

Answer 3 ln 2 + 3 ln x − (1/2)ln y, for x > 0 and y > 0.

Try a topic test

Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.

Test this topic →

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

For x > 0 and y > 0, expand log₂(8x²/y).

Give me a hint

Separate product and quotient terms, then use the known power 8 = 2³.

Compare my reasoning

The quotient law gives log₂(8x²) − log₂ y.
The product and power laws give log₂ 8 + 2 log₂ x − log₂ y.
Since log₂ 8 = 3, the result is 3 + 2 log₂ x − log₂ y on the stated domain.

3 + 2 log₂ x − log₂ y, for x > 0 and y > 0.

Look for these in your work

Use product, quotient and power laws with one base.
Retain the positive-argument domain.

2 · Independent

Solve ln(x − 1) + ln(x + 1) = ln 8 and explain any rejected root.

Give me a hint

State the domain before combining the logarithms.

Compare my reasoning

The original arguments require x − 1 > 0 and x + 1 > 0, hence x > 1.
Combine to ln(x² − 1) = ln 8; equality of logs gives x² = 9 and candidates ±3.
At x = 3 the arguments 2 and 4 are valid and multiply to 8; reject −3 because its original arguments are negative.

x = 3 only; x = −3 fails the original log domains.

Look for these in your work

Check each original argument, not just the combined product.
Verify the retained root in the original equation.

3 · Transfer

Positive dimensionless measurements R and S satisfy ln R − 2 ln S = ln 5. Find R when S = 2 and explain the recovered relationship.

Give me a hint

Use the power and quotient laws to combine the left side before removing the logarithm.

Compare my reasoning

For positive R and S, ln R − 2 ln S = ln(R/S²).
Equality with ln 5 gives R/S² = 5, so R = 5S².
At S = 2, R = 20; substituting gives ln 20 − 2 ln 2 = ln 5.

R = 5S²; when S = 2, R = 20.

Look for these in your work

Interpret the coefficient 2 as a power.
Check the positive values in the original log relationship.

Common mistakes

A log law for a product is not a log law for a sum.
Keep a common valid base and positive arguments.
After combining logs, check each original argument before retaining equation roots.

Recall without your notes

What can you explain now?

Simplify ln 3 + ln 4, and explain why the answer is not ln 7.

Compare with the explanation

ln 12, not ln 7.

Adding logarithms combines a product, 3 × 4, rather than the sum of the two arguments.

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

Your review choice appears on Today. Sign in to sync it across devices.

Continue to next lesson Back to Today

Make it stick

State the base and argument conditions for a real logarithm. Give a product-law calculation and show why original log domains can reject a root after combining.

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

Check the official syllabus 2026–2027

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.