Learn in your language
Skip to content
A Level · Cambridge (CIE) · 9709

Find parametric gradients from two derivatives with respect to the parameter

Calculate a curve point and its coordinate rates, then use their ratio for a tangent or normal when the horizontal rate is non-zero.

Pure Mathematics 2 & 3 · Differentiation · 2.4 / 3.4
Exam scope and assessment notes

Pure Mathematics 2 (Paper 2, AS only) and Pure Mathematics 3 (Paper 3, A Level) · Mathematics 9709, 2026–2027. Paper 1 knowledge is assumed. The AS route using Papers 1 + 2 cannot be carried forward to A Level. Shared sections 2.4 and 3.4 cover exponential, natural logarithmic and trigonometric derivatives, composites, products and quotients, and first derivatives of parametric and implicit curves, including tangents and normals. Paper 3 additionally requires the derivative of inverse tangent; use its separate lesson for that method. Derivatives of inverse sine and inverse cosine are not required in section 3.4. Trigonometric differentiation uses radians. Keep original domains and check zero derivative denominators separately. Written practice is self-checked.

Before you begin

  • Differentiate natural and radian trigonometric composites.
  • Build point-gradient lines and distinguish a horizontal coordinate from a parameter.

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

Quick readiness check

Which ratio gives dy/dx when dx/dt is non-zero?

What you will learn

  • Find dy/dx = (dy/dt)/(dx/dt) for a parametrically defined curve where dx/dt ≠ 0.
  • Use the specified parameter value to calculate the point, tangent and normal and check any original parameter domain.
Two coordinate rates give one gradient-101-11PxyTangent: x + y = √2 · Normal: y = x
The blue unit circle has x = cos t and y = sin t. At t = π/4, P = (√2/2, √2/2); the coordinate rates are dx/dt = −√2/2 and dy/dt = √2/2. Their vertical-over-horizontal ratio gives tangent gradient −1. The red tangent is x + y = √2; the dashed normal is y = x, with gradient 1. Increasing t moves P left and up locally. The axes have equal unit lengths, so the perpendicular lines and circle retain their geometry; lines are shown only in the displayed window.

Differentiate both coordinates before taking their ratio

A parametric curve supplies x = f(t) and y = g(t). The parameter t identifies a point (f(t), g(t)); it need not equal x or represent time. Where dx/dt ≠ 0, the chain rule gives dy/dx = (dy/dt)/(dx/dt). The numerator is the vertical coordinate rate and the denominator the horizontal one. Their ratio is the curve's Cartesian gradient, not either coordinate rate alone or the inverted ratio dx/dy.

For x = cos t and y = sin t with radian parameter t, dx/dt = −sin t and dy/dt = cos t. Hence dy/dx = −cot t where sin t ≠ 0. At t = π/4 the point is (√2/2, √2/2), the rates are −√2/2 and √2/2, and the gradient is −1. Increasing t moves the point left and up locally, consistent with that negative gradient. The parametrisation traces the unit circle and may revisit the same point after a full turn.

Use the given parameter to find both the point and line

At t = π/4 on the unit circle, the tangent is y − √2/2 = −(x − √2/2), or x + y = √2. The normal has gradient 1 through the same point, so it is y = x. Check the parameter domain and dx/dt before using a finite point-gradient rule. If dx/dt = 0, inspect dy/dt separately: a non-zero vertical rate gives a vertical tangent, while two zero rates leave the ratio indeterminate and need a separate argument.

For x = t − e^(2t), y = t + e^(2t), differentiate to get dx/dt = 1 − 2e^(2t) and dy/dt = 1 + 2e^(2t). At t = 0 the point is (−1, 1), the rates are −1 and 3, and the gradient is −3. Thus the tangent is y = −3x − 2 and the normal is x − 3y + 4 = 0. Evaluate the two original coordinate rules at t = 0 for the point, rather than mistake their derivatives for coordinates.

Pause and explain

For x = cos t, y = sin t at t = π/4, why is the tangent gradient negative?

Put the idea to work

Worked example

For x = cos t, y = sin t, find the tangent and normal at t = π/4, using radians.

Show the worked solution
  1. Evaluate both coordinate rules to find P(√2/2, √2/2).
  2. Differentiate with respect to t: dx/dt = −sin t, dy/dt = cos t; at π/4 their ratio is −1.
  3. Use that gradient for tangent x + y = √2; the normal's negative reciprocal gradient is 1, giving y = x.

Answer dy/dx = −1 at P(√2/2, √2/2); tangent x + y = √2; normal y = x.

Try a topic test

Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.

Test this topic →

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 x = t² + 1 and y = t³ + t, find tangent and normal equations at t = 1.

Give me a hint

Find the point from the coordinates and the gradient from their parameter derivatives.

Compare my reasoning
  1. At t = 1 the original point is (2, 2).
  2. The coordinate rates are dx/dt = 2t and dy/dt = 3t² + 1; their ratio at 1 is 4/2 = 2.
  3. The tangent is y − 2 = 2(x − 2), or y = 2x − 2; the normal is y − 2 = −(x − 2)/2, or x + 2y = 6.

Point (2, 2); gradient 2; tangent y = 2x − 2; normal x + 2y = 6.

Look for these in your work

  • Evaluate coordinates and coordinate rates separately.
  • Build both lines through the same point with perpendicular finite gradients.
2 · Independent

For x = t + 1 and y = ln t with t > 0, find the gradient, tangent and normal at t = 1. Check the gradient by eliminating t.

Give me a hint

The horizontal coordinate rate is 1; retain the original positive log parameter.

Compare my reasoning
  1. At t = 1 the point is (2, 0), and (dy/dt)/(dx/dt) = (1/t)/1 gives gradient 1.
  2. The tangent is y = x − 2 and the normal is y = −x + 2.
  3. Eliminating t gives y = ln(x − 1) for x > 1, whose derivative 1/(x − 1) also gives 1 at x = 2.

Gradient 1 at (2, 0); tangent y = x − 2; normal y = −x + 2; Cartesian check y = ln(x − 1), x > 1.

Look for these in your work

  • Respect the log parameter domain and convert it when eliminating t.
  • Check the parameter-gradient ratio against a valid Cartesian derivative.
3 · Transfer

For x = t − e^(2t) and y = t + e^(2t), find tangent and normal equations at t = 0 and describe the signs of the coordinate rates there.

Give me a hint

Differentiate each exponential composite separately before forming the vertical-over-horizontal ratio.

Compare my reasoning
  1. At zero the original point is (−1, 1).
  2. The rates are dx/dt = 1 − 2e^(2t) = −1 and dy/dt = 1 + 2e^(2t) = 3, so dy/dx = −3.
  3. The tangent is y = −3x − 2; the normal is x − 3y + 4 = 0. The negative horizontal and positive vertical rates describe local left-and-up motion as the parameter increases.

Point (−1, 1); rates −1 and 3; gradient −3; tangent y = −3x − 2; normal x − 3y + 4 = 0.

Look for these in your work

  • Use the exponential inner factor in both coordinate derivatives.
  • Check both line equations and the rate signs at the original point.

Common mistakes

  • Differentiate both coordinates with respect to the same parameter before taking the ratio.
  • Do not invert the gradient ratio or use dy/dt as dy/dx.
  • Check dx/dt before dividing and evaluate the original coordinate rules for the point.
Recall without your notes

What can you explain now?

At a parameter value, dy/dt = 6 and dx/dt = 2. What is dy/dx?

Compare with the explanation

3.

The curve gradient is vertical rate divided by horizontal rate, 6/2.

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

Your review choice appears on Today. Sign in to sync it across devices.

Make it stick

Explain how two coordinate rates produce a Cartesian gradient. Find a parameter point, tangent and normal, and state what must be checked before dividing by the horizontal rate.

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.

StudyForA · Learn with understanding

Original teaching content and links to official sources. Check your exam year and qualification before using a resource.