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A Level · Cambridge (CIE) · 9709

Quadratics and repeated roots

Use the discriminant to interpret how a quadratic meets the horizontal axis.

Pure Mathematics 1 · Quadratic roots revision · 1.1

Pure Mathematics 1 (Paper 1) · Mathematics 9709, 2026–2027. This short revision lesson focuses on discriminants and repeated roots; it does not replace the full section 1.1 pathway. Written solutions use self-check criteria, not an official mark scheme. Specialist accuracy review has not been completed.

Before you begin

  • Expand a squared bracket and square a negative number.
  • Rearrange an equation so that one side is zero before identifying its coefficients.

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

Quick readiness check

For 3x² − 8x + 2 = 0, which substitution correctly finds the discriminant?

What you will learn

  • Identify a, b and c correctly.
  • Use the discriminant to find a parameter.
y = (x − 2)² + k-22610-10245xyA: k = -2, Δ = 8; 2 real rootsB: k = 0, Δ = 0; repeated rootC: k = 2, Δ = -8; 0 real roots
Three graphs of y = (x − 2)² + k. A has k = −2 and crosses the x-axis at 2 − √2 and 2 + √2. B has k = 0 and touches at (2, 0), a repeated root. C has k = 2 and stays above the x-axis. Their discriminants are 8, 0 and −8. Only −1 ≤ x ≤ 5 is drawn; each function is defined for all real x. Horizontal and vertical scales differ.

What the discriminant tells you

For ax²+bx+c=0 with a≠0, the quantity b²−4ac appears under the square root in the quadratic formula. A positive value produces two distinct real roots, zero produces one repeated real root, and a negative value produces no real roots.

A repeated root corresponds to the graph touching the x-axis at its vertex. This provides a geometric interpretation of setting the discriminant equal to zero.

Parameters are coefficients too

If one coefficient contains an unknown parameter, substitute it into b²−4ac just as you would a number. Solve the resulting equation or inequality and then check it against the requested root condition.

Discriminant Δ = b²−4ac

Sources for this lesson

Pause and explain

The diagram shows A: y = (x − 2)² − 2, B: y = (x − 2)² and C: y = (x − 2)² + 2. Which graph has a repeated real root, and why?

Put the idea to work

Worked example

Find k so that x²−10x+k=0 has equal roots.

Show the worked solution
  1. Identify a=1, b=−10, c=k.
  2. Set (−10)²−4k=0.
  3. Solve 100−4k=0; the resulting quadratic is (x−5)².

Answer k=25

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 = 2x² − 12x + k, find k so that the graph touches the x-axis. Give the touching point and verify your result by factorising.

Give me a hint

Touching means a repeated root. Use Δ = 0, then find x = −b/(2a).

Compare my reasoning
  1. Identify a = 2, b = −12 and c = k. The discriminant is 144 − 8k.
  2. Set 144 − 8k = 0, giving k = 18. The repeated root is x = 12/4 = 3.
  3. With k = 18, y = 2(x − 3)². It is non-negative and equals zero only at x = 3, so the touching point is (3, 0).

k = 18; touching point (3, 0); y = 2(x − 3)².

Look for these in your work

  • Show the coefficient substitution and the condition Δ = 0.
  • Find the root as well as the parameter.
  • Expand the factorisation to check all three coefficients.
2 · Independent

Find m so that 3x² + 12x + m = 0 has equal roots. State the repeated root and verify the factorisation.

Give me a hint

Keep the distinction between a coefficient m and a root x.

Compare my reasoning
  1. The discriminant is 12² − 4 × 3 × m = 144 − 12m.
  2. Equal roots require 144 − 12m = 0, so m = 12. Then x = −12/6 = −2.
  3. Substitution gives 3x² + 12x + 12 = 3(x + 2)². Expanding verifies the original coefficients.

m = 12; repeated root x = −2; 3(x + 2)² = 0.

Look for these in your work

  • State why the discriminant must be zero.
  • Keep the minus sign in −b/(2a).
  • Check your parameter and root in the original equation.
3 · Transfer

Consider (p − 1)x² + 4x + 4 = 0. Find p for a repeated quadratic root. Then explain why p = 1 also gives exactly one real solution but not a repeated quadratic root.

Give me a hint

Check that the coefficient of x² is non-zero before using a quadratic-root argument.

Compare my reasoning
  1. For a quadratic, p ≠ 1. Its discriminant is 16 − 16(p − 1) = 32 − 16p.
  2. Set Δ = 0 to obtain p = 2, which satisfies p ≠ 1. The equation becomes (x + 2)² = 0, with repeated root x = −2.
  3. At p = 1, the x² term disappears. The linear equation 4x + 4 = 0 has the single solution x = −1; it is not a quadratic and has no repeated quadratic root.

p = 2 gives a repeated quadratic root x = −2. At p = 1 the equation is linear, with x = −1.

Look for these in your work

  • Separate the quadratic case from the zero-leading-coefficient case.
  • Verify the repeated-root case by factorisation.
  • Explain why one real solution alone is insufficient evidence of a repeated quadratic root.

Common mistakes

  • Square the whole negative coefficient.
  • Equal roots require Δ=0, not c=0.
Recall without your notes

What can you explain now?

How many distinct real roots does 2x²+3x+2=0 have?

Compare with the explanation

None

Its discriminant is 9−16=−7, which is negative.

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

Without notes, sketch a quadratic with two distinct roots, a repeated root and no real roots. Label the sign of Δ in each case. Then explain why a ≠ 0 must be checked when the leading coefficient contains a parameter.

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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