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A Level · Cambridge (CIE) · 9709

Use remainder values and conditions to find unknown coefficients

Evaluate at a linear divisor's root, including non-monic divisors, and turn factor and remainder information into simultaneous equations.

Pure Mathematics 2 & 3 · Shared Algebra foundations · 2.1 / 3.1
Exam scope and assessment notes

Pure Mathematics 2 (Paper 2, AS only) and Pure Mathematics 3 (Paper 3, A Level) · Shared Algebra foundations, Mathematics 9709, 2026–2027. Paper 1 knowledge is assumed. These modulus and polynomial requirements occur in both sections 2.1 and 3.1. Paper 3 additionally requires partial fractions and rational-power binomial expansions; follow its separate lessons for those methods. The AS route using Papers 1 + 2 cannot be carried forward to A Level. Both sections use linear modulus graphs; nonlinear y = |f(x)| and y = f(|x|) graphs are excluded. Polynomial dividends have degree at most 4 and divisors are linear or quadratic. Written practice is self-checked.

Before you begin

  • Evaluate polynomials at signed and fractional inputs.
  • Use a quotient–remainder identity and solve simultaneous linear equations.

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

Quick readiness check

At which input does the divisor 2x − 1 vanish?

What you will learn

  • Use f(c) as the remainder for division by a linear factor vanishing at c.
  • Determine unknown coefficients from specified factors or remainders.

Choose the root of the divisor, not a copied coefficient

For division by ax + b with a ≠ 0, the remainder is a constant r. In f(x) = (ax + b)Q(x) + r, substitute c = −b/a so the divisor term vanishes: r = f(c). The remainder is f(c), with no extra division by a. If f(c) = 0, ax + b is a factor. This is the remainder and factor theorem for a linear divisor; a quadratic divisor can have a linear remainder, so one evaluation does not generally determine it.

For f(x) = 2x³ − x² + 4x − 3 divided by 2x − 1, use c = 1/2. The value is 2(1/8) − 1/4 + 2 − 3 = −1, so the remainder is −1. Direct division gives Q = x² + 2, and (2x − 1)(x² + 2) − 1 reconstructs f. Using x = 1 or dividing f(1/2) by 2 would give an incorrect result.

Each condition supplies an equation

If x − 1 is a factor of f(x) = x³ + px² + qx + 6, then f(1) = 0 gives p + q = −7. If division by x + 2 has remainder 12, then f(−2) = 12 gives 4p − 2q − 2 = 12, or 2p − q = 7. Solving gives p = 0 and q = −7. Check both original evaluations once the coefficients have been found.

Different linear conditions can determine several unknown coefficients, provided the resulting equations supply enough independent information. A stated remainder need not be zero; only a factor condition gives a zero remainder. Retain fractions and signs when the divisor root is negative or non-integral, and distinguish a remainder value from the quotient polynomial.

Pause and explain

Division of f by 2x − 1 gives constant remainder r. Which is correct?

Put the idea to work

Worked example

Find the remainder when 2x³ − x² + 4x − 3 is divided by 2x − 1.

Show the worked solution
  1. The divisor vanishes at c = 1/2.
  2. Evaluate f(1/2) = 2(1/8) − 1/4 + 4(1/2) − 3 = −1.
  3. Check f = (2x − 1)(x² + 2) − 1; the constant remainder is −1.

Answer Remainder −1, using divisor root 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

Find the remainder when x³ − 2x² + 5x − 4 is divided by x − 2.

Give me a hint

Use the divisor root 2 in the original polynomial.

Compare my reasoning
  1. For a linear divisor x − 2, the remainder is f(2).
  2. Evaluate f(2) = 8 − 8 + 10 − 4 = 6.
  3. The remainder is 6, so x − 2 is not a factor of this polynomial.

Remainder 6; x − 2 is not a factor.

Look for these in your work

  • Choose the zero of the actual divisor.
  • Distinguish a non-zero remainder from a factor condition.
2 · Independent

Find the remainder when 4x³ + 6x² − x + 2 is divided by 2x + 3, and verify with a quotient identity.

Give me a hint

Use x = −3/2 and keep the evaluated fractional powers exact.

Compare my reasoning
  1. The divisor root is −3/2, so the remainder is f(−3/2).
  2. The cubic and quadratic terms cancel; the remaining value is 3/2 + 2 = 7/2.
  3. Indeed f(x) = (2x + 3)(2x² − 1/2) + 7/2, confirming the same remainder.

Remainder 7/2; quotient check uses 2x² − 1/2.

Look for these in your work

  • Use the non-monic divisor's negative fractional root.
  • Verify the evaluated remainder in a rebuilt identity.
3 · Transfer

Let f(x) = x³ + px² + qx + 6. Given that x − 1 is a factor and the remainder on division by x + 2 is 12, find p and q and check both conditions.

Give me a hint

Translate the conditions into f(1) = 0 and f(−2) = 12 before solving.

Compare my reasoning
  1. f(1) = 0 gives p + q = −7; f(−2) = 12 gives 2p − q = 7.
  2. Adding the equations gives 3p = 0, hence p = 0 and q = −7.
  3. For f(x) = x³ − 7x + 6, f(1) = 0 and f(−2) = −8 + 14 + 6 = 12, so both conditions hold.

p = 0 and q = −7; both original remainder conditions hold.

Look for these in your work

  • Translate factor and non-zero remainder information correctly.
  • Check the recovered polynomial against both conditions.

Common mistakes

  • For ax + b, substitute −b/a rather than b or −b alone.
  • Do not divide the evaluated remainder by the leading coefficient.
  • A non-zero remainder condition is not a factor condition.
Recall without your notes

What can you explain now?

For f(x) = 4x³ + 6x² − x + 2 divided by 2x + 3, which input gives the remainder and what is it?

Compare with the explanation

Use x = −3/2; remainder 7/2.

At −3/2 the cubic and quadratic contributions −27/2 and 27/2 cancel, leaving 3/2 + 2 = 7/2.

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

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Make it stick

Derive the remainder theorem from the rebuilt identity at a divisor root. Show how a non-monic divisor changes the input but adds no extra scale to the remainder.

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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