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A Level · Cambridge (CIE) · 9709

Use derivative signs to locate increasing and decreasing intervals

Factor the derivative, test signs on the curve's actual domain and interpret turning or rising behaviour in a restricted model.

Section 1.7 · Differentiation

Pure Mathematics 1 · Differentiation · 1.7

Pure Mathematics 1 (Paper 1) · AS & A Level Mathematics 9709, 2026–2027. Use an informal understanding of limits; formal general differentiation from first principles and points of inflexion are not required in section 1.7. Written practice is self-checked.

Before you begin

  • Differentiate polynomials and rational powers.
  • Use a sign chart and retain domain restrictions.

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

Quick readiness check

What does f′(x) > 0 throughout an interval tell you?

What you will learn

  • Use positive and negative derivative values to identify increasing and decreasing intervals.
  • Separate a stationary input from a domain break and respect the supplied model interval.

Test the gradient over intervals, not just at one point

A positive derivative throughout an interval means the function increases there; a negative derivative means it decreases. Find inputs where the derivative is zero or undefined, split the permitted domain at them and test a value in each interval. A zero derivative at a single input does not by itself prove that the direction changes. A constant function has zero derivative throughout and is neither strictly increasing nor strictly decreasing.

For f(x) = x³ − 6x² + 9x + 1, f′(x) = 3(x − 1)(x − 3). It is positive for x < 1, negative for 1 < x < 3 and positive for x > 3. The curve therefore rises, falls and rises across those intervals. Inputs 1 and 3 are stationary, with heights f(1) = 5 and f(3) = 1; the original function remains defined at both.

A missing input is not a stationary point

For f(x) = x + 4/x on the supplied domain x > 0, f′(x) = 1 − 4/x². Since x² is positive, its sign is the sign of x² − 4: negative on 0 < x < 2 and positive on x > 2. The domain excludes x = 0, so it cannot become a stationary point or be filled into a sign chart as if the curve were continuous there. The supplied positive domain also means negative inputs are outside this problem.

A practical model may permit only a closed time interval. For h(t) = −t² + 8t + 2 on 0 ≤ t ≤ 8, the rate h′(t) = 8 − 2t is positive before t = 4 and negative after it. Thus the model rises to height 18 at t = 4 and then falls. Include physical endpoint values when interpreting a maximum over a closed interval; a mathematical rule's behaviour outside the supplied time range is not part of the stated model.

Pause and explain

For f(x) = x + 4/x on x > 0, is x = 0 a stationary point?

Put the idea to work

Worked example

Find where f(x) = x³ − 6x² + 9x + 1 has positive, zero or negative gradient.

Show the worked solution
  1. Differentiate and factor: f′(x) = 3x² − 12x + 9 = 3(x − 1)(x − 3).
  2. The two factors have the same sign for x < 1 and x > 3, giving positive gradient; between them their signs differ, giving negative gradient.
  3. At x = 1 and 3 the gradient is zero; the curve is defined there and the corresponding heights are 5 and 1.

Answer Positive for x < 1 and x > 3; negative for 1 < x < 3; zero at x = 1 and 3.

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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 increasing and decreasing intervals of y = x² − 4x + 7.

Give me a hint

Differentiate, solve for the zero gradient and test its sign on each side.

Compare my reasoning
  1. The derivative is y′ = 2x − 4, which is zero at x = 2.
  2. For x < 2 it is negative, so the curve decreases there.
  3. For x > 2 it is positive, so the curve increases there; the zero-gradient point is (2, 3).

Decreasing for x < 2; increasing for x > 2; stationary point (2, 3).

Look for these in your work

  • Use derivative signs on both intervals.
  • Evaluate the curve separately for the stationary point's height.
2 · Independent

On the supplied domain x > 0, find increasing and decreasing intervals for f(x) = x + 4/x.

Give me a hint

Use the positive denominator x² to read the sign of x² − 4.

Compare my reasoning
  1. f′(x) = 1 − 4/x² = (x² − 4)/x².
  2. On x > 0, the numerator is negative for 0 < x < 2 and positive for x > 2.
  3. Thus the curve decreases on (0, 2) and increases on (2, ∞); x = 0 remains excluded and f(2) = 4.

Decreasing for 0 < x < 2; increasing for x > 2; stationary point (2, 4).

Look for these in your work

  • Respect the supplied positive domain and non-zero denominator.
  • Separate the genuine stationary point from an excluded input.
3 · Transfer

For the supplied height model h(t) = −t² + 8t + 2 metres on 0 ≤ t ≤ 8 seconds, find when the rate is positive or negative and the greatest height in that interval.

Give me a hint

Use the derivative sign, then compare the stationary height with the two included endpoints.

Compare my reasoning
  1. h′(t) = 8 − 2t, positive for 0 ≤ t < 4 and negative for 4 < t ≤ 8.
  2. At t = 4 the rate is zero and h(4) = 18 metres.
  3. The endpoint heights h(0) and h(8) are both 2 metres, so the greatest permitted height is 18 metres at 4 seconds.

Positive rate for 0 ≤ t < 4; negative for 4 < t ≤ 8; maximum 18 m at t = 4 s.

Look for these in your work

  • Keep the rate intervals within the supplied time domain.
  • Compare the stationary value with both closed endpoints.

Common mistakes

  • Find signs on intervals rather than inferring them from the derivative's roots alone.
  • An undefined original input is not a stationary curve point.
  • Restrict the answer to the stated real domain or physical time interval.
Recall without your notes

What can you explain now?

For y = x² − 4x + 7, find where the derivative is negative, zero and positive.

Compare with the explanation

Negative for x < 2; zero at x = 2; positive for x > 2.

The derivative 2x − 4 changes sign from negative to positive at 2.

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Make it stick

Construct a derivative sign chart, identify domain breaks separately from zero gradients and explain how a restricted model changes the intervals and endpoint checks.

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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