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NotesMath AI HLTopic 2.5Quadratic models
Back to Math AI HL Topics
2.5.25 min read

Quadratic models (Math AI HL)

IB Mathematics: Applications and Interpretation • Unit 2

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Contents

  • What makes a situation quadratic?
  • Finding the vertex (maximum or minimum)
  • Common traps with quadratic models
  • Using the model to answer context questions
A single maximum or minimum → think quadratic: A quadratic model fits when the quantity rises to a peak then falls (or falls to a trough then rises).

The graph is a parabola.

The key feature is one turning point — either a maximum or a minimum.

Signal words in IB questions that suggest a quadratic model:

  • "Maximum height", "highest point", "peak" — parabola opens downward (a < 0)
  • "Minimum cost", "lowest point", "bottom of a dip" — parabola opens upward (a > 0)
  • Object in projectile motion: always quadratic in time
  • Area optimisation problems: usually quadratic
  • Revenue = price × quantity where price depends on quantity: usually quadratic
ScenarioQuadratic?Clue
Dolphin jump: h = −0.5d² + 3dYes (max)Negative leading coefficient → has a maximum height
Carpet area: A = x(20 − πx/2)Yes (max)Area optimisation → quadratic in x
Population growing 15% per yearNo — exponentialPercentage growth → exponential
Ball rolling at constant speedNo — linearConstant speed → linear distance-time

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Parabola opens upward — vertex is a MINIMUM
Parabola opens downward — vertex is a MAXIMUM
vertex x-coord
x = −b/(2a)
vertex y-coord
Substitute x = −b/(2a) back into f(x)

A dolphin jumps and its height is h(d) = −0.5d² + 3d, where d is horizontal distance in metres.

Find the maximum height and the value of d at which it occurs.

Step by step

  1. Identify a = −0.5, b = 3. Since a < 0, the vertex is a maximum.
  2. Find the d-coordinate of the vertex.
  3. Find the maximum height.

Final answer

Maximum height = 4.5 m, occurring at a horizontal distance of 3 m from take-off.

Finding the vertex — maximum case on f(x) = −x² + 2x + 8 (a < 0).

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GDC method is equally valid: Enter f(x) in Y1, set a suitable window (e.g. Xmin=0, Xmax=7, Ymin=−1, Ymax=6), then use Calc → Maximum.

The GDC confirms (3, 4.5).

Both methods earn full marks.

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Trap 1 — confusing x at vertex with the vertex y-value: If asked "find the maximum height", give the y-coordinate of the vertex, not the x-coordinate.

These are different numbers.

State both clearly: "Maximum height = 4.5 m occurs at d = 3 m."
Trap 2 — forgetting the domain: The dolphin re-enters the water where h(d) = 0.

Solve −0.5d² + 3d = 0 → d(−0.5d + 3) = 0 → d = 0 or d = 6.

So the model is only valid for 0 ≤ d ≤ 6.

Answers outside this range are meaningless.

For h(d) = −0.5d² + 3d, find where the dolphin re-enters the water.

Step by step

  1. Set h(d) = 0.
  2. Factorise.
  3. Solve.

Final answer

The dolphin re-enters the water at d = 6 m. (d = 0 is the starting point.)

Trap 3 — sign of "a": Always check: if the real-world situation has a maximum (peak), then a must be negative.

If you get a positive a when modelling a maximum situation, recheck your equation.
The model is a tool — use it to answer the actual question: Once you have the quadratic model, typical IB follow-up questions ask: find the maximum/minimum, find zeros (where object lands, when profit = 0), or find f(x) for a given x.

A company's profit P (thousands of $) when selling x hundred units is P(x) = −2x² + 12x − 10.

(a) Find the maximum profit.

(b) Find the number of units for which there is no profit.

Step by step

  1. Part (a): find the vertex. a = −2, b = 12.
  2. Part (b): set P = 0 and solve.
  3. Divide by −2.
  4. Factorise.

Final answer

(a) Maximum profit = $8 000 when selling 300 units. (b) Zero profit when selling 100 units or 500 units.

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The braking distance d metres of a car travelling at a speed of s km/h is modelled by d(s) = 0.0065s² + 0.15s. Use the model to estimate the braking distance of a car travelling at 80 km/h. [2 marks]

Related Math AI HL Topics

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2.1.1Gradient and y-intercept
2.1.2Writing the equation of a straight line
2.1.3Parallel and perpendicular lines
2.1.4Linear models in context
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