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DP IB Maths: AI SL

Revision Notes

Home / IB / Maths: AI SL / DP / Revision Notes / 2. Functions / 2.3 Modelling with Functions / 2.3.2 Quadratic & Cubic Models


2.3.2 Quadratic & Cubic Models


Quadratic Models

What are the parameters of a quadratic model?

  • A quadratic model is of the form space f left parenthesis x right parenthesis equals a x squared plus b x plus c
  • The c represents the value of the function when x = 0
    • This is the value of the function when the independent variable is not present
    • This is usually referred to as the initial value
  • The a has the biggest impact on the rate of change of the function
    • If a has a large absolute value then the rate of change varies rapidly
    • If a has a small absolute value then the rate of change varies slowly
  • The maximum (or minimum) of the function occurs when space x equals negative fraction numerator b over denominator 2 a end fraction
    • This is given in the formula booklet as the axis of symmetry

What can be modelled as a quadratic model?

  • If the graph of the data resembles a union or intersection shape
  • These can be used if the graph has a single maximum or minimum
    • H(t) is the vertical height of a football t seconds after being kicked
    • A(x) is the area of rectangle of length x cm that can be made with a 20 cm length of string

What are possible limitations of a quadratic model?

  • A quadratic has either a maximum or a minimum but not both
    • This means one end is unbounded
    • In real-life this might not be the case
    • The function might have both a maximum and a minimum
    • To overcome this you can decide on an appropriate domain so that the outputs are within a range
  • Quadratic graphs are symmetrical
    • This might not be the case in real-life

Exam Tip

  • Read and re-read the question carefully, try to get involved in the context of the question!
    • Imagine what happens to a stone as you throw it from a cliff, what would the path look like?
    • What would it be like to manage a toy factory, would you expect profit to rise or fall as you increase the price of the toy?
  • Sketch a graph of the function being used as the model, use your GDC to help you
  • If you are completely stuck try “doing something” with the quadratic function – sketch it, factorise it, solve it

Worked Example

A company sells unicorn toys. The profit, £ P, of the selling one unicorn toy can be modelled by the function

space P open parentheses x close parentheses equals 1 over 10 left parenthesis negative x squared plus 20 x minus 50 right parenthesis

where x is the selling price of the toy.

Find the selling price which maximises profit. State the maximum profit.

2-3-2-ib-ai-sl-quadratic-models-we-solution

Cubic Models

What are the parameters of a cubic model?

  • A cubic model is of the form space f open parentheses x close parentheses equals a x cubed plus b x squared plus c x plus d
  • The d represents the value of the function when x = 0
    • This is the value of the function when the independent variable is not present
    • This is usually referred to as the initial value
  • The a has the biggest impact on the rate of change of the function
    • If a has a large absolute value then the rate of change varies rapidly
    • If a has a small absolute value then the rate of change varies slowly

What can be modelled as a cubic model?

  • If the graph of the data has exactly one maximum and one minimum within an interval
  • If the graph is monotonic with no maximum or minimum
    • D(t) is the vertical distance below starting point of a bungee jumper t seconds after jumping
    • V(x) is the volume of a cuboid of length x cm that can be made with a 200 cm2 of cardboard

What are possible limitations of a cubic model?

  • Cubic graphs have no global maximum or minimum
    • This means the function is unbounded
    • In real-life this might not be the case
    • The function might have a maximum or minimum
    • To overcome this you can decide on an appropriate domain so that the outputs are within a range

Exam Tip

  • Read and re-read the question carefully, try to get involved in the context of the question!
  • Always sketch the graph using your GDC to help 
  • Pay particular attention to the domain of the question
    • If the domain is given, make sure that you focus only on that section when you sketch the graph
    • If the domain is not given, think about whether or not it needs to be restricted based on the context of the question, e.g. can time be negative?

Worked Example

The vertical height of a child above the ground, h metres, as they go down a water slide can be modelled by the function

space h open parentheses t close parentheses equals 4 over 7 open parentheses 35 minus 12 t plus 6 t squared minus t cubed close parentheses,

where t is the time in seconds after the child enters the slide.

a)
State the vertical height of the slide.

2-3-2-ib-ai-sl-cubic-models-a-we-solution

b)
Given that the child reaches the ground at the bottom of the slide, find the domain of the function.

2-3-2-ib-ai-sl-cubic-models-b-we-solution



  • 1. Number & Algebra
    • 1.1 Number Toolkit
      • 1.1.1 Standard Form
        • 1.1.2 Exponents & Logarithms
          • 1.1.3 Approximation & Estimation
            • 1.1.4 GDC: Solving Equations
            • 1.2 Sequences & Series
              • 1.2.1 Language of Sequences & Series
                • 1.2.2 Arithmetic Sequences & Series
                  • 1.2.3 Geometric Sequences & Series
                    • 1.2.4 Applications of Sequences & Series
                    • 1.3 Financial Applications
                      • 1.3.1 Compound Interest & Depreciation
                        • 1.3.2 Amortisation & Annuities
                      • 2. Functions
                        • 2.1 Linear Functions & Graphs
                          • 2.1.1 Equations of a Straight Line
                          • 2.2 Further Functions & Graphs
                            • 2.2.1 Functions
                              • 2.2.2 Graphing Functions
                                • 2.2.3 Properties of Graphs
                                • 2.3 Modelling with Functions
                                  • 2.3.1 Linear & Piecewise Models
                                    • 2.3.2 Quadratic & Cubic Models
                                      • 2.3.3 Exponential Models
                                        • 2.3.4 Direct & Inverse Variation
                                          • 2.3.5 Sinusoidal Models
                                            • 2.3.6 Strategy for Modelling Functions
                                          • 3. Geometry & Trigonometry
                                            • 3.1 Geometry Toolkit
                                              • 3.1.1 Coordinate Geometry
                                                • 3.1.2 Arcs & Sectors
                                                • 3.2 Geometry of 3D Shapes
                                                  • 3.2.1 3D Coordinate Geometry
                                                    • 3.2.2 Volume & Surface Area
                                                    • 3.3 Trigonometry
                                                      • 3.3.1 Pythagoras & Right-Angled Triganometry
                                                        • 3.3.2 Non Right-Angled Trigonometry
                                                          • 3.3.3 Applications of Trigonometry & Pythagoras
                                                          • 3.4 Voronoi Diagrams
                                                            • 3.4.1 Voronoi Diagrams
                                                              • 3.4.2 Toxic Waste Dump Problem
                                                            • 4. Statistics & Probability
                                                              • 4.1 Statistics Toolkit
                                                                • 4.1.1 Sampling & Data Collection
                                                                  • 4.1.2 Statistical Measures
                                                                    • 4.1.3 Frequency Tables
                                                                      • 4.1.4 Linear Transformations of Data
                                                                        • 4.1.5 Outliers
                                                                          • 4.1.6 Univariate Data
                                                                            • 4.1.7 Interpreting Data
                                                                            • 4.2 Correlation & Regression
                                                                              • 4.2.1 Bivariate data
                                                                                • 4.2.2 Correlation Coefficients
                                                                                  • 4.2.3 Linear Regression
                                                                                  • 4.3 Probability
                                                                                    • 4.3.1 Probability & Types of Events
                                                                                      • 4.3.2 Conditional Probability
                                                                                        • 4.3.3 Sample Space Diagrams
                                                                                        • 4.4 Probability Distributions
                                                                                          • 4.4.1 Discrete Probability Distributions
                                                                                            • 4.4.2 Expected Values
                                                                                            • 4.5 Binomial Distribution
                                                                                              • 4.5.1 The Binomial Distribution
                                                                                                • 4.5.2 Calculating Binomial Probabilities
                                                                                                • 4.6 Normal Distribution
                                                                                                  • 4.6.1 The Normal Distribution
                                                                                                    • 4.6.2 Calculations with Normal Distribution
                                                                                                    • 4.7 Hypothesis Testing
                                                                                                      • 4.7.1 Hypothesis Testing
                                                                                                        • 4.7.2 Chi-squared Test for Independence
                                                                                                          • 4.7.3 Goodness of Fit Test
                                                                                                            • 4.7.4 The t-test
                                                                                                          • 5. Calculus
                                                                                                            • 5.1 Differentiation
                                                                                                              • 5.1.1 Introduction to Differentiation
                                                                                                                • 5.1.2 Applications of Differentiation
                                                                                                                  • 5.1.3 Modelling with Differentiation
                                                                                                                  • 5.2 Integration
                                                                                                                    • 5.2.1 Trapezoid Rule: Numerical Integration
                                                                                                                      • 5.2.2 Introduction to Integration
                                                                                                                        • 5.2.3 Applications of Integration
                                                                                                                      Daniel Finlay

                                                                                                                      Author: Daniel

                                                                                                                      Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.


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