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A Level · Cambridge (CIE) · 9709

Differentiate inverse tangent with its inner derivative

Use the principal inverse-tangent derivative, apply the chain rule and find tangent or normal lines using radian outputs.

Pure Mathematics 3 · Differentiation · 3.4

Pure Mathematics 3 (Paper 3, A Level); Mathematics 9709, 2026–2027, section 3.4. Paper 1 knowledge and the shared differentiation lessons are assumed. Inverse tangent is an additional Paper 3 derivative. Derivatives of inverse sine and inverse cosine are not required in section 3.4. Inverse trigonometric outputs here are radians. Written practice is self-checked

Before you begin

  • Interpret a principal inverse trigonometric value and use 1 + tan² y = sec² y.
  • Apply the chain rule, point–gradient line equations and perpendicular gradients.

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

Quick readiness check

What is arctan 1 on the principal radian branch?

What you will learn

  • Use d(arctan x)/dx = 1/(1 + x²) and d(arctan u)/dx = u′/(1 + u²).
  • Evaluate a derivative, form a tangent and normal, and distinguish inverse tangent from reciprocal tangent.
Inverse tangent and the chain ruleBlue: y = arctan(2x − 1)-0.50122.5-101yxRed: tangent gradient 1Grey dashed: normal gradient −1Black point: P = (1, π/4)
The blue curve is y = arctan(2x − 1), with radian outputs. At P = (1, π/4), the derivative is 2/[1 + (2x − 1)²] = 1. The solid red tangent has equation y − π/4 = x − 1. The dashed grey normal has equation y − π/4 = −(x − 1) and gradient −1. Both lines pass through the exact point P. The curve is defined for every real input; it continues beyond the window and approaches the unattained horizontal limits ±π/2. The displayed horizontal and vertical scales differ, so the mathematical perpendicularity comes from the gradients, not the angle drawn on screen.

Inverse tangent names an angle, not a reciprocal

The function arctan x, also written tan⁻¹ x in inverse-function notation, returns the unique angle y in −π/2 < y < π/2 for which tan y = x. It accepts every real input x and outputs a radian angle. It is different from cot x = 1/tan x where that reciprocal representation is defined. An inverse operation undoes tangent on its principal branch; it does not turn the tangent's value upside down.

To obtain the derivative, let y = arctan x so tan y = x. Implicit differentiation gives sec² y · dy/dx = 1. Since sec² y = 1 + tan² y = 1 + x², dy/dx = 1/(1 + x²). The denominator is strictly positive for every real x; the principal inverse branch has no finite-input pole. The derivative is 1 at 0 and becomes smaller as |x| grows, consistent with the curve flattening towards its unattained horizontal limits.

Differentiate the inner input and square its whole value

For y = arctan u(x), the chain rule gives y′ = u′/(1 + u²) wherever the inner function is differentiable. For y = arctan(2x − 1), this becomes 2/[1 + (2x − 1)²]. At x = 1 the curve has y = arctan 1 = π/4 and gradient 2/(1 + 1) = 1. The tangent is y − π/4 = x − 1, and the normal is y − π/4 = −(x − 1). Square the complete inner expression; (2x − 1)² is not 4x² − 1.

For y = arctan(x²), the derivative is 2x/(1 + x⁴), not 1/(1 + x²). Its sign follows the inner derivative 2x. At x = 0 the gradient is zero, so the tangent is horizontal and the normal is the vertical line x = 0; a formula using −1/0 would not be a finite normal gradient. If inverse-tangent outputs are instead measured in degrees, their derivative carries the conversion factor 180/π. Use radian outputs for the standard calculus formula and convert a stated angular rate only after calculating it.

Pause and explain

Which derivative is correct for arctan(2x − 1)?

Put the idea to work

Worked example

For y = arctan(2x − 1), find the tangent and normal at x = 1, with y measured in radians.

Show the worked solution
  1. The point is P = (1, π/4), since the inner value is 1.
  2. Apply the chain rule: y′ = 2/[1 + (2x − 1)²]; at 1 the tangent gradient is 1.
  3. Use the point–gradient form with gradients 1 and −1 for the tangent and normal.

Answer Tangent y − π/4 = x − 1; normal y − π/4 = −(x − 1).

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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 y = arctan(3x), find y′ and the tangent and normal at x = 0, using radian outputs.

Give me a hint

Use the inner derivative 3 and square the inner value 3x before evaluating the gradient.

Compare my reasoning
  1. The derivative is y′ = 3/(1 + 9x²).
  2. At 0 the point is (0, 0) and the tangent gradient is 3.
  3. The tangent is y = 3x; the perpendicular normal has gradient −1/3, so y = −x/3.

y′ = 3/(1 + 9x²); tangent y = 3x; normal y = −x/3.

Look for these in your work

  • Apply the chain rule and evaluate at the actual point.
  • Use perpendicular gradients and verify both lines pass through the point.
2 · Independent

For y = arctan(x²), find y′, the tangent and normal at x = −1, and the tangent and normal at x = 0.

Give me a hint

The inner derivative changes sign, and its square produces x⁴ in the denominator.

Compare my reasoning
  1. Differentiate to get 2x/(1 + x⁴). At −1 the point is (−1, π/4) and the gradient is −1.
  2. At −1 the tangent is y − π/4 = −(x + 1), and the normal is y − π/4 = x + 1.
  3. At 0 the point is (0, 0) and the gradient is 0, giving horizontal tangent y = 0 and vertical normal x = 0.

y′ = 2x/(1 + x⁴); at −1: tangent y − π/4 = −(x + 1), normal y − π/4 = x + 1; at 0: tangent y = 0, normal x = 0.

Look for these in your work

  • Retain the signed inner derivative and the fourth power.
  • Handle the zero-gradient normal as a vertical line.
3 · Transfer

A supplied angular model is θ(t) = arctan(t/2) radians, with t measured numerically in seconds and t ≥ 0. Find its instantaneous angular rate at t = 2 in radians per second and degrees per second.

Give me a hint

Differentiate in radians first, then multiply the resulting angular rate by 180/π to convert it.

Compare my reasoning
  1. The inner derivative is 1/2, so θ′(t) = (1/2)/[1 + (t/2)²] = 2/(t² + 4).
  2. At t = 2 the rate is 2/8 = 1/4 rad/s; the angle itself is π/4 radians, a different quantity.
  3. The converted rate is (1/4)(180/π) = 45/π degrees per second, approximately 14.3°/s. Both rates describe the same supplied model at the same time.

θ′(t) = 2/(t² + 4); at 2 s: 1/4 rad/s = 45/π degrees per second ≈ 14.3°/s.

Look for these in your work

  • Include the input scale and distinguish angle from angular rate.
  • Convert output units with the explicit factor 180/π.

Common mistakes

  • tan⁻¹ x as an inverse function is not cot x.
  • Include the inner derivative in the numerator and the square of the whole inner input in the denominator.
  • A horizontal tangent has a vertical normal; convert angular output units explicitly.
Recall without your notes

What can you explain now?

Find the derivative of arctan(3x).

Compare with the explanation

3/(1 + 9x²).

The inner derivative is 3 and the squared inner input is (3x)² = 9x².

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

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Make it stick

Derive the inverse-tangent formula from tan y = x, extend it to an inner function and use it to find a tangent, normal or angular rate with stated units.

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.

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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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