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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 / 2.3.5 Sinusoidal Models


2.3.5 Sinusoidal Models


Sinusoidal Models

What are the parameters of a sinusoidal model?

  • A sinusoidal model is of the form
    • space f open parentheses x close parentheses equals a sin left parenthesis b x right parenthesis plus d
    • space f open parentheses x close parentheses equals a cos open parentheses b x close parentheses plus d
  • The a represents the amplitude of the function
    • The bigger the value of a the bigger the range of values of the function
  • The b determines the period of the function
    • The bigger the value of b the quicker the function repeats a cycle
  • The d represents the principal axis
    • This is the line that the function fluctuates around

What can be modelled as a sinusoidal model?

  • Anything that oscillates (fluctuates periodically)
  • Examples include:
    • D(t) is the depth of water at a shore t hours after midnight
    • T(d) is the temperature of a city d days after the 1st January
    • H(t) is vertical height above ground of a person t second after entering a Ferris wheel

What are possible limitations of a sinusoidal model?

  • The amplitude is the same for each cycle
    • In real-life this might not be the case
    • The function might get closer to the principal axis over time
  • The period is the same for each cycle
    • In real-life this might not be the case
    • The time to complete a cycle might change over time

Exam Tip

  • Read and re-read the question carefully, try to get involved in the context of the question!
  • Sketch a graph of the function being used as the model, use your GDC to help you and focus on the given domain

Worked Example

The water depth, D, in metres, at a port can be modelled by the function

 space D open parentheses t close parentheses equals 3 sin open parentheses 15 degree cross times t close parentheses plus 12 comma blank 0 less or equal than t less than 24

where t is the elapsed time, in hours, since midnight.

a)
Write down the depth of the water at midnight.

2-3-5-ib-ai-sl-sinusoidal-models-a-we-solution

b)
Find the minimum water depth and the number of hours after midnight that this depth occurs.

2-3-5-ib-ai-sl-sinusoidal-models-b-we-solution

c)
Calculate how long the water depth is at least 13.5 metres each day.

2-3-5-ib-ai-sl-sinusoidal-models-c-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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