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A Level · Cambridge (CIE) · 9709

Use factors to solve cubic and quartic equations completely

Check a linear factor, divide to reduce the degree and retain every distinct real root allowed by the problem.

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

  • Use the factor theorem and polynomial division.
  • Factor and solve quadratic equations, including a difference of squares.

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

Quick readiness check

What does f(3/2) = 0 prove about the factor 2x − 3?

What you will learn

  • Verify a factor with the factor theorem and factorise the reduced quotient.
  • Solve degree-three or degree-four polynomial equations and interpret repeated roots or contextual restrictions.

One known factor reduces the degree; it does not finish the solution

A linear expression ax + b with a ≠ 0 is a factor of f when f(−b/a) = 0. Verify the factor, divide f by it, then factor or solve the quotient. An equation expressed as a product equal to zero is solved by setting each factor to zero. Include roots from the original factor and from every remaining factor; a factor supplied in the question is a starting point rather than the entire root set.

For f(x) = 2x³ − 3x² − 8x + 12, f(3/2) = 0, so 2x − 3 is a factor. Division gives x² − 4 = (x − 2)(x + 2). Hence f(x) = (2x − 3)(x − 2)(x + 2), with roots 3/2, 2 and −2. Re-expanding the product and substituting the roots provide complementary checks.

Separate multiplicity, distinct roots and permitted model inputs

A quartic may factor into two quadratics or several linear factors. For x⁴ − 5x² + 4, the substitution u = x² gives u² − 5u + 4 = (u − 1)(u − 4), so the real roots are ±1 and ±2. Treat negative values of u cautiously when solving an equation in x², since real squares are non-negative. A repeated linear factor represents one distinct root with multiplicity, not multiple different inputs.

For t³ − 3t² + 4 = (t − 2)²(t + 1), the algebraic roots are t = 2, repeated, and t = −1. If a supplied time model requires t ≥ 0, only t = 2 is permitted, and it describes one distinct time. State the full algebraic root set before applying a contextual restriction so that a discarded root and its reason remain visible. Solving the equation checks the supplied model's event condition; it does not independently validate that model.

Pause and explain

For (t − 2)²(t + 1) = 0 with t ≥ 0, how many distinct permitted time values are there?

Put the idea to work

Worked example

Given that 2x − 3 is a factor of 2x³ − 3x² − 8x + 12, factorise completely and solve the equation f(x) = 0.

Show the worked solution
  1. The divisor root is 3/2 and f(3/2) = 0, confirming the factor.
  2. Division gives quotient x² − 4 = (x − 2)(x + 2).
  3. Set each factor in (2x − 3)(x − 2)(x + 2) to zero to obtain −2, 3/2 and 2.

Answer f(x) = (2x − 3)(x − 2)(x + 2); roots x = −2, 3/2 or 2.

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

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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

Given x − 2 is a factor, solve x³ − 4x² + x + 6 = 0.

Give me a hint

Divide by the supplied factor, then solve the quadratic quotient as well.

Compare my reasoning
  1. At x = 2 the polynomial is zero; division gives quotient x² − 2x − 3.
  2. Factor the quotient as (x − 3)(x + 1).
  3. The full product (x − 2)(x − 3)(x + 1) gives roots 2, 3 and −1.

x = −1, 2 or 3.

Look for these in your work

  • Verify the supplied factor before reducing the degree.
  • Include roots from every linear factor.
2 · Independent

Factorise x⁴ − 5x² + 4 completely over the reals and solve the equation equal to zero.

Give me a hint

Use a quadratic in x², then factor each difference of squares.

Compare my reasoning
  1. Set u = x² to factor u² − 5u + 4 as (u − 1)(u − 4).
  2. Return to x: (x² − 1)(x² − 4) = (x − 1)(x + 1)(x − 2)(x + 2).
  3. Every factor supplies a real root, giving −2, −1, 1 and 2.

(x − 1)(x + 1)(x − 2)(x + 2); roots −2, −1, 1 and 2.

Look for these in your work

  • Return from the substituted variable to the original one.
  • Retain both signs for each positive square value.
3 · Transfer

A supplied event equation is t³ − 3t² + 4 = 0, where t is numerical time in seconds with t ≥ 0. Given the root t = 2, find every permitted time and explain the repeated root.

Give me a hint

Divide by t − 2 and check whether the quotient contains that factor again.

Compare my reasoning
  1. Division gives t² − t − 2 = (t − 2)(t + 1), so the full expression is (t − 2)²(t + 1).
  2. The algebraic roots are 2, with multiplicity 2, and −1; reject −1 because t ≥ 0.
  3. Only the distinct time t = 2 s remains. Its repeated factor does not describe two different times.

Only t = 2 s is permitted; it is a repeated root, with negative root −1 excluded.

Look for these in your work

  • Find the quotient roots, including any repeated supplied root.
  • Distinguish distinct permitted times from multiplicity.

Common mistakes

  • Include the root from the supplied factor as well as the quotient roots.
  • A repeated factor is one distinct root, not two different event times.
  • Apply model restrictions after solving the algebraic equation and explain exclusions.
Recall without your notes

What can you explain now?

Given that x − 1 is a factor of x³ − 2x² − 5x + 6, solve the equation equal to zero.

Compare with the explanation

x = −2, 1 or 3.

The quotient is x² − x − 6 = (x − 3)(x + 2), so all three linear factors supply roots.

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

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Make it stick

Starting from one verified factor, explain how to find all other real roots. Separate repeated factors from distinct roots and apply any model domain last.

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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