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A Level · Cambridge (CIE) · 9709

Use a linear numerator over an irreducible quadratic factor

Combine a constant numerator over a linear factor with Bx + C over a quadratic, and check its real domain.

Pure Mathematics 3 · Algebra · 3.1
Exam scope and assessment notes

Pure Mathematics 3 (Paper 3, A Level) · Mathematics 9709, 2026–2027, section 3.1. Paper 1 knowledge is assumed. Shared modulus, polynomial division, factor and remainder lessons are linked from both sections 2.1 and 3.1. Paper 3 adds partial fractions and rational-power binomial expansions. Partial-fraction numerators may have the same degree as the denominator, but higher-degree numerators are excluded. General-term questions for the rational binomial expansion are not included. Use the stated strict validity condition. Written practice is self-checked; human subject-teacher review remains pending.

Before you begin

  • Expand and compare polynomial coefficients.
  • Determine whether a quadratic has real roots and retain denominator exclusions.

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

Quick readiness check

What is the general numerator over the irreducible factor x² + 1?

What you will learn

  • Decompose a proper fraction with denominator (ax + b)(cx² + d), using a linear numerator over a real irreducible quadratic.
  • Solve the coefficient equations and distinguish a positive quadratic from one that factors over the reals.

The numerator degree must be lower than its factor degree

For a denominator (x − 1)(x² + 1), use A/(x − 1) + (Bx + C)/(x² + 1). A constant alone over the quadratic is not the general form: the linear term Bx is needed even though it can occasionally turn out to be zero. The factor x² + 1 is positive for every real x, so it is irreducible over the reals and introduces no real excluded input; x = 1 remains excluded.

For numerator 3x² + x − 2, clearing the denominator gives 3x² + x − 2 = A(x² + 1) + (Bx + C)(x − 1). At x = 1, 2 = 2A, so A = 1. The x² coefficient gives 3 = A + B, so B = 2; the x coefficient gives 1 = C − B, so C = 3. The constant equation −2 = A − C checks the result. Thus the fraction is 1/(x − 1) + (2x + 3)/(x² + 1).

Check the factor type before choosing its template

For (2x + 1)(x² + 4), the quadratic is again positive for every real input, so use A/(2x + 1) + (Bx + C)/(x² + 4) and exclude only x = −1/2. By contrast x² − 4 factors into (x − 2)(x + 2), so a complete distinct-real-linear form uses separate constant terms over those factors. A quadratic written compactly is not necessarily irreducible.

Comparing coefficients avoids pretending an irreducible quadratic has a real root at which to substitute. One linear-root evaluation plus the remaining coefficient equations determines A, B and C. Keep exact fractions if necessary, then multiply through to verify every coefficient. If the whole rational function has equal numerator and denominator degrees, extract its constant quotient before applying this proper template.

Pause and explain

For denominator (2x + 1)(x² + 4), which real input is excluded?

Put the idea to work

Worked example

Decompose (3x² + x − 2)/[(x − 1)(x² + 1)].

Show the worked solution
  1. Use A/(x − 1) + (Bx + C)/(x² + 1). At 1, A = 1.
  2. Comparing x² and x coefficients gives B = 2 and C = 3.
  3. Verify A − C = −2 and the full numerator; exclude x = 1 only over the reals.

Answer 1/(x − 1) + (2x + 3)/(x² + 1), x ≠ 1.

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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

Decompose (x² + x + 2)/[(x + 1)(x² + 1)].

Give me a hint

Include Bx + C and allow a coefficient to become zero after solving the equations.

Compare my reasoning
  1. Write x² + x + 2 = A(x² + 1) + (Bx + C)(x + 1).
  2. At −1 obtain A = 1. The leading coefficient gives B = 0, and the x coefficient gives C = 1.
  3. The rebuilt numerator is (x² + 1) + (x + 1) = x² + x + 2, with x ≠ −1.

1/(x + 1) + 1/(x² + 1), x ≠ −1; B happens to be 0.

Look for these in your work

  • Start with the full linear-numerator form.
  • Accept a zero coefficient only after solving and verifying it.
2 · Independent

Decompose (2x² + 3x + 4)/[(x − 1)(x² + 1)].

Give me a hint

Use the real linear-factor root, then match the leading and linear coefficients exactly.

Compare my reasoning
  1. At x = 1 the cleared identity gives 9 = 2A, so A = 9/2.
  2. The x² coefficient gives B = 2 − 9/2 = −5/2; the x coefficient gives C = 3 + B = 1/2.
  3. Check the constant A − C = 4 and rebuild the numerator. Only x = 1 is excluded over the reals.

9/[2(x − 1)] + (−5x + 1)/[2(x² + 1)], x ≠ 1.

Look for these in your work

  • Keep exact fractional coefficients in both numerators.
  • Verify the constant equation and real domain.
3 · Transfer

Decompose (3x² + 5x + 6)/[(2x + 1)(x² + 4)] and justify its real domain.

Give me a hint

The quadratic has no real root; the linear root −1/2 isolates A.

Compare my reasoning
  1. At −1/2 the numerator and quadratic are both 17/4, giving A = 1.
  2. The leading coefficient equation 3 = A + 2B gives B = 1; the x equation 5 = B + 2C gives C = 2.
  3. The rebuilt numerator is (x² + 4) + (x + 2)(2x + 1) = 3x² + 5x + 6. Exclude −1/2 only, since x² + 4 > 0.

1/(2x + 1) + (x + 2)/(x² + 4), x ≠ −1/2.

Look for these in your work

  • Use both coefficients over the positive quadratic.
  • Verify the full identity and justify every real exclusion.

Common mistakes

  • Keep Bx + C over the quadratic instead of guessing a constant numerator.
  • Do not substitute a nonexistent real zero of x² + 1.
  • Factor a reducible quadratic into its real linear factors before selecting the general form.
Recall without your notes

What can you explain now?

Which denominator factor has no real zero: x² + 4 or x² − 4?

Compare with the explanation

x² + 4 has no real zero; x² − 4 has zeros −2 and 2.

A real square is non-negative, so adding 4 is strictly positive. Subtracting 4 permits x² = 4.

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Make it stick

Explain why an irreducible quadratic needs a linear numerator, and why a quadratic written as cx² + d must still be checked for real factorisation.

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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