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A Level · Cambridge (CIE) · 9709

Use the discriminant to classify roots and parameter values

Connect the square-root term in the quadratic formula with real intersections, including repeated roots.

Section 1.1 · Quadratics

Pure Mathematics 1 · Quadratics · 1.1

Pure Mathematics 1 (Paper 1) · AS & A Level Mathematics 9709, 2026–2027. Paper 1 is part of every route. Written practice is self-checked; it does not award official examination marks.

Before you begin

  • Identify signed coefficients in ax² + bx + c = 0.
  • Solve a linear inequality, reversing its sign when dividing by a negative number.

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

Quick readiness check

What are a, b and c for 2x² − 3x + 5 = 0?

What you will learn

  • Use b² − 4ac to count distinct real roots for a ≠ 0.
  • Translate a root or intersection condition into a condition on a parameter.

Count roots before calculating them

For real a, b and c with a ≠ 0, the quadratic formula is x = (−b ± √(b² − 4ac))/(2a). Its discriminant D = b² − 4ac determines the real possibilities. D > 0 gives two distinct real roots; D = 0 gives one repeated real root x = −b/(2a); D < 0 gives no real roots. 'No real roots' does not mean the polynomial is undefined, nor does it mean there are no complex roots.

Graphically, a quadratic y = ax² + bx + c crosses the x-axis twice, touches it at the vertex, or misses it. A repeated root is counted twice algebraically but corresponds to one x-coordinate. The rule assumes a non-zero quadratic coefficient: if a depends on a parameter, check values making a = 0 separately because the equation may then be linear.

D = b² − 4ac

Apply the rule to the equation you actually solved

For x² − 6x + k = 0, D = 36 − 4k. Requiring two distinct real roots means 36 − 4k > 0, hence k < 9. Equality gives k = 9 and repeated root x = 3; k > 9 gives no real roots. The phrase 'real roots' without 'distinct' includes the repeated case and uses D ≥ 0.

For a line meeting a quadratic curve, equate the two y-expressions and rearrange to a quadratic equal to zero. Use the discriminant of this new equation, not the curve's original x-intercept equation. 'Touches' corresponds to D = 0 for the resulting quadratic; 'two intersections' corresponds to D > 0.

Pause and explain

Which condition gives two distinct real roots of a genuine quadratic?

Put the idea to work

Worked example

For x² − 6x + k = 0, find when there are two distinct real roots, a repeated real root, or no real roots.

Show the worked solution
  1. Here a = 1, b = −6, c = k, so D = (−6)² − 4k = 36 − 4k.
  2. Solve D > 0 to get k < 9, and D = 0 to get k = 9.
  3. For k > 9, D < 0; at k = 9 the equation is (x − 3)² = 0.

Answer Two distinct real roots: k < 9. Repeated root x = 3: k = 9. No real roots: k > 9.

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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

Classify the real roots of 3x² − 4x + 2 = 0 without solving it.

Give me a hint

Use b = −4 and c = +2 in b² − 4ac.

Compare my reasoning
  1. Identify a = 3, b = −4, c = 2.
  2. Calculate D = (−4)² − 4(3)(2) = 16 − 24 = −8.
  3. A negative discriminant gives no real roots; the graph does not meet the x-axis.

No real roots, because D = −8.

Look for these in your work

  • Keep the squared negative coefficient positive.
  • State the type of roots from the sign of D.
2 · Independent

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

Give me a hint

The negative constant makes the product 4ac negative.

Compare my reasoning
  1. Identify a = 2, b = 3, c = −2.
  2. Calculate D = 9 − 4(2)(−2) = 25.
  3. Since 25 > 0, the equation has two distinct real roots.

Two distinct real roots; D = 25.

Look for these in your work

  • Subtract the negative product correctly.
  • Do not confuse a square discriminant with a repeated root.
3 · Transfer

The line y = x + k meets y = x² − 3x + 5. Find the values of k giving two intersections, one tangency or no intersections.

Give me a hint

Equate the y-values before calculating a discriminant.

Compare my reasoning
  1. Rearrange x + k = x² − 3x + 5 to x² − 4x + (5 − k) = 0.
  2. Its discriminant is 16 − 4(5 − k) = 4k − 4.
  3. Positive, zero and negative discriminants correspond respectively to k > 1, k = 1 and k < 1. At k = 1, the point is (2, 3).

Two intersections for k > 1; tangent at (2, 3) for k = 1; none for k < 1.

Look for these in your work

  • Use the intersection equation's coefficients.
  • Translate all three discriminant cases into parameter conditions.

Common mistakes

  • Square the signed coefficient using brackets: (−6)² = 36.
  • Distinguish real roots from distinct real roots when choosing ≥ or >.
  • Check whether a parameter can make the leading coefficient zero.
Recall without your notes

What can you explain now?

For which real k does x² + 2kx + 9 = 0 have a repeated root?

Compare with the explanation

k = −3 or k = 3.

The discriminant is 4k² − 36. Setting it to zero gives k² = 9, so include both signs.

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

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Make it stick

Explain the difference between a repeated root and two distinct roots. Then say which equation's discriminant you need when a line meets a parabola.

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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