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A Level · Cambridge (CIE) · 9709

Find arithmetic progression terms and finite sums

Recognise a constant difference, recover the first term from indexed data and turn a total constraint into a valid integer term count.

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

  • Solve simultaneous linear equations.
  • Distinguish the first term, an indexed term and a cumulative total.

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

Quick readiness check

Which progression is arithmetic?

What you will learn

  • Recognise and construct an arithmetic progression using its common difference.
  • Use the nth-term and first-n-term sum formulae, including practical constraints on n.

A constant difference determines the nth term

An arithmetic progression has the same difference d between consecutive terms. Starting with first term a, the nth term is uₙ = a + (n − 1)d for positive integers n. There are n − 1 steps from the first term to the nth. For 7, 10, 13, …, a = 7 and d = 3; the twelfth term is 7 + 11(3) = 40. Negative differences are allowed and produce decreasing progressions.

Given indexed terms, write separate equations using their actual indices. If u₄ = 14 and u₁₀ = 38, then a + 3d = 14 and a + 9d = 38. Subtraction gives 6d = 24, so d = 4 and a = 2. Thus uₙ = 4n − 2. The first supplied observation need not be the first term of the progression.

Pair opposite ends to obtain a sum

Pair the first and last terms, the second and next-to-last, and so on. Each pair has sum a + uₙ. This gives Sₙ = n(a + uₙ)/2 = n[2a + (n − 1)d]/2, with the same formula working for odd n by accounting for the middle term. Sₙ is a total, not the nth individual value. For a = 2 and d = 4, S₂₀ = 20(2 + 78)/2 = 800.

In a count problem n must be a positive integer, and a physical interpretation may further require positive terms. A seating model with first row 18 seats and two more seats per row has Sₙ = n(n + 17). Fifteen rows give 480 seats and sixteen give 528, so sixteen is the smallest count reaching at least 500. Check neighbouring integer totals rather than rounding an inequality solution without justification.

Pause and explain

An AP starts at 7 and increases by 3. Which expression gives u₁₂?

Put the idea to work

Worked example

An arithmetic progression has u₄ = 14 and u₁₀ = 38. Find its nth term and S₂₀.

Show the worked solution
  1. Write a + 3d = 14 and a + 9d = 38, giving d = 4 and a = 2.
  2. The nth term is uₙ = 2 + 4(n − 1) = 4n − 2, so u₂₀ = 78.
  3. Use S₂₀ = 20(2 + 78)/2 = 800 and verify both supplied indexed terms.

Answer uₙ = 4n − 2; S₂₀ = 800.

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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

For 7, 10, 13, …, find the twelfth term and the sum of the first twelve terms.

Give me a hint

Find the last term first, then use the mean of the first and last terms for the total.

Compare my reasoning
  1. The common difference is 3, so u₁₂ = 7 + 11(3) = 40.
  2. The first-twelve sum is S₁₂ = 12(7 + 40)/2.
  3. This equals 282; the single term 40 is different from the cumulative sum.

u₁₂ = 40; S₁₂ = 282.

Look for these in your work

  • Use eleven differences for the twelfth term.
  • Use the actual first and last values to calculate the sum.
2 · Independent

An AP has u₃ = 11 and u₈ = 31. Find a, d, uₙ and S₁₅.

Give me a hint

Subtract the indexed-term equations before recovering a.

Compare my reasoning
  1. a + 2d = 11 and a + 7d = 31 give 5d = 20, hence d = 4 and a = 3.
  2. Then uₙ = 3 + 4(n − 1) = 4n − 1 and u₁₅ = 59.
  3. The sum is S₁₅ = 15(3 + 59)/2 = 465; check u₃ and u₈ in the rule.

a = 3; d = 4; uₙ = 4n − 1; S₁₅ = 465.

Look for these in your work

  • Respect the supplied term indices.
  • Verify the recovered rule and calculate a total rather than just its last term.
3 · Transfer

A hall has 18 seats in row one and two more in every subsequent row. Under this model, find the smallest number of rows providing at least 500 seats.

Give me a hint

Use the AP total, then compare adjacent positive integer counts.

Compare my reasoning
  1. With a = 18 and d = 2, Sₙ = n[36 + 2(n − 1)]/2 = n(n + 17).
  2. S₁₅ = 15(32) = 480, below the target, while S₁₆ = 16(33) = 528 reaches it.
  3. Each added row has a positive number of seats, so these increasing totals establish that 16 is the smallest valid count.

16 rows, providing 528 seats; 15 rows provide only 480.

Look for these in your work

  • Model the capacity as a sum of rows.
  • Justify the minimum with adjacent integer totals and increasing capacity.

Common mistakes

  • Use n − 1 differences, not n, when counting from the first term.
  • An indexed observation is not automatically the first term.
  • Check integer counts and the model's permitted domain in total problems.
Recall without your notes

What can you explain now?

An AP has a = 7 and d = 3. Find u₁₂ and S₁₂.

Compare with the explanation

u₁₂ = 40; S₁₂ = 282.

There are eleven steps to term twelve, and its twelve-term total is 12(7 + 40)/2.

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

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Make it stick

Derive uₙ and Sₙ from the meaning of a common difference, then explain why a minimum row count needs an integer and a neighbouring check.

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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