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A Level · Cambridge (CIE) · 9709

Select binomial coefficients and solve for unknown parameters

Choose the correct power term, include contributions from products and retain every parameter value consistent with the given coefficients.

Pure Mathematics 1 · Series · 1.6

Pure Mathematics 1 (Paper 1) · AS & A Level Mathematics 9709, 2026–2027. Use positive-integer binomial powers and positive-integer term indices. Written practice is self-checked.

Before you begin

  • Use the general term of a positive-integer binomial expansion.
  • Solve quadratic equations and collect like powers in a product.

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

Quick readiness check

Which term of (a + bx)^n supplies x² when b is constant?

What you will learn

  • Find a requested coefficient without expanding unnecessary terms.
  • Use supplied coefficient information to solve for a parameter and check possible signs.

Match the exponent before calculating the coefficient

For (2 + x)^5, the x³ term has k = 3 and coefficient (5 choose 3)2² = 10 × 4 = 40. For (3 − 2x)^5, the x² coefficient is (5 choose 2)3³(−2)² = 1080. The term's position is k + 1, so an x² term is usually the third term when the variable occurs as x, rather than the second.

When the variable term is x², its kth power is x^(2k). Thus (1 + x²)^4 has no x³ term. In a product, more than one pair of powers can contribute: the x² coefficient of (1 + x)(2 − x)^4 combines 1 times the x² coefficient 24 and x times the x coefficient −32, giving 24 − 32 = −8. Count every route to the target power.

A squared parameter can have two signs

In (1 + kx)^5, the x² coefficient is 10k². If it equals 90, then k² = 9 and k = 3 or −3. Both meet the given squared-coefficient condition; a positive-root assumption would lose one. The x coefficient is 5k, so additional information that it is 15 selects k = 3, while −15 selects k = −3.

A coefficient equation describes a constant multiplier of the chosen power, not a value of x. Substitute candidate parameters into every supplied condition. If two coefficient statements imply incompatible parameter values, state that no parameter satisfies them together. Where a problem supplies a positive or negative restriction, apply it after solving the coefficient equation.

Pause and explain

10k² = 90 has which real parameter values?

Put the idea to work

Worked example

The coefficient of x² in (1 + kx)^5 is 90. Find all real k. Which remains if the x coefficient is also 15?

Show the worked solution
  1. The x² coefficient is (5 choose 2)k² = 10k².
  2. Solve 10k² = 90 to get k = 3 or −3.
  3. The additional condition 5k = 15 selects k = 3; without it both signs remain valid.

Answer k = 3 or −3 from the x² coefficient; with the x coefficient 15, k = 3 only.

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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

Find the coefficient of x³ in (2 + x)^5.

Give me a hint

Select k = 3 and keep the remaining power of 2.

Compare my reasoning
  1. The relevant term is (5 choose 3)2^(5 − 3)x³.
  2. Compute (5 choose 3) = 10 and 2² = 4.
  3. Their product is 40, so the term is 40x³ and its coefficient is 40.

40.

Look for these in your work

  • Select the term by its variable exponent.
  • Include the remaining constant power.
2 · Independent

Find the coefficient of x² in (1 + x)(2 − x)^4.

Give me a hint

Use the constant, linear and quadratic terms of the second factor to identify contributions.

Compare my reasoning
  1. The relevant part of (2 − x)^4 is 16 − 32x + 24x².
  2. Multiplication by 1 contributes 24x² and multiplication by x contributes −32x².
  3. Add both contributions to obtain −8x²; higher powers cannot contribute to x² here.

−8.

Look for these in your work

  • Find both ways to obtain the target power.
  • Add the signed coefficients rather than multiplying them together.
3 · Transfer

For the model (1 + px)^4, the x coefficient is −8. Find p, the x² and x³ coefficients, and explain whether an x² coefficient of 30 could satisfy the same condition.

Give me a hint

Use the linear coefficient to fix p before checking the other powers.

Compare my reasoning
  1. 4p = −8 gives p = −2.
  2. The x² coefficient is 6p² = 24 and the x³ coefficient is 4p³ = −32.
  3. A proposed x² coefficient of 30 contradicts the already fixed value 24, so no p satisfies those two supplied conditions together.

p = −2; x² coefficient 24; x³ coefficient −32; the proposed coefficient 30 is incompatible.

Look for these in your work

  • Use one condition to determine the parameter.
  • Verify all remaining conditions instead of solving them independently as if parameters could differ.

Common mistakes

  • Match the requested variable power, not just the term number.
  • A product can contribute to one power in several ways.
  • Do not discard a negative parameter root without a supplied restriction.
Recall without your notes

What can you explain now?

The x coefficient in (1 + px)^4 is −8. Find p and the x³ coefficient.

Compare with the explanation

p = −2; x³ coefficient −32.

The first condition is 4p = −8, so p = −2; the cubic coefficient is 4p³ = −32.

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

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Make it stick

Find one binomial coefficient from the general term, then explain how a product or a squared parameter changes the checking steps.

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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