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A Level · Cambridge (CIE) · 9709

Count unordered selections with combinations

Remove repeated orderings when only membership matters, and restore role assignments when the problem requires them.

Statistics 1 · Permutations and combinations · 5.2
Exam scope and assessment notes

Probability & Statistics 1 (Paper 5, AS & A Level) · Mathematics 9709, 2026–2027, section 5.2. State whether order matters, objects are distinguishable, repetition is permitted and positions or rows are labelled. Count mutually exclusive cases before adding them; account for internal arrangements when using blocks. These lessons include seating in two or more rows. Arrangements in a circle are excluded from this syllabus section. Unless a question says otherwise, people and named roles are distinguishable, and selection is without replacement. Written practice is self-checked; human subject-teacher review remains pending.

Before you begin

  • Calculate factorials and ordered selections.
  • Identify which positions or roles are labelled.

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

Quick readiness check

Which outcomes are identical for an ordinary two-person committee?

What you will learn

  • Derive nCr by dividing ordered selections by r!.
  • Separate selecting a group from allocating its roles.
Two names from A, B, C give six ordered pairs AB, BA, AC, CA, BC, CB but three unordered sets AB, AC, BC. Each unordered set has two descriptions in the ordered list. Thus 3P2 = 6 and 3C2 = 6/2! = 3.Does swapping names change it?Ordered roles: 6 pairsABBAACCABCCBUnordered members: 3 setsABACBCAB and BA name the same set6/2! = 3
Two names from A, B, C give six ordered pairs AB, BA, AC, CA, BC, CB but three unordered sets AB, AC, BC. Each unordered set has two descriptions in the ordered list. Thus 3P2 = 6 and 3C2 = 6/2! = 3.

Membership is the complete outcome

For the names A, B and C, selecting two gives AB, AC and BC: three sets. Ordered pairs give AB, BA, AC, CA, BC and CB: six. Each set occurs 2! times in the ordered list. When order is irrelevant, divide the ordered count by r! to remove this repeated counting.

The formula is nCr = n!/[r!(n − r)!] for integer 0 ≤ r ≤ n. Seven candidates choosing a committee of three give 7C3 = 35. The symmetry nCr = nC(n − r) follows because choosing the included people determines the excluded people. Choosing no one or everyone each gives one set.

Selection and roles can be successive stages

Choosing a three-person committee from six and then choosing its chair gives 6C3 × 3 = 60. A chair is singled out, but the other two members have no different roles. Equivalently choose the chair first in six ways and two other members from the remaining five: 6 × 5C2 = 60.

Do not use 6P3 = 120 for this committee-with-chair model: that incorrectly distinguishes the two ordinary members. Conversely, using only 6C3 = 20 ignores the chair. A formula is justified by the definition of the complete outcome, not a keyword such as ‘choose’.

Pause and explain

Select two members from A, B and C with no roles. How many sets?

Put the idea to work

Worked example

Select two names from A, B and C, first without roles and then for two labelled roles.

Show the worked solution
  1. Without roles, list AB, AC and BC: three sets.
  2. For two labelled roles, each set has two orders: AB/BA, AC/CA, BC/CB.
  3. Thus 3C2 = 3 and 3P2 = 6.

Answer 3 unordered sets; 6 ordered assignments.

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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

Choose a three-person committee with no roles from seven candidates. Count the committees.

Give me a hint

Divide ordered three-person selections by the number of internal orders.

Compare my reasoning
  1. Ordered selections number 7 × 6 × 5 = 210.
  2. Each committee appears 3! = 6 times in that list.
  3. The count is 210/6 = 7C3 = 35.

35 committees.

Look for these in your work

  • Define a committee as a set of members.
  • Explain the division by six.
2 · Independent

Select two different books from eight for an unordered reading shortlist. Count shortlists.

Give me a hint

Swapping the order in which the books are named does not change the shortlist.

Compare my reasoning
  1. Ordered choices give 8 × 7 = 56.
  2. Each pair is named twice, once in each order.
  3. Divide by 2! to get 8C2 = 28.

28 shortlists.

Look for these in your work

  • Use no replacement.
  • Remove only the duplicate ordering.
3 · Transfer

Select a three-person committee from six candidates and appoint one member as chair; the other two have equal roles. Count outcomes in two ways.

Give me a hint

Either choose the members first or choose the chair first.

Compare my reasoning
  1. Choose the group then chair: 6C3 × 3 = 20 × 3 = 60.
  2. Choose chair then the remaining pair: 6 × 5C2 = 6 × 10 = 60.
  3. Do not assign an order to the two ordinary members.

60 outcomes by either method.

Look for these in your work

  • Include the distinguished chair in each outcome.
  • Keep ordinary members unordered.

Common mistakes

  • A combination records membership rather than sequence.
  • Divide by r! only when all those orders represent the same outcome.
  • Treat named roles as an additional stage when appropriate.
Recall without your notes

What can you explain now?

Why does nCr equal nC(n − r)?

Compare with the explanation

Each selected set determines exactly one complementary excluded set.

This is a one-to-one correspondence, not an assumption that order matters.

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

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Make it stick

Ask whether swapping selected names changes the outcome; combine membership choices with any distinct roles.

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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