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A Level · Cambridge (CIE) · 9709

Compose functions in the correct order and check the inputs

Read gf as g after f and check that every inner output is allowed by the outer function.

Pure Mathematics 1 · Functions · 1.2

Pure Mathematics 1 (Paper 1) · AS & A Level Mathematics 9709, 2026–2027. Written practice is self-checked; show your reasoning and domain restrictions.

Before you begin

  • Identify the domain and range of a simple function.
  • Substitute a whole expression into a second function using brackets.

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

Quick readiness check

Which function is applied first in gf(x)?

What you will learn

  • Form gf(x) and fg(x) using the Cambridge composition convention.
  • Check the range-to-domain condition and state any deliberate domain restriction.

Read from the input outwards

Cambridge notation gf(x) means g(f(x)): apply f first, then g. This is composition, not multiplication of the two outputs. For f(x) = 3x + 2 and g(x) = x², gf(x) = (3x + 2)², whereas fg(x) = 3x² + 2. In general gf and fg are different functions.

An input-output chain can prevent order errors: write x → f(x) → g(f(x)). Substitute the complete inner expression wherever the outer rule has its input variable. For squaring, keep the whole inner expression in brackets, including any negative sign or constant.

Make the domains fit before declaring a composite

The composite gf can be defined on the entire stated domain of f only when the range of f lies within the domain of g. If f(x) = 2x − 1 on all real x and g(x) = √x on x ≥ 0, the condition fails: some f outputs are negative. To build a valid restricted composite, explicitly restrict f's inputs to x ≥ 1/2. On that new domain, gf(x) = √(2x − 1).

In the reverse order, g's outputs are non-negative and all are accepted by the original real-domain f. Thus fg(x) = 2√x − 1 on x ≥ 0 is valid without further restriction. A displayed algebraic expression can have a natural domain, but that does not silently override a function's declared domain. State which input set you are using.

Pause and explain

For f(x) = x − 5 on x ≥ 0 and g(x) = √x on x ≥ 0, can gf use all of f's original inputs?

Put the idea to work

Worked example

Let f(x) = 2x − 1 on all real x and g(x) = √x on x ≥ 0. Explain how to form gf and give fg.

Show the worked solution
  1. The range of the original f is all real numbers, which is not contained in g's domain x ≥ 0.
  2. Restrict f to x ≥ 1/2; then gf(x) = √(2x − 1) on that restricted domain.
  3. For fg, every output √x is allowed by f, so fg(x) = 2√x − 1 on x ≥ 0.

Answer The unrestricted gf is not valid. With f restricted to x ≥ 1/2, gf(x) = √(2x − 1). fg(x) = 2√x − 1 for x ≥ 0.

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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

Let f(x) = 3x + 2 and g(x) = x² on all real x. Find gf(2) and fg(2).

Give me a hint

Write each input-output chain before evaluating.

Compare my reasoning
  1. For gf(2), apply f: f(2) = 8, then g(8) = 64.
  2. For fg(2), apply g: g(2) = 4, then f(4) = 14.
  3. The results differ because composition order matters; all intermediate inputs here are real and permitted.

gf(2) = 64; fg(2) = 14.

Look for these in your work

  • Apply the inner function first.
  • Show the intermediate value for each order.
2 · Independent

Let f(x) = x + 4 on x ≥ 0 and g(x) = 1/x on x > 0. Find gf, its domain and its range.

Give me a hint

All outputs of f are at least 4, so they satisfy g's input requirement.

Compare my reasoning
  1. The range of f is [4, ∞), contained within the positive domain of g.
  2. Thus gf(x) = 1/(x + 4) on the whole stated domain x ≥ 0.
  3. At x = 0 the output is 1/4; larger inputs approach but never attain zero.

gf(x) = 1/(x + 4), domain x ≥ 0, range 0 < y ≤ 1/4.

Look for these in your work

  • Check the original range-to-domain condition.
  • Keep the original input set and the correct output endpoints.
3 · Transfer

A two-stage model uses f(x) = x − 5 for x ≥ 0, followed by g(u) = √u for u ≥ 0. What is the largest subset of f's domain on which the real composite can be defined? Give its rule.

Give me a hint

Require f's output to be a permitted square-root input.

Compare my reasoning
  1. The second stage requires x − 5 ≥ 0.
  2. Together with the original x ≥ 0, the allowed input set is x ≥ 5.
  3. Explicitly restrict the first stage to that set; the composite is gf(x) = √(x − 5).

Restrict to x ≥ 5; gf(x) = √(x − 5).

Look for these in your work

  • Express the second stage's input requirement as an inequality.
  • Declare the restriction and retain the original domain condition.

Common mistakes

  • gf applies f first; it is not the product g(x)f(x).
  • Check the inner range against the outer domain, not the reverse.
  • State a new input restriction instead of silently changing a declared function.
Recall without your notes

What can you explain now?

For f(x) = x² + 1 and g(x) = 3x, both on all real x, find gf(x) and fg(x).

Compare with the explanation

gf(x) = 3x² + 3; fg(x) = 9x² + 1.

Apply f then g for gf; apply g then f for fg. All intermediate real values are allowed.

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

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Make it stick

Explain why equal input domains do not guarantee that composition is possible. Build an example where gf and fg have different values and different domain restrictions.

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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